Tag: Quantum information

  • Entanglement of nanophotonic quantum memory nodes in a telecom network

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    Distributing quantum entanglement between quantum memory nodes separated by extended distances1,4 is an important element for the realization of quantum networks, enabling potential applications ranging from quantum repeaters2,5 and long-distance secure communication6,7 to distributed quantum computing8,9 and distributed quantum sensing and metrology10,11. Proposed architectures require quantum nodes containing multiple long-lived qubits that can collect, store and process information communicated by photonic channels based on telecommunication (telecom) fibres or satellite-based links. In particular, the abilities to herald on successful photon arrival events and to detect quantum-gate errors are central to scalable implementations. As photons and individual matter qubits interact weakly in free space12, a promising approach to enhance the interaction between light and communication qubits is to use nanophotonic cavity quantum electrodynamic (QED) systems, in which tight light confinement inside the nanostructure enables strong interactions between the photon and the communication qubit13,14,15,16. Furthermore, nanophotonic systems offer a path towards large-scale manufacturing and on-chip electric and optical control integration17,18,19. Several experiments demonstrated remote entanglement in systems ranging from neutral atoms20,21,22,23 and trapped ions24,25 to semiconductor quantum dots26 and nitrogen-vacancy centres in diamond27,28. Recently, two atomic ensemble memories have been entangled through a metropolitan fibre network29,30,31. However, real-world applications require a combination of efficient photon coupling, long-lived heralded memory and multi-qubit operations with practical telecom fibre networks, which is an outstanding challenge.

    Here we report the realization of a two-node quantum network between two multi-qubit quantum network nodes constituted by silicon-vacancy (SiV) centres in diamond coupled to nanophotonic cavities and integrated with a telecom fibre network. SiVs coupled to cavities have emerged as a promising quantum network platform, having demonstrated memory-enhanced quantum communication32 and robust multi-qubit single-node operation33. We extend these single-node experiments by demonstrating remote entanglement generation between two electron spins in two spatially separated SiV centres with a success rate of up to 1 Hz. Our approach uses serial, heralded spin-photon gate operations with time-bin qubits for robust entanglement of separated nodes and does not require phase stability across the link. We further make use of the multi-qubit capabilities to entangle two long-lived nuclear spins, using integrated error detection to enhance entanglement fidelities and dynamical decoupling sequences to extend the entanglement duration to 1 s. Both entanglement generation techniques rely on the strong light–matter interaction enabled by the coupling of SiV to the nanophotonic cavity. To demonstrate the feasibility of deployed quantum networks using our platform, we use bidirectional quantum frequency conversion (QFC) to convert the wavelength of the photonic qubits to telecom wavelengths. Building on recently demonstrated compatibility of our platform with bidirectional QFC34,35, we demonstrate remote entanglement generation through spools of up to 40 km of low-loss telecom fibre. Finally, we combine these techniques to demonstrate entanglement generation through a 35-km-long loop of fibre with 17 dB loss deployed in the Boston area urban environment.

    Two-node quantum network using integrated nanophotonics

    Our quantum network nodes consist of SiV centres in diamond that reside in individually operated dilution refrigerator setups in separate laboratories (Fig. 1a). By selectively implanting the 29Si isotope into the diamond substrate, each SiV deterministically contains two addressable spin qubits: one electron spin used as a communication qubit, which couples strongly to itinerant photons, and one long-lived 29Si nuclear spin, used as a memory qubit to store entanglement. Under an externally applied magnetic field, Zeeman sublevels define the electronic spin qubit states (|↓e, |↑e) and the nuclear spin qubit states (|↓n, |↑n) (refs. 36,37) (Fig. 1b, left). Microwave pulses are used to drive the electronic spin-flipping transitions, whereas radio-frequency pulses drive the nuclear spin-flipping transitions33. The SiV centres are embedded into nanophotonic diamond cavities, which enhance interactions between light and the electron spin12,38. The strong emitter–cavity coupling as characterized by the single-photon cooperativity in node A of 12.4 and node B of 1.5 (Supplementary Information) results in an electron-spin-dependent cavity reflectance14 (Fig. 1b, right). This can be used to construct a reflection-based spin-photon gate (e–γ gate), which contains a sequence of rapid microwave gates generating entanglement between the electron spin of the SiV and the photonic qubits14. Moreover, taking advantage of the strong coupling between the electron spin of SiV and the 29Si nuclear spin, nucleus–photon entanglement can be created using the photon–nucleus entangling (PHONE) gate as demonstrated recently33. The two nodes are connected either directly by an optical fibre of length a ≈ 20 m (Fig. 1a) or by a considerably longer telecom fibre link as discussed below (Fig. 4a).

    Fig. 1: A two-node quantum network of cavity-coupled solid-state emitters.
    figure 1

    a, Experimental setup. Each SiV is localized in a nanophotonic cavity within an individually operated cryostat held at temperatures below 200 mK in two separate laboratories. The line-of-sight distance between the two SiVs is 6 m. A gold coplanar waveguide is used to deliver microwave and radio-frequency pulses to the SiV. Both quantum network nodes are connected by an optical fibre of length a ≈ 20 m and frequency-shifting setup to compensate for differences in the optical transition frequencies, or a long telecom fibre link using QFC (Fig. 4a). The measurement of the photonic time-bin qubit is performed at node B using a time-delay interferometer (TDI), which measures the time-bin qubit in the basis |± (|e ± |l). b, Left, energy levels of 29SiV showing the microwave and radio-frequency transitions in the two-qubit manifold (blue and turquoise arrows) and the spin-conserving optical transitions (red and orange). Right, the reflection spectrum of cavity QED system of node A shows the electron-spin-dependent cavity reflectance. The dashed line indicates the frequency of maximum reflectance contrast, which is used as the frequency for the electron spin state readout and the photonic entanglement. Norm., normalized.

    We use a serial network configuration to generate remote entanglement between the electron spins in node A and node B, mediated by a time-bin photonic qubit (Fig. 2a). We first use a e–γ gate to generate an entangled Bell state between electron spin \(\left|{\downarrow }_{{\rm{e}}}^{{\rm{A}}}\right\rangle \), \(\left|{\uparrow }_{{\rm{e}}}^{{\rm{A}}}\right\rangle \) of node A and an incoming time-bin photonic qubit \(\left|e\right\rangle \), \(\left|l\right\rangle \) (ref. 14). Here, \(\left|e\right\rangle \) and \(\left|l\right\rangle \) describe the presence of a photon in the early and late time bins of the photonic qubit, which are separated by δt = 142 ns, respectively. The resulting photon–electron Bell state can be described as \(| {\rm{Photon}},{\rm{SiV}}\,{\rm{A}}\rangle =(| e{\downarrow }_{{\rm{e}}}^{{\rm{A}}}\rangle +| l{\uparrow }_{{\rm{e}}}^{{\rm{A}}}\rangle )/\sqrt{2}\) (Methods). After that, the photonic qubit travels by optical fibre to node B, in which a second e–γ gate entangles the photonic qubit with the electron spin in node B. In the ideal, lossless case, the resulting state is a three-particle Greenberger–Horne–Zeilinger (GHZ) state:

    $$\begin{array}{l}| {\rm{Photon}},\,{\rm{SiV}}\,{\rm{A}},\,{\rm{SiV}}\,{\rm{B}}\rangle \,=\,(| e{\downarrow }_{{\rm{e}}}^{{\rm{A}}}{\downarrow }_{{\rm{e}}}^{{\rm{B}}}\rangle +| l{\uparrow }_{{\rm{e}}}^{{\rm{A}}}{\uparrow }_{{\rm{e}}}^{{\rm{B}}}\rangle )/\sqrt{2}\\ \,\,\,\,\,\,\,\,\,\,\,=\,(| +\rangle | {\varPhi }_{{\rm{ee}}}^{+}\rangle +| -\rangle | {\varPhi }_{{\rm{ee}}}^{-}\rangle )/\sqrt{2}.\end{array}$$

    Fig. 2: Remote entanglement between two electronic spins.
    figure 2

    a, Entanglement generation sequence. A photonic qubit is entangled with the electron spin in node A using the e–γ gate. A second e–γ gate entangles the photonic qubit with node B, generating a GHZ state among the two electronic qubits and the photonic qubit. A measurement of the photonic qubit in the |± basis heralds the generation of an electronic Bell state \(\left|{\varPhi }_{{\rm{ee}}}^{\pm }\right\rangle \). b, Measurement results of Bell-state measurement. Measured correlations in the ZZ, XX and YY bases of the electronic spin corresponding to a Bell-state fidelity of \({{\mathcal{F}}}_{| {\varPhi }_{{\rm{ee}}}^{-}\rangle }=0.86(3)\) (blue) and \({{\mathcal{F}}}_{| {\varPhi }_{{\rm{ee}}}^{+}\rangle }=0.74(3)\) (red). Dashed bars show correlations predicted by a theoretical model using independently measured performance parameters of our system. c, Sweep of mean photon number of the photonic qubit showing that the success rates can be increased by sending photonic qubits with a higher mean photon number. The average fidelity of the generated \(\left|{\varPhi }_{{\rm{ee}}}^{+}\right\rangle \) and \(\left|{\varPhi }_{{\rm{ee}}}^{-}\right\rangle \) states is plotted. Inset, fidelities of states shown in b. Entanglement is shown to persist above the classical limit (dashed line) for success rates up to 1 Hz. Filled curves show predictions by a theory model using independently measured performance parameters of our system (Supplementary Information). Error bars in b and c are 1 s.d.

    Here, |± = (|e ± |l)/√2 describes two orthogonal superposition states of the photonic time-bin qubit, and \(| {\varPhi }_{{\rm{ee}}}^{\pm }\rangle =(| {\downarrow }_{{\rm{e}}}^{{\rm{A}}}{\downarrow }_{{\rm{e}}}^{{\rm{B}}}\rangle \pm | {\uparrow }_{{\rm{e}}}^{{\rm{A}}}{\uparrow }_{{\rm{e}}}^{{\rm{B}}}\rangle )/\sqrt{2}\) describes the maximally entangled Bell states of the two spatially separated electron spins. The photonic qubit is measured in the |± basis using a TDI to herald the generation of an electronic Bell state:

    $$\left|{\rm{SiV}}\,{\rm{A}},{\rm{SiV}}\,{\rm{B}}\right\rangle =\left\{\begin{array}{ll}\left|{\varPhi }_{{\rm{ee}}}^{+}\right\rangle ,\quad & {\rm{if}}\,{\rm{TDI}}\,{\rm{measures}}\left|+\right\rangle \\ \left|{\varPhi }_{{\rm{ee}}}^{-}\right\rangle ,\quad & {\rm{if}}\,{\rm{TDI}}\,{\rm{measures}}\left|-\right\rangle .\end{array}\right.$$

    Note that similar to the previously used single-node schemes14, this method is robust to photon loss, as any losses of photons can be detected by a missing heralding event. Furthermore, the main advantage of our serial scheme is that both the early and late time bins of the photonic qubit travel through the same path, so no phase or polarization locking is necessary to guarantee high interference contrast at the TDI. This relaxes the requirements on system stability compared with one-photon schemes, which typically require an interferometric measurement of two emitted photons travelling through two stabilized paths23,26,28,31 and avoids the reduction in entanglement rate typically present in two-photon schemes27,39. Furthermore, extending the number of network nodes to more than two can be achieved either by connecting more than two nodes in series or by using a switch network between multiple nodes to generate pairwise connectivity.

    As cavity-coupled 29SiV centres possess an inhomogeneous distribution of optical transition frequencies of around ±50 GHz centred around 406.640 THz (737.2 nm), see ref. 40 and Methods, the frequency difference between the nodes needs to be coherently bridged. For node B used in this work, for instance, the optical frequency ωB of the SiV is detuned from that of node A (ωA) by Δω = 13 GHz. To address this, we prepare the photonic qubit at frequency ωA and then coherently shift its frequency by Δω after it has interacted with the SiV at node A, either using electro-optic frequency shifting or by bidirectional QFC34,35.

    Electronic spin entanglement

    To demonstrate the basic principles of network operation, we first focus on the nodes connected directly by an optical fibre of length a ≈ 20 m and use electro-optical frequency shifting (see Methods for more details). The above protocol is applied using weak coherent states (WCS, with mean photon number μ = 0.017) to encode time-bin qubits. After the TDI measurement heralds the generation of a Bell-state, single-qubit rotations and subsequent readout of the electron spin at each node implement the measurement of the correlations \(\left\langle {\sigma }_{i}^{{\rm{A}}}{\sigma }_{i}^{{\rm{B}}}\right\rangle ,i\in \{x,y,z\}\), which we abbreviate as XX, YY and ZZ, respectively. Figure 2b shows the results of the correlation measurements, from which we extract the fidelities of the resulting electron–electron state with respect to the maximally entangled Bell states \({{\mathcal{F}}}_{\left|{\varPhi }_{{\rm{ee}}}^{-}\right\rangle }=0.86(3)\) (if the TDI measured |−), and \({{\mathcal{F}}}_{\left|{\varPhi }_{{\rm{ee}}}^{+}\right\rangle }=0.74(3)\) (if the TDI measured |+), unambiguously demonstrating entanglement between the two nodes. The observed difference in fidelity is because of one source of infidelity associated with the imperfect reflection contrast of the two cavity-coupled SiVs. This results in reflection of the photonic qubit even when the electron spin is in the low-reflectivity |↓e state. For our system configuration, this type of error accumulates preferentially for the \(\left|{\varPhi }_{{\rm{ee}}}^{+}\right\rangle \) state, which is why \({{\mathcal{F}}}_{\left|{\varPhi }_{{\rm{ee}}}^{+}\right\rangle }\) is consistently lower than \({{\mathcal{F}}}_{\left|{\varPhi }_{{\rm{ee}}}^{-}\right\rangle }\) (Supplementary Information). Further error sources include contributions from 2− or higher photon number Fock states of the WCS used as time-bin photonic qubits. By varying the mean photon number μ in the WCS, we can increase the entanglement generation rate at the cost of reduced fidelity of the generated state. We explore this trade-off in Fig. 2c, in which we show that we are able to operate at success rates of 1 Hz while maintaining entanglement.

    Nuclear spin entanglement

    Extending remote entanglement to larger distances requires the ability to preserve entanglement long enough such that the heralding signal obtained at node B can be classically relayed to node A. The coherence times of the electron spins in nodes A and B are 125 μs and 134 μs, respectively. Assuming classical communication using optical fibres in the telecom band, the decoherence of the electron spins would limit the distance between the nodes to approximately 25 km. To overcome this limitation, we demonstrate remote entanglement generation between two 29Si nuclei, which are long-lived quantum memories with storage times of more than 2 s (ref. 33). Analogous to the generation of electron–electron entanglement, remote nuclear entanglement is mediated by the photonic time-bin qubit (Fig. 3a). Thus, the first step of the remote entanglement generation sequence is creating entanglement between a photonic time-bin qubit and the 29Si nuclear spin at node A. This is achieved using the recently demonstrated PHONE gate, which uses only microwave pulses to directly entangle the 29Si nuclear spin with the photonic qubit (see ref. 33 and Methods), without the need to swap quantum information from electron to nuclear spin. After applying the PHONE gate on the SiV in node A and the photonic qubit, in the ideal limit, their quantum state is

    $$\left|{\rm{Photon}},{\rm{SiV}}\,{\rm{A}}\right\rangle =\left(\left|e{\downarrow }_{{\rm{n}}}^{{\rm{A}}}\right\rangle +\left|l{\uparrow }_{{\rm{n}}}^{{\rm{A}}}\right\rangle \right)\left|{\downarrow }_{{\rm{e}}}^{{\rm{A}}}\right\rangle /\sqrt{2}.$$

    Fig. 3: Remote entanglement and long-lived storage using nuclear spins.
    figure 3

    a, Entanglement generation and subsequent dynamical decoupling using nuclear spin qubits. Nuclear–nuclear entanglement is created by sequentially entangling a time-bin photonic qubit with the 29Si nuclei at nodes A and B using two PHONE gates. Measurement of the electron spin qubits allows for integrated error detection by flagging microwave gate errors that occurred during the PHONE gate. b, Results of Bell-state measurement of \(\left|{\varPhi }_{{\rm{nn}}}^{-}\right\rangle \) after performing error detection, resulting in a Bell-state fidelity of \({{\mathcal{F}}}_{\left|{\varPhi }_{{\rm{nn}}}^{-}\right\rangle }^{{\rm{ED}}}=0.77(5)\). Dashed bars show correlations predicted by a theoretical model using independently measured performance parameters of our system. c, Decoherence protection of remotely entangled nuclear–nuclear Bell states, both with (turquoise) and without (blue) error detection. By performing XY8 dynamical decoupling sequences on the two nuclei, entanglement can be preserved for up to 1 s. Filled curves show predictions by a theory model using independently measured performance parameters of our system (Supplementary Information). The XY8-1 decoupling sequence was used for the datapoint with 10 ms decoupling time, whereas the XY8-128 sequence was used for all other measurements. The dashed line indicates the classical limit. Error bars in b and c are 1 s.d.

    This implies that unless a microwave gate error has occurred, the electron spin is disentangled from the nuclear spin and is in the \(\left|{\downarrow }_{{\rm{e}}}^{{\rm{A}}}\right\rangle \) state. Thus, the electron spin can be used as a flag qubit to perform error detection by discarding a measurement when the electron spin is measured in \(\left|{\uparrow }_{{\rm{e}}}^{{\rm{A}}}\right\rangle \). By performing a second PHONE gate between the 29Si nuclear spin of node B and the time-bin qubit and by subsequently measuring out the photonic time-bin qubit in the |± basis, the nuclear Bell states \(\left|{\varPhi }_{{\rm{nn}}}^{\pm }\right\rangle \) are created. Following the entanglement generation, we perform XY8-type decoupling sequences on both nuclei to protect the nuclear–nuclear Bell state from decoherence caused by a quasi-static environment. Figure 3b shows the probability correlations of the resulting \(\left|{\varPhi }_{{\rm{nn}}}^{-}\right\rangle \) state using a XY8-1 decoupling sequence with a total nuclear spin decoupling time of 10 ms. After using error detection by discarding measurements in which the electronic flag qubits are measured in the |↑e state, the Bell-state fidelity is \({{\mathcal{F}}}_{\left|{\varPhi }_{{\rm{nn}}}^{-}\right\rangle }^{{\rm{ED}}}=0.77(5)\), which is an improvement from the directly measured value of \({{\mathcal{F}}}_{\left|{\varPhi }_{{\rm{nn}}}^{-}\right\rangle }^{{\rm{raw}}}=0.64(5)\) without error detection. Similar to \(\left|{\varPhi }_{{\rm{ee}}}^{+}\right\rangle \), the generated \(\left|{\varPhi }_{{\rm{nn}}}^{+}\right\rangle \) state accumulates errors because of imperfect reflectance contrast (Supplementary Information). Figure 3c shows Bell-state fidelities for longer total nuclear decoupling times. By performing XY8–128 decoupling sequences, entanglement can be preserved for up to 500 ms, with the application of error detection further extending this to 1 s.

    Entanglement distribution through 35 km of deployed fibre

    Light at the resonant wavelength of the SiV (737 nm) experiences a high in-fibre loss of up to 4 dB km−1, which limits the range of remote entanglement distribution at this wavelength. To make our quantum network compatible with existing classical communication infrastructures that use low-loss optical fibres, we use bidirectional QFC to and from the telecom O-band (Fig. 4a); see the Methods. After the photonic qubit at 737 nm is reflected off the SiV of node A, a fibre-coupled PPLN waveguide pumped with 1,623 nm light converts the wavelength of the photonic qubit to 1,350 nm (ref. 34). This frequency lies in the telecom O-band and shows low attenuation (<0.3 dB km−1) in conventional telecom single-mode fibre. After downconversion, the photonic qubit is sent through telecom fibre of varying length before a second PPLN upconverts the photonic qubit back to 737 nm. This bidirectional frequency conversion allows for straightforward bridging of the frequency difference Δω of the two SiVs: the frequency of the upconversion setup of the pump laser is offset by Δω from the frequency of the downconversion pump laser. The total efficiency of the bidirectional QFC, including a final filter cavity, is 5.4%, whereas the noise counts at the superconducting nanowire single-photon detector (SNSPD) of node B are 2.5 Hz.

    Fig. 4: Nuclear spin entanglement distribution through 35 km of deployed fibre.
    figure 4

    a, Schematic of QFC setup. At node A, the photonic qubit is downconverted from 737 nm to 1,350 nm, which can propagate with low loss in telecom single-mode fibres. At the node B, it is upconverted back to 737 nm. The pump laser frequencies in the upconversion and downconversion setups are detuned by Δω = 13 GHz to compensate for the difference in optical frequencies of the two SiVs. b, Nuclear spin Bell-state fidelities for varying lengths of telecom fibre spools between the two nodes. Entanglement persists for fibre lengths up to 40 km. Bell-state decoherence can be explained by a model incorporating a decrease in signal-to-noise ratio because of dark counts at 2.7 Hz and conversion noise photons at 2.5 Hz (solid line). The dashed line shows the classical limit. c, Measurement results of Bell-state measurement of \({\left|{\varPhi }_{{\rm{nn}}}^{-}\right\rangle }^{{\rm{ED}}}\) state created through a 35-km long deployed fibre link shown in d, resulting in a fidelity of \({{\mathcal{F}}}_{\left|{\varPhi }_{{\rm{nn}}}^{-}\right\rangle }^{{\rm{ED}}}=0.69(7)\). Dashed bars show correlations predicted by a theoretical model using independently measured performance parameters of our system. d, Route of the deployed fibre link connecting nodes A and B. It consists of 35 km deployed telecom fibre routed towards and back from an off-site location, crossing four municipalities in the greater Boston metropolitan region. Error bars in b and c are 1 s.d. Scale bar, 1,000 m (d).

    Using this frequency conversion scheme together with the entanglement method described above (Fig. 3a), we remotely entangle two 29Si nuclei through spools of low-loss telecom fibre up to 40 km in length (Fig. 4b). For future repeater node applications of truly space-like separated quantum network nodes, it is important that entanglement persists until all nodes have received the classical heralding signal. To account for this effect, we execute an XY8–1 decoupling sequence for a total duration of 10 ms before performing the Bell-state measurement. The decoupling duration is much larger than the classical signal travelling time Δt(l) ≈ 200 μs for the maximal fibre length of l = 40 km. Thus, for the measured fibre distances, Bell-state decoherence does not affect the measured Bell-state fidelities. Instead, we find that the fibre-distance-dependence of the nuclear–nuclear entanglement fidelities is well described by SNSPD dark counts and telecom conversion noise photons, which reduce the signal-to-noise ratio at high fibre attenuation (solid line in Fig. 4b).

    In a practical setting, large-scale quantum networks can strongly benefit from existing fibre infrastructure to allow for long-distance entanglement distribution. Deployed fibres are subject to added noise and excess loss, as well as phase- and polarization drifts34,35. We demonstrate that our system is compatible with conventional fibre infrastructure and is resilient to these error sources by generating nuclear entanglement through a 35-km loop of telecom fibre deployed in the Boston area urban environment (Fig. 4d). The overall measured loss in the loop (17 dB at 1,350 nm) exceeds the nominal fibre attenuation of 11 dB at this wavelength, indicative of excess loss typical of deployed environments. As the input polarization of the upconverting PPLN needs to align with the dipole moment of the crystal, polarization drifts introduced by the deployed fibre are actively compensated to prevent a loss in conversion efficiency (Methods). Using the deployed link, we generate entanglement with a fidelity of \({{\mathcal{F}}}_{\left|{\varPhi }_{{\rm{nn}}}^{-}\right\rangle }^{{\rm{ED}}}=0.69(7)\) (Fig. 4c), demonstrating the quantum network performance in a realistic fibre environment.

    Outlook

    Our experiments demonstrate key ingredients for building large-scale deployed networks using the SiV-based integrated nanophotonic platform. They open opportunities for exploration of a variety of quantum networking applications, ranging from distributed blind quantum computing41 and non-local sensing, interferometry and clock networks10,42, to the generation of complex photonic cluster states43. Extension to entanglement distribution between true space-like separated nodes using deployed fibre requires only relatively minor experimental modifications and is not limited by the performance of the quantum nodes (Supplementary Information). The success rate of the entanglement generation is currently limited by losses in the bidirectional QFC, which can be minimized by improving mode-matching into the PPLN and the efficiency of the filtering setup44. Furthermore, in-fibre attenuation could be further reduced to 0.2 dB km−1 by using two-stage QFC to 1,550 nm (ref. 45). The use of WCS also reduces the success rate and fidelity, which could be avoided by using SiV-based single-photon sources46 combined with active strain tuning of the nanophotonic cavities for wavelength matching40,47. Efficient coupling between the fibre network and the nanophotonic cavity could be improved by recently demonstrated cryogenic packaging techniques48, whereas cooling requirements of the repeater nodes could be eased by deterministic straining of SiVs49. Entanglement fidelities could be improved by working with previously demonstrated nanophotonic cavities with higher cooperativity32. Implementing the above improvements, electron–electron entanglement fidelities of about 0.95 with success rates of about 100 Hz could be achieved (Supplementary Information). Finally, the number of accessible qubits could be increased by addressing weakly coupled 13C spins50, allowing for more flexible multi-node network configurations. Combining these advances with the potential ability to create a large number of cavity QED systems fabricated on a chip, this approach can eventually result in large-scale, deployable quantum networking systems.

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  • ‘Quantum internet’ demonstration in cities is most advanced yet

    ‘Quantum internet’ demonstration in cities is most advanced yet

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    A pair of researchers work on one of the quantum network nodes, where mirrors and filters guide the laser beams to the diamond chip.

    A quantum network node at Delft University of Technology.Credit: Marieke de Lorijn for QuTech

    Three separate research groups have demonstrated quantum entanglement — in which two or more objects are linked so that they contain the same information even if they are far apart — over several kilometres of existing optical fibres in real urban areas. The feat is a key step towards a future quantum internet, a network that could allow information to be exchanged while encoded in quantum states.

    Together, the experiments are “the most advanced demonstrations so far” of the technology needed for a quantum internet, says physicist Tracy Northup at the University of Innsbruck in Austria. Each of the three research teams — based in the United States, China and the Netherlands — was able to connect parts of a network using photons in the optical-fibre-friendly infrared part of the spectrum, which is a “major milestone”, says fellow Innsbruck physicist Simon Baier.

    A quantum internet could enable any two users to establish almost unbreakable cryptographic keys to protect sensitive information. But full use of entanglement could do much more, such as connecting separate quantum computers into one larger, more powerful machine. The technology could also enable certain types of scientific experiment, for example by creating networks of telescopes that have the resolution of a single dish hundreds of kilometres wide.

    Two of the studies1,2 were published in Nature on 15 May. The third was described earlier this month in a preprint posted on arXiv3, which has not yet been peer reviewed.

    Impractical environment

    Many of the technical steps for building a quantum internet have been demonstrated in the laboratory over the past decade or so. And researchers have shown that they can produce entanglement using lasers in direct line of sight of each other, either in separate ground locations or on the ground and in space.

    But going from the lab to a city environment is “a different beast”, says Ronald Hanson, a physicist who led the Dutch experiment3 at the Delft University of Technology. To build a large-scale network, researchers agree that it will probably be necessary to use existing optical-fibre technology. The trouble is, quantum information is fragile and cannot be copied; it is often carried by individual photons, rather than by laser pulses that can be detected and then amplified and emitted again. This limits the entangled photons to travelling a few tens of kilometres before losses make the whole thing impractical. “They also are affected by temperature changes throughout the day — and even by wind, if they’re above ground,” says Northup. “That’s why generating entanglement across an actual city is a big deal.”

    The three demonstrations each used different kinds of ‘quantum memory’ device to store a qubit, a physical system such as a photon or atom that can be in one of two states — akin to the ‘1’ or ‘0’ of ordinary computer bits — or in a combination, or ‘quantum superposition’, of the two possibilities.

    In one of the Nature studies, led by Pan Jian-Wei at the University of Science and Technology of China (USTC) in Hefei, qubits were encoded in the collective states of clouds of rubidium atoms1. The qubits’ quantum state can be set using a single photon, or can be read out by ‘tickling’ the atomic cloud to emit a photon. Pan’s team had such quantum memories set up in three separate labs in the Hefei area. Each lab was connected by optical fibres to a central ‘photonic server’ around 10 kilometres away. Any two of these nodes could be put in an entangled state if the photons from the two atom clouds arrived at the server at exactly the same time.

    By contrast, Hanson and his team established a link between individual nitrogen atoms embedded in small diamond crystals with qubits encoded in the electron states of the nitrogen and in the nuclear states of nearby carbon atoms3. Their optical fibre went from the university in Delft through a tortuous 25-kilometre path across the suburbs of The Hague to reach a second laboratory in the city.

    In the US experiment, Mikhail Lukin, a physicist at Harvard University in Cambridge, Massachusetts, and his collaborators also used diamond-based devices, but with silicon atoms instead of nitrogen, making use of the quantum states of both an electron and a silicon nucleus2. Single atoms are less efficient than atomic ensembles at emitting photons on demand, but they are more versatile, because they can perform rudimentary quantum computations. “Basically, we entangled two small quantum computers,” says Lukin. The two diamond-based devices were in the same Harvard laboratory, but to mimic the conditions of a metropolitan network, the researchers used an optical fibre that snaked around the local Boston area. “It crosses the Charles River six times,” Lukin says.

    Challenges ahead

    The entanglement procedure used by the Chinese and the Dutch teams required photons to arrive at a central server with exquisite timing precision, which was one of the main challenges in the experiments. Lukin’s team instead used a protocol that does not require such fine-tuning. Instead of entangling the qubits by getting them to emit photons, the researchers sent one photon to entangle itself with the silicon atom at the first node. The same photon then went around the fibre-optic loop and came back to graze the second silicon atom, thereby entangling it with the first.

    Pan has calculated that at the current pace of advance, by the end of the decade his team should be able to establish entanglement over 1,000 kilometres of optical fibres using ten or so intermediate nodes, with a procedure called entanglement swapping. (At first, such a link would be very slow, creating perhaps one entanglement per second, he adds.) Pan is the leading researcher for a project using the satellite Micius, which demonstrated the first quantum-enabled communications in space, and he says there are plans for a follow-up mission.

    “The step has now really been made out of the lab and into the field,” says Hanson. “It doesn’t mean it’s commercially useful yet, but it’s a big step.”

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  • Fusion of deterministically generated photonic graph states

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    Experimental setup

    The apparatus used in our work consists of a single-sided high-finesse cavity in which we optically trap two rubidium atoms. Most experimental details about the setup including the cavity quantum electrodynamics parameters have already been described elsewhere16. In the following, we provide further information that is important for the current work.

    The atoms are trapped in a two-dimensional optical standing-wave potential formed by two pairs of counter-propagating laser beams. The first is a retro-reflected laser at a wavelength of λ = 1,064 nm along the x axis. The second propagates inside the cavity mode along the y axis with λ = 772 nm. The atoms are loaded from a magneto optical trap to the cavity centre using a second 1,064-nm running-wave laser. The light scattered by the atom during laser cooling is imaged by means of the objective onto an electron-multiplying charge-coupled device camera to spatially resolve the position of the atoms. After each loading attempt, we find a random number of atoms n at random positions. The experimental control software identifies atom pairs with a suitable relative distance d. If no such atom pair is present, a new loading attempt starts immediately. Otherwise, a tightly focused resonant laser beam, propagating through the objective and steered by the AOD, removes the n − 2 unwanted atoms. The x component of the centre-of-mass position of the atom pair (x2 + x1)/2 is then actively stabilized to the centre of the cavity mode by acting on the relative phase of the 1,064-nm counter-propagating laser beams. The y components y1 and y2 are controlled by modulating the optical power of the 772-nm intra-cavity trap until the atoms are found in a desired position.

    Fusion gate and post-selection

    For a fusion gate to be successful, two photons have to be detected, as described in the main text. Mathematically, this can be understood by considering two atom–photon entangled states of the form

    $$\left|{\psi }_{{\rm{AP}}}\right\rangle =\frac{1}{\sqrt{2}}\left(\left|F=1,{m}_{F}=1\right\rangle \left|L\right\rangle -\left|F=1,{m}_{F}=-1\right\rangle \left|R\right\rangle \right).$$

    (3)

    The relative minus sign in the above equation arises from the Clebsch–Gordan coefficients in the two emission paths. Applying the projector R|L| to the product state |ψAP |ψAP corresponds to the detection of an R and an L photon, signalling a successful fusion. This leaves us with the |Ψ+ Bell state. Here we implicitly assumed that the two photons occupy the same spatiotemporal mode function. In the experiment, however, their temporal wave packet may not be perfectly indistinguishable, leading to an incomplete erasure of which-path information. Such imperfection can arise from spontaneous scattering by means of the excited state or from unbalanced atom–cavity or atom–laser coupling. This effect becomes visible when post-selecting on the arrival time of the photons. The influence of the arrival time on the fidelity of the atom–atom Bell state is summarized in Extended Data Fig. 1. Panel a shows the intensity profile of the photon temporal wave function as a function of tR,L, with tR and tL being the arrival times of the R-polarized and L-polarized photons produced in the fusion process, respectively. Events in which a photon arrives outside the time interval marked by the dashed lines are discarded. This interval contains about 98% of all single-photon counts. Panel b is a two-dimensional density plot of the number of two-photon events versus arrival times tR and tL. We can see that most events lie in the vicinity of the point tR = tL = 200 ns. The dashed line encloses the region defining the post-selection criteria, which we specify in more detail below. Panel c is a density plot similar to b showing the fidelity as a function of tR and tL. We find that the fidelity is highest near the diagonal of the plot, that is tR ≈ tL. This motivates our choice of the post-selection region enclosed by the dashed line. Pixels for which we did not acquire enough data to compute the fidelity are shown in white. The fidelity is computed using the formula

    $${\mathcal{F}}=\frac{1}{4}\left(1+\langle XX\rangle +\langle YY\rangle -\langle ZZ\rangle \right).$$

    (4)

    Here XX, YY and ZZ are two-qubit operators consisting of the respective Pauli operators. In panels d–f, we analyse their expectation values XX, YY and ZZ as a function of arrival time difference |tR − tL|. We plot the expectation value both for |tR − tL| = τ (orange) and |tR − tL| ≤ τ (purple), that is, the cumulative expectation value. We find all correlators to be in good agreement with the ideal case, for which we expect XX = YY = 1 and ZZ = −1. The high fidelity of the two-atom Bell state is also an indicator of a high photon indistinguishability. The dashed lines indicate the maximum value of τ, that is, |tR − tL| ≤ τ, chosen for the data presented in Fig. 1c.

    Post-selection criteria

    For the data in Extended Data Fig. 1c, as well as the data presented in the main text, we apply two post-selection steps. The first step consists of restricting the absolute detection time of the photons to a predefined interval of 1 μs width (see dashed lines in Extended Data Fig. 1a). This step applies to both single-photon and two-photon events. The second post-selection condition involves the relative arrival time difference |tR − tL| in the case of two-photon events and therefore only applies to photons generated in the fusion process. The diagonal dashed lines in Extended Data Fig. 1b,c mark the condition |tR − tL| ≤ τmax = 250 ns. Events in which the photons are detected with a relative delay larger than τmax are discarded. In about 80% of experimental runs, the two photons fall within the interval of τmax.

    As stated in the main text, the atom–atom Bell-state fidelity ranges between 0.851(6) and 0.963(8). The first number refers to the scenario in which no post-selection on the photon arrival time is applied. The second number is obtained when restricting the photon arrival times to tR,L ≤ 500 ns and |tR − tL| ≤ 20 ns. In this case, the post-selection ratio is about 15%.

    The above numbers refer to the scenario in which the atom is initialized to |F = 2, mF = 0 before photon generation. However, in the ring and tree states protocol, the last fusion step consists of a two-photon emission from |F = 2, mF = ±2. In this case, the photon wave packet is slightly longer, as the mF = ±2 Zeeman sublevels couple to different excited states in the emission process. Here we apply the same 1-μs time interval as for the mF = 0 case, as at least 95% of the photon wave packet is enclosed by this window. However, for the two-photon events in the fusion process, we choose a maximum time difference of τmax = 400 ns to accommodate for a post-selection fraction of about 80%, similar to the mF = 0 case.

    Atom readout

    At the end of the generation sequence for tree and ring graph states, the atomic qubits are still entangled with the photons previously generated. One way to measure the atomic qubits is to perform an atom-to-photon state transfer, as done in ref. 16. Here the qubit is mapped from |F = 1, mF = ±1 to |F = 2, mF = ±1 before photon production. In this way, the qubit is fully transferred to the photon, which can then be measured optically. In this work, however, we chose another technique to measure the atomic qubit. For a Z measurement, we transfer the qubit to |F = 2, mF = ±2 and generate a photon measuring it in the R/L basis. Detecting an R (L) photon projects the atomic qubit onto the state |0S (|1S). When measuring the qubit in X or Y, we set the basis directly on the atomic qubit with a π/2 pulse whose phase is tuned according to the basis. The advantage of this scheme is that it can be repeated until success in the case of photon loss, thus increasing the overall efficiency of the state readout. However, as errors are more likely to occur after many repetitions, we limit the number of attempts to three.

    Detailed protocol description

    In the following, we will describe the generation protocol for the ring and tree graph states with explicit expressions for each step. In the derivation, we do not explicitly include the free evolution of the atomic qubit. In the experiment, the phases that arise from the qubit oscillation are tracked by measuring the stabilizer operators as a function of certain timing parameters related to, for instance, Raman transfers and photon emissions. Notably, these phases may be tuned for each atom independently by varying the respective time of the photon-production pulse.

    Ring states

    We first describe the protocol of the ring graph states and choose the pentagon ring as a specific example. The box-shaped and hexagon-shaped graphs are obtained from a similar protocol, only omitting a single π/4 rotation. A sketch of the experimental sequence is given in Extended Data Fig. 2a.

    The first step of the protocol is to entangle the two atoms and prepare them in the Bell state \(|{\Psi }^{+}\rangle =({|01\rangle }_{{\rm{S}}}+{|10\rangle }_{{\rm{S}}})/\sqrt{2}\). To obtain the pentagon graph, which has an odd number of vertices, we need to apply a global −π/4 pulse. This ‘pushes’ the two qubits apart, forming two separate vertices with an edge between them (Extended Data Fig. 2a, (2)). The corresponding state (omitting normalization constants) reads

    $$\begin{array}{l}\mathop{\longrightarrow }\limits^{-{\rm{\pi }}/4}{| 00\rangle }_{{\rm{S}}}+{| 01\rangle }_{{\rm{S}}}+{| 10\rangle }_{{\rm{S}}}-{| 11\rangle }_{{\rm{S}}}\\ =\,{| 0+\rangle }_{{\rm{S}}}+{| 1-\rangle }_{{\rm{S}}}.\end{array}$$

    (5)

    Here we have substituted the transformations

    $$\begin{array}{l}{\left|0\right\rangle }_{{\rm{S}}}\mathop{\longrightarrow }\limits^{-{\rm{\pi }}/4}\cos \left(\frac{\theta }{2}\right){\left|0\right\rangle }_{{\rm{S}}}+\sin \left(\frac{\theta }{2}\right){\left|1\right\rangle }_{{\rm{S}}},\\ {\left|1\right\rangle }_{{\rm{S}}}\mathop{\longrightarrow }\limits^{-{\rm{\pi }}/4}-\sin \left(\frac{\theta }{2}\right){\left|0\right\rangle }_{{\rm{S}}}+\cos \left(\frac{\theta }{2}\right){\left|1\right\rangle }_{{\rm{S}}}\end{array}$$

    (6)

    and used θ = −π/4, as well as \(\cos \left(\frac{{\rm{\pi }}}{8}\right)=\frac{\sqrt{2+\sqrt{2}}}{2}\) and \(\sin \left(\frac{{\rm{\pi }}}{8}\right)=\frac{\sqrt{2-\sqrt{2}}}{2}\). Subsequently, each atom emits a photon, giving

    $$\begin{array}{l}\mathop{\longrightarrow }\limits^{{\rm{PP}}}{| 0\rangle }_{{\rm{S}}}| 0\rangle (| 0\rangle {| 0\rangle }_{{\rm{S}}}+| 1\rangle {| 1\rangle }_{{\rm{S}}})+{| 1\rangle }_{{\rm{S}}}| 1\rangle (| 0\rangle {| 0\rangle }_{{\rm{S}}}-| 1\rangle {| 1\rangle }_{{\rm{S}}})\\ \,=\,({| 0\rangle }_{{\rm{S}}}| 0\rangle +{| 1\rangle }_{{\rm{S}}}| 1\rangle )| 0\rangle {| 0\rangle }_{{\rm{S}}}+({| 0\rangle }_{{\rm{S}}}| 0\rangle -{| 1\rangle }_{{\rm{S}}}| 1\rangle )| 1\rangle {| 1\rangle }_{{\rm{S}}},\end{array}$$

    (7)

    followed by a π/2 rotation on the atomic qubits:

    $$\begin{array}{l}\mathop{\longrightarrow }\limits^{{\rm{\pi }}/2}({| +\rangle }_{{\rm{S}}}| 0\rangle +{| -\rangle }_{{\rm{S}}}| 1\rangle )| 0\rangle {| +\rangle }_{{\rm{S}}}+({| +\rangle }_{{\rm{S}}}| 0\rangle -{| -\rangle }_{{\rm{S}}}| 1\rangle )| 1\rangle {| -\rangle }_{{\rm{S}}}\\ \,=\,({| 0\rangle }_{{\rm{S}}}| +\rangle +{| 1\rangle }_{{\rm{S}}}| -\rangle )| 0\rangle {| +\rangle }_{{\rm{S}}}+({| 0\rangle }_{{\rm{S}}}| -\rangle +{| 1\rangle }_{{\rm{S}}}| +\rangle )| 1\rangle {| -\rangle }_{{\rm{S}}},\end{array}$$

    (8)

    which is equal to a four-qubit linear cluster state with the atoms at both ends of the chain. Note that the (global) π/2 pulse affects only the spin component of the multi-qubit state. We perform another photon production on both spins and obtain

    $$\begin{array}{l}\mathop{\longrightarrow }\limits^{{\rm{PP}}}({| 0\rangle }_{{\rm{S}}}| 0+\rangle +{| 1\rangle }_{{\rm{S}}}| 1-\rangle )| 0\rangle (| 0\rangle {| 0\rangle }_{{\rm{S}}}+| 1\rangle {| 1\rangle }_{{\rm{S}}})\\ \,+\,({| 0\rangle }_{{\rm{S}}}| 0-\rangle +{| 1\rangle }_{{\rm{S}}}| 1+\rangle )| 1\rangle (| 0\rangle {| 0\rangle }_{{\rm{S}}}-| 1\rangle {| 1\rangle }_{{\rm{S}}}).\end{array}$$

    (9)

    We apply a Z gate to qubit 6 and a Hadamard to qubits 1 and 6 (indices run from left to right).

    $$\begin{array}{l}\mathop{\longrightarrow }\limits^{{H}_{1}\otimes {Z}_{6}\otimes {H}_{6}}({| +\rangle }_{{\rm{S}}}| 0+\rangle +{| -\rangle }_{{\rm{S}}}| 1-\rangle )| 0\rangle (| -\rangle {| 0\rangle }_{{\rm{S}}}+| +\rangle {| 1\rangle }_{{\rm{S}}})\\ \,\,\,\,\,+\,({| +\rangle }_{{\rm{S}}}| 0-\rangle +{| -\rangle }_{{\rm{S}}}| 1+\rangle )| 1\rangle (| +\rangle {| 0\rangle }_{{\rm{S}}}+| -\rangle {| 1\rangle }_{{\rm{S}}}).\end{array}$$

    (10)

    Now we perform the fusion operation on qubits 1 and 6 and obtain

    $$\begin{array}{l}\mathop{\longrightarrow }\limits^{{\rm{Fusion}}}\frac{1}{2\sqrt{2}}({| 10\rangle }_{{\rm{S}}}(| 0+0+\rangle +| 1-0+\rangle +| 0-1-\rangle +| 1+1-\rangle )\\ \,\,+{| 01\rangle }_{{\rm{S}}}(| 0+0-\rangle -| 1-0-\rangle +| 0-1+\rangle -| 1+1+\rangle ))\\ \,=\,\frac{1}{2\sqrt{2}}({| 0\rangle }_{{\rm{L}}}(| 0+0+\rangle +| 1-0+\rangle +| 0-1-\rangle +| 1+1-\rangle )\\ \,\,+{| 1\rangle }_{{\rm{L}}}(| 0+0-\rangle -| 1-0-\rangle +| 0-1+\rangle -| 1+1+\rangle )).\end{array}$$

    (11)

    Here we have moved the second spin qubit to the first position and reintroduced the logical qubit encoding using |0L and |1L. Furthermore, we have added a normalization constant. The above expression represents the state that corresponds to the graph shown in Extended Data Fig. 2c. The measured stabilizer expectation values are shown in Extended Data Fig. 2b.

    Tree states

    We now describe the experimental protocol for generating the target state of the form

    $$|{\psi }_{{\rm{t}}{\rm{r}}{\rm{e}}{\rm{e}}}\rangle =\frac{1}{2\sqrt{2}}[|0\rangle {(|0++\rangle +|1–\rangle )}^{\otimes 2}+|1\rangle {(|0++\rangle -|1–\rangle )}^{\otimes 2}].$$

    (12)

    We start by preparing both atoms in the |F = 2, mF = 0 state, followed by three sequential photon-production cycles on each atom in parallel. From this, we obtain the tensor product of two GHZ states, each consisting of one atom and three photons (see also ref. 16). Omitting normalization constants, we can write the state as

    $$| \psi \rangle =({| 0\rangle }_{{\rm{S}}}| 000\rangle +{| 1\rangle }_{{\rm{S}}}| 111\rangle )\otimes ({| 0\rangle }_{{\rm{S}}}| 000\rangle -{| 1\rangle }_{{\rm{S}}}| 111\rangle ).$$

    (13)

    Note that the second term carries a relative minus sign with respect to the first term. This is reflected in the parity measurement shown in Fig. 3b. We now perform a Hadamard gate on all qubits except qubits 2 and 6 (indices run from left to right) and obtain

    $$\longrightarrow ({| +\rangle }_{{\rm{S}}}| 0++\rangle +{| -\rangle }_{{\rm{S}}}| 1–\rangle )\otimes ({| +\rangle }_{{\rm{S}}}| 0++\rangle -{| -\rangle }_{{\rm{S}}}| 1–\rangle ).$$

    (14)

    For the atoms, the Hadamard is carried out with a Raman laser (see Fig. 1e), whereas for the photons, it is absorbed into the setting of the measurement basis.

    We now merge both branches into one larger graph state by applying the fusion gate. Hence we generate two photons from the atoms with the global STIRAP control laser. Detecting one photon in R and one in L effectively projects the atoms onto the subspace {|01S, |10S}.

    $$\begin{array}{l}\mathop{\longrightarrow }\limits^{{| 01\rangle }_{{\rm{S}}}{\langle 01| }_{{\rm{S}}}+{| 10\rangle }_{{\rm{S}}}{\langle 10| }_{{\rm{S}}}}({| 10\rangle }_{{\rm{S}}}+{| 01\rangle }_{{\rm{S}}}){| 0++\rangle }^{\otimes 2}+({| 10\rangle }_{{\rm{S}}}-{| 01\rangle }_{{\rm{S}}})| 0++\rangle | 1–\rangle \\ \,+({| 10\rangle }_{{\rm{S}}}-{| 01\rangle }_{{\rm{S}}})| 1–\rangle | 0++\rangle +({| 10\rangle }_{{\rm{S}}}+{| 01\rangle }_{{\rm{S}}}){| 1–\rangle }^{\otimes 2}\\ \,=\,{| 10\rangle }_{{\rm{S}}}{(| 0++\rangle +| 1–\rangle )}^{\otimes 2}+{| 01\rangle }_{{\rm{S}}}{(| 0++\rangle -| 1–\rangle )}^{\otimes 2}.\end{array}$$

    (15)

    For convenience, we have moved the second spin qubit to the first position in the above expression, which allows us to express the two atoms as a logical qubit encoded in the basis {|0L ≡ |10S, |1L ≡ |01S}. Adding a normalization constant, we can then write the final state as

    $$|{\psi }_{{\rm{t}}{\rm{r}}{\rm{e}}{\rm{e}}}\rangle =\frac{1}{2\sqrt{2}}[{|0\rangle }_{{\rm{L}}}{(|0++\rangle +|1–\rangle )}^{\otimes 2}+{|1\rangle }_{{\rm{L}}}{(|0++\rangle -|1–\rangle )}^{\otimes 2}].$$

    (16)

    This is equal to the expression in equation (12), with the only difference being that the root qubit is now redundantly encoded by the two atoms. Alternatively, it would be possible to remove one of the atoms from the state by an X basis measurement.

    Coincidence rate

    For each multi-qubit state, the typical generation and detection rate is between 0.4 and 2.3 coincidences per minute. The total number of events as well as the total measurement time are summarized in Extended Data Table 1 for each graph state generated in this work. These numbers include all post-selection steps as described above.

    Entanglement witness and fidelity bounds

    To quantify the agreement between the experimentally produced multi-photon state and the target state, we use an entanglement witness. This has the advantage that we can derive a lower bound of the fidelity without the need for full quantum-state tomography. The fidelity of a density matrix ρ with respect to the target state |ψ is defined as

    $${\mathcal{F}}={\rm{Tr}}\{\rho \left|\psi \right\rangle \left\langle \psi \right|\}.$$

    (17)

    Using the stabilizers, we can express the projector to the target state as

    $$\left|\psi \right\rangle \left\langle \psi \right|=\prod _{i}\frac{1+{S}_{i}}{2}=\prod _{i\in a}\frac{1+{S}_{i}}{2}\prod _{j\in b}\frac{1+{S}_{j}}{2}={G}_{a}\cdot {G}_{b}.$$

    (18)

    Here we have written the projector as a product of two terms Ga and Gb associated with two sets of stabilizers a and b. Each set a/b can be measured with a single local measurement setting Ma/Mb. These only involve measurements in the X or Z basis for every qubit. We can then write the projector in terms of Ga and Gb, giving

    $$\left|\psi \right\rangle \left\langle \psi \right|={G}_{a}\cdot {G}_{b}={G}_{a}+{G}_{b}-1+\left(1-{G}_{a}\right)\left(1-{G}_{b}\right)$$

    (19)

    As the stabilizers Si take the values +1 or −1, the product terms Ga and Gb are either 1 or 0. We conclude that (1 − Ga)(1 − Gb) is non-negative. Omitting this term, we find the lower bound

    $${{\mathcal{F}}}_{-}\equiv \left\langle {G}_{a}\right\rangle +\left\langle {G}_{b}\right\rangle -1\le {\mathcal{F}}.$$

    (20)

    The above expression is applicable if the stabilizers can be divided into two sets a and b, each of which can be measured with a single measurement setting (Ma and Mb). In the context of our experiment, this applies to tree graph states as well as ring graph states of even parity, that is, an even number of vertices. To the best of our knowledge, there is no equivalent method for ring graph states of odd parity, such as the pentagon graph, and a fidelity lower bound cannot be derived.

    We can further derive a fidelity upper bound based on the terms Ga and Gb. First, for any pure state |ψ, we have

    $$\left\langle \psi \right|{G}_{a}{G}_{b}\left|\psi \right\rangle \le \sqrt{\left\langle \psi \right|{G}_{a}{G}_{a}^{\dagger }\left|\psi \right\rangle \left\langle \psi \right|{G}_{b}^{\dagger }{G}_{b}\left|\psi \right\rangle },$$

    (21)

    by direct application of the Cauchy–Schwarz inequality. The terms (1 + Si)/2 are projectors, because \({S}_{i}^{2}=1\) and therefore

    $${\left(\frac{1+{S}_{i}}{2}\right)}^{2}=\frac{1+2{S}_{i}+{S}_{i}^{2}}{4}=\frac{1+{S}_{i}}{2}.$$

    (22)

    By construction, the stabilizers Si commute and therefore the projectors (1 + Si)/2 commute as well. Hence, because Ga/b are products of commuting projectors, Ga and Gb themselves are also projectors:

    $${G}_{a/b}^{2}={\left(\prod _{i\in a/b}\frac{1+{S}_{i}}{2}\right)}^{2}=\prod _{i\in a/b}{\left(\frac{1+{S}_{i}}{2}\right)}^{2}=\prod _{i\in a/b}\frac{1+{S}_{i}}{2}={G}_{a/b}.$$

    (23)

    Equation (21) can then be simplified as \(\langle \psi | {G}_{a}{G}_{b}| \psi \rangle \le \sqrt{\langle \psi | {G}_{a}| \psi \rangle \langle \psi | {G}_{b}| \psi \rangle }\).

    Then, to generalize to mixed states, we write the mixed state ρ as a linear combination of pure states, that is, \(\rho ={\sum }_{k}{p}_{k}\left|{\psi }_{k}\right\rangle \left\langle {\psi }_{k}\right|\), and apply the above inequality to each of them:

    $$\langle {G}_{a}{G}_{b}\rangle =\sum _{k}{p}_{k}\langle {\psi }_{k}| {G}_{a}{G}_{b}| {\psi }_{k}\rangle \le \sum _{k}{p}_{k}\sqrt{\langle {\psi }_{k}| {G}_{a}| {\psi }_{k}\rangle \langle {\psi }_{k}| {G}_{b}| {\psi }_{k}\rangle }.$$

    (24)

    We identify the right term as a scalar product of two vectors and again use the Cauchy–Schwarz inequality

    $$\sum _{k}\sqrt{{p}_{k}\left\langle {\psi }_{k}\right|{G}_{a}\left|{\psi }_{k}\right\rangle }\sqrt{{p}_{k}\left\langle {\psi }_{k}\right|{G}_{b}\left|{\psi }_{k}\right\rangle }\le \sqrt{\left(\sum _{k}{p}_{k}\left\langle {\psi }_{k}\right|{G}_{a}\left|{\psi }_{k}\right\rangle \right)\left(\sum _{{k}^{{\prime} }}{p}_{{k}^{{\prime} }}\left\langle {\psi }_{{k}^{{\prime} }}\right|{G}_{b}\left|{\psi }_{{k}^{{\prime} }}\right\rangle \right)},$$

    (25)

    which shows the upper bound of the fidelity

    $${\mathcal{F}}=\langle {G}_{a}{G}_{b}\rangle \le \sqrt{\langle {G}_{a}\rangle \langle {G}_{b}\rangle }\equiv {{\mathcal{F}}}_{+}.$$

    (26)

    In the next section, we will use both fidelity bounds for a comparison between the experimental data and the expected fidelity.

    Estimation of errors

    In our previous work16, we identified some error mechanisms present in our system. For single-emitter protocols, the main error sources are spontaneous scattering in the photon-emission process (about 1% per photon) and imperfect Raman rotations (about 1% per π/2 pulse). In the following, we discuss several more mechanisms that could negatively affect the fidelity. In some cases, the effect of these mechanisms on the fidelity of multi-qubit entangled states is difficult to quantify because of the complexity of the entanglement topology and the protocols to generate it. Furthermore, measuring the fidelity of multi-qubit states is a non-trivial task and our measurement setup only allows us to extract a lower and an upper bound of the fidelity.

    Fusion gate

    For the two-emitter protocols developed in this work, the cavity-assisted fusion gate is probably the largest source of error. As shown in the main text, this mechanism can be used to prepare the |Ψ+ Bell state with a fidelity ranging between 0.85 and 0.96, depending on how strictly we post-select on the arrival time of the photons. The fact that the fidelity decreases with a larger arrival time difference τ (see Extended Data Fig. 1) can be explained by an imperfect indistinguishability of the photons involved in the fusion process. For the standard value of τmax = 250 ns, the fidelity of the |Ψ+ Bell state is 0.92. This number includes state readout of the two atoms, each of which is expected to introduce an error similar to a photon emission (roughly 1%). We conclude that the infidelity from the fusion process is on the order of 6%.

    Decoherence

    Another potential source of infidelity is atomic decoherence caused by magnetic-field noise or intensity fluctuations of the optical-trapping beams. We have measured the coherence time of the atomic qubit T2 to be approximately 1 ms. However, the atomic qubit is largely protected by a dynamical decoupling mechanism that is built into the protocol16, thereby extending the coherence time. The exact extent to which this mechanism takes effect depends on the specific timing parameters in the sequence and the frequency range in which the noise sources are most dominant (for example, magnetic-field fluctuations). Therefore, it is difficult to quantify how much the decoherence translates into infidelity of the final graph state. Furthermore, different types of graph state are more or less susceptible to noise44. It is therefore not straightforward to theoretically model the role of decoherence in the fidelity of the final multi-partite entangled state.

    Qubit leakage

    During the protocol, the emitter qubits are continuously transferred between different atomic states. These states are |1, ±1, |2, ±2 and |2, 0, in which we again write the state as |F, mF with the quantum numbers F and mF. However, there seems to be a low probability that, during the emission process, the atom undergoes a transition to |1, 0 (instead of |1, ±1). This is readily explained by and consistent with the finding of spontaneous scattering during the vSTIRAP process, but may equally result from a contamination of σ+/σ polarization components in the vSTIRAP control laser. The latter is in turn caused by either an imperfect polarization setting or longitudinal polarization components owing to the tight focus of the beam. The unwanted σ+/σ components couple to the \(\left|{F}^{{\prime} }=1,{m}_{F}^{{\prime} }=\pm 1\right\rangle \) states and can thus drive a two-photon transition to |F = 1, mF = 0. This process results in the atom leaving the qubit subspace but, unfortunately, such an event remains undetected. If the protocol resumes with a Raman π/2 pulse, the parasitic population in |1, 0 is then partly transferred to |2, ±1, as the corresponding transitions have the same resonance frequency. A subsequently emitted photon will then yield a random measurement outcome, which is detrimental to the fidelity of the state.

    The leakage mechanism described above is difficult to quantify, mainly because our experiment lacks an mF-selective state readout. We do however estimate that the longitudinal polarization components of the addressing beam have a relative amplitude on the order of about 1%, contributing to each single-photon emission. For the global beam, this effect is negligible owing to a larger focus.

    Other sources of error

    Other sources of error include drifts of the optical fibres, such as for the Raman beam, the global and addressing vSTIRAP beam or the optical traps, as well as the magnetic field. Furthermore, the position of the atoms is not fixed but varies from one loading attempt to another. In this work, we chose position criteria that are less strict than those in ref. 16, to increase the data rate. In combination with the drifts mentioned above, this leads to a variance in coupling between the atoms and the cavity, as well as the atoms and different laser beams. As a consequence, this may affect the fidelity of different processes, such as the fusion gate or Raman transfers. Furthermore, a drift of the magnetic field or the light shift induced by the optical trap can influence the phase of the atomic qubits at different stages of the protocol.

    A way to reduce the overall infidelity would be to increase the cooperativity C. This would reduce the effect of spontaneous scattering, improve photon indistinguishability and thereby increase the fidelity of the fusion process and partly mitigate the qubit-leakage error. Photon emission through the D1 line of rubidium would have a similar effect, owing to a larger hyperfine splitting in the 52P1/2 excited state. Another strategy to improve the system would be a better control of the atom positions by using more advanced trapping techniques, such as optical tweezers. This would greatly reduce all errors associated with the variance of the atom positions. It would also allow longer trapping times and therefore higher data rates.

    Error model

    As an (oversimplified) ansatz to estimate the combined effect of the error mechanisms described above, we write the density matrix as a mixture of the ideal density matrix and white noise. This is a common approach to investigate, for instance, the robustness of entanglement witnesses against noise (see, for example, ref. 45). The density matrix then reads

    $${\rho }_{\exp }=\left(1-{p}_{{\rm{noise}}}\right){\rho }_{{\rm{ideal}}}+{p}_{{\rm{noise}}}\frac{{\mathbb{1}}}{{2}^{n}},$$

    (27)

    in which pnoise is the total error probability, ρideal is the ideal density matrix, \({\mathbb{1}}\) is the identity matrix and n is the number of qubits. We decompose pnoise into the different error contributions and write

    $${p}_{{\rm{noise}}}=1-{\left(1-{p}_{{\rm{P}}}\right)}^{{N}_{{\rm{P}}}}{\left(1-{p}_{{\rm{R}}}\right)}^{{N}_{{\rm{R}}}}{\left(1-{p}_{{\rm{F}}}\right)}^{{N}_{{\rm{F}}}}.$$

    (28)

    Here pP denotes the probability of spontaneous scattering during photon emission, pR the error probability during a Raman rotation, pF the error probability for the fusion process and NP, NR and NF are the respective number of operations in the protocol. Note that we do not include mechanisms such as decoherence or qubit leakage in the above formula, as we are unable to assign a value to a specific step of the protocol.

    In Extended Data Table 2, we compare the fidelity model to the measured lower and upper bounds as defined by equation (20) and equation (26), respectively. For the tree and box graph states, the predicted fidelities \({{\mathcal{F}}}_{{\rm{model}}}\) are found to fall between the measured bounds, as expected. For the hexagon graph, \({{\mathcal{F}}}_{{\rm{model}}}\) falls slightly above the upper bound but is still consistent with it when taking into account the statistical uncertainty (less than one standard deviation). As mentioned earlier, the model does not include the effect of qubit leakage, decoherence and drifts of, for instance, the magnetic field or optical fibres. Hence, it is likely that the predicted fidelities are slightly overestimated.

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  • Intel brings quantum-computing microchips a step closer

    Intel brings quantum-computing microchips a step closer

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    Nature, Published online: 01 May 2024; doi:10.1038/d41586-024-01208-z

    By adapting methods for fabricating and testing conventional computer chips, researchers have brought silicon-based quantum computers closer to reality — and to accessing the immense benefits of a mature chipmaking industry.

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  • Probing entanglement in a 2D hard-core Bose–Hubbard lattice

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  • High-fidelity spin qubit operation and algorithmic initialization above 1 K

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    Measurement setup

    The full experimental setup is shown in Extended Data Fig. 1. The device is measured in a Bluefors XLD400 dilution refrigerator. The device is mounted on the cold finger. Within T = 1 K, elevation from the base temperature is achieved by switching on and tuning the heater near the sample. Temperatures above 1 K are attained by reducing the amount of He mixture in the circulation and consequently the cooling power. Temperature control becomes non-trivial above 1.2 K and nonviable above 1.5 K.

    An external d.c. magnetic field is supplied by an American Magnetics AMI430 magnet. The magnetic field points in the [110] direction of the Si lattice. The d.c. voltages are supplied with Basel Precision Instruments SP927 LNHR DACs through d.c. lines with a bandwidth of 0–20 Hz. Dynamic voltage pulses are generated with a Quantum Machines OPX and combined with d.c. voltages by custom voltage combiners at the 50 K stage in the refrigerator. The OPX has a sampling time of 4 ns. The dynamic pulse lines in the fridge have a bandwidth of 0–50 MHz, which translates into a minimum rise time of 20 ns. Microwave pulses are synthesized using a Keysight PSG8267D Vector Signal Generator with the baseband I/Q and pulse modulation signals from the OPX. The modulated signal spans from 250 kHz to 44 GHz but is band-limited by the fridge line and the d.c. block.

    The charge sensor comprises a single-island SET connected to a tank circuit for reflectometry measurement. The return signal is amplified by a Cosmic Microwave Technology CITFL1 LNA at the 4 K stage and a Mini-circuits ZX60-P33ULN+ LNA followed by two Mini-circuits ZFL-1000LN+ LNAs at room temperature. The Quantum Machines OPX generates the tones for the RFSET and digitizes and demodulates the signals after the amplification.

    Device tune-up

    We first load the electrons according to the mapping of the double-dot charge configurations over a large range, using lock-in charge sensing measurement61 with the RFSET. The measurement can be done in the physical gate basis by sweeping VP1 and VP2, as shown in Extended Data Fig. 2a, or in the virtual gate basis by sweeping VP1 − VP2 and VJ, as shown in Fig. 1a. In the virtual gate basis, voltages of −0.32 VJ and −0.25 VJ are applied on P1 and P2 to compensate for the effect of pulsing J. During operation, each dot is loaded with an odd number of electrons, from which the unpaired electron carries the spin information. This is denoted as the (m + 1, n + 1) charge state in the charge maps, where m and n are even numbers.

    The tune-up proceeds with locating the PSB region around the inter-dot charge transition, as indicated by the dashed square in Extended Data Fig. 2c,d. The initial PSB search involves loading a mixed spin state in (m + 1, n + 1), which has some probability of being even-parity (|↓↓ or |↑↑), and subsequently pulsing to a location near the inter-dot charge transition point. Single-shot charge readout is performed before and after reaching the location and the final readout signal is provided by subtracting the two signals. Except at ultra-low B0, the readout mechanism is dominated by parity readout because of the relatively large dEZ between the two qubits32. An even-parity spin state appears as blockaded in the PSB region, which translates to a lower radiofrequency signal compared with that from an unblockaded state. The averaged radiofrequency signal, therefore, indicates the probability of having an even-parity state across multiple shots.

    The two-level behaviour in the PSB region is used to perform single-shot spin readout. The readout signal in each shot of the experiment is compared with a preset threshold that lies between the two levels, as we see in the readout histograms in Fig. 2b. We assign value 1 to a blockaded readout, and value 0 to an unblockaded readout. Finally, we average over all shots to obtain Pblockade for the statistics.

    Extended Data Fig. 2f shows the ESR spectrum as a function of VJ, in which we identify two regimes. At VJ < 1.175 V, only two transitions pertaining to the driven rotation of the individual qubits are detected. Driven over time, these transitions correspond to the Rabi oscillations in Fig. 1e. At VJ > 1.175 V, in which the exchange energy is large, we see four transitions among the four two-qubit states corresponding to the controlled rotation operations38,50. The layout of the transitions, together with the background signal, shows the composition of the initialized qubit state. The traces in Fig. 2a are taken from these measurements at high VJ. A more scalable two-qubit operation is the electrically pulsed controlled phase operation (CZ)35,36. This is adopted in this work to construct the CZ gate (Extended Data Fig. 2g), or the DCZ gate in the main text.

    Algorithmic initialization

    When the qubit energy hfqubit is greater than the thermal energy kBT, electron-spin qubit initialization may rely on intrinsic polarization mechanisms such as spin-dependent tunnelling from a reservoir62,63,64, PSB17,18,49,65 or relaxation16,43,66. Higher-fidelity single-qubit state preparation can be achieved using initialization by measurement39,67 and conditional single-qubit pulses9,68. These approaches either partially rely on intrinsic polarization or require readout with a reservoir, which are incompatible with operation at elevated temperatures. In this work, we design a generic two-qubit algorithmic initialization protocol that works in conditions for which hfqubit is comparable to or less than kBT. The method is applicable to a large-scale qubit array, in which initialization and readout are performed pairwise16,49,65.

    The algorithmic initialization protocol, as shown in Extended Data Fig. 3a, proceeds as follows:

    1. 1.

      Enter (m + 1, n + 1) to create two unpaired spins in the double-dot system.

    2. 2.

      This results in one of the |↓↓, |↓↑, |↑↓ and |↑↑ states. The probability of creating the ground state |↓↓ decreases as the temperature increases, as the thermal energy becomes comparable or greater than the qubit exchange coupling and the Zeeman energies.

    3. 3.

      Ramp to the PSB region for parity readout and apply a filter that rejects odd-parity states. The parity readout preserves the even-parity states as long as it is performed faster than the spin relaxation time32.

      1. (a)

        If the state is unblockaded and thus determined as an odd-parity (|↓↑, |↑↓) or excited state, the initialization is restarted.

      2. (b)

        If the state is blockaded and thus determined as even-parity (|↓↓, |↑↑), the initialization proceeds to the next stage.

    4. 4.

      This results in either |↓↓ or |↑↑, with an increased probability of |↓↓ from step 3. We calibrate the CZ gate at this stage, either from the exchange-induced splitting of the ESR transitions (Extended Data Fig. 2f) or from the CZ oscillations (Extended Data Fig. 2g).

    5. 5.

      A zero-CNOT (zCNOT) gate23 is performed to convert |↑↑ into |↑↓, leaving |↓↓ unchanged. The construction of the zCNOT gate in this work is shown in Extended Data Fig. 2g.

    6. 6.

      Ramp to the PSB region for parity readout, and apply a filter that rejects odd-parity states.

      1. (a)

        If the state is unblockaded and thus determined as |↑↓ or an excited state, the initialization is restarted.

      2. (b)

        If the state is blockaded and thus determined as |↓↓, the initialization is determined to be completed.

    7. 7.

      The resulting state is purely |↓↓.

    The if conditions above are implemented using real-time logic in the FPGA.

    The protocol can also be adapted to prepare any other state on the parity basis. |↑↓ and |↓↑ can be prepared from |↓↓ with a microwave π pulse on Q1 and Q2. |↑↑ can be prepared by replacing the zCNOT with CNOT in the algorithm.

    We test the algorithmic initialization in a wide range of B0 from 1 T down to 25 mT. The results at different stages of the protocol are seen in Fig. 2a and Extended Data Fig. 3b. Stage I, the outcome of a 100-μs ramp into the operation point, has a mixture of |↓↓, |↓↑, |↑↓ and |↑↑ states and the measured ESR transitions are almost indistinguishable. After Stage II, the output is a mixture of |↓↓ and |↑↑ with the odd-parity states or excited states filtered out through PSB, which can be identified from the associated ESR transitions. Stage III converts the remnant |↑↑ into |↑↓, which is then filtered out through PSB because of the odd parity.

    It is also important to assess the time cost for the algorithmic initialization, as it involves multiple control and readout iterations. The table in Extended Data Fig. 3b breaks down the time spent on control and readout. We see that the readout integration time tintegration dominates the time consumption. At B0 = 85 mT and T = 1 K, the full algorithmic initialization takes an average of around three iterations, which totals around 150 μs. Evaluating this in the context of different B0 and temperatures, we obtain the dependence shown in Extended Data Fig. 3c,d. At ultralow B0, for which a reduction in the control and readout fidelity is seen, Niteration decreases, possibly because the system deviates from the parity basis. Higher B0 provides a larger qubit energy, increasing the likelihood of obtaining a |↓↓ state after the load ramp and reducing Niteration. Similarly, Niteration also increases with higher temperatures. At B0 above 1 T, the onset of excited state-level crossings enhances spin randomization after the load ramp, and thus more Niteration is required. We expect that Niteration may be reduced by incorporating corrective control based on measurement9,68 to accelerate the polarization towards the target state.

    SPAM error analysis with repeated readout

    A more comprehensive SPAM error analysis uses machine learning of the increased statistics from multiple measurements. The experimental sequence consists of initialization followed by repeated parity readout that results in a series of binary measurement outcomes m1, m2, …, mn, where mi {evenparity = 0, oddparity = 1}. This initialization-(measurement)n sequence is performed 1,000 shots.

    A hidden Markov model (HMM) can describe this series of measurements formalism in which the true, but hidden, spin state s1, s2, …, sn follows the Markov chain and the measurement outcomes, mi, are probabilistically related to the underlying spin state. Three different tensors completely determine HMMs:

    1. 1.

      A start probability vector, Π, encoding the initializing probabilities in each spin state.

    2. 2.

      A transition probability matrix, A, encoding the probabilities of transiting between spin states during measurements.

    3. 3.

      A measurement probability matrix, Θ, encoding the probability of the measurement outcomes conditioned on the current hidden spin state.

    To find the likely HMM model for a given set of data, we perform expectation maximization in which we maximize the marginal likelihood, which is dependent on the marginalized hidden spin state, such that

    $$\begin{array}{l}L({\boldsymbol{\Pi }},{A},{\Theta }\,;{\bf{m}})\,:=\,p({\bf{m}}|{\boldsymbol{\Pi }},{A},{\Theta })\\ \,\,=\int \,p({\bf{s}},{\bf{m}}|{\boldsymbol{\Pi }},{A},{\Theta }){\rm{d}}{\bf{s}}.\end{array}$$

    (1)

    For HMM models, there exists the Baum–Welch algorithm that can perform this expectation maximization by an iterative update rule, without the need for backpropagation of gradients69.

    We use the Cramer–Rao bound to quantify the level of uncertainty in these parameters when fitted by expectation maximization70. The Cramer–Rao bound states that if \({{\rm{est}}}_{{\boldsymbol{\theta }}}({\bf{m}})\) is an unbiased estimate of the parameters \({\boldsymbol{\theta }}:=({\boldsymbol{\Pi }},{A},{\Theta })\) given the data m, such as that produced by expectation maximization, then

    $${{\rm{cov}}}_{{\boldsymbol{\theta }}}\left({{\rm{est}}}_{{\boldsymbol{\theta }}}({\bf{m}})\right)\ge I{\left({\boldsymbol{\theta }};{\bf{m}}\right)}^{-1},$$

    (2)

    where \(I{({\boldsymbol{\theta }};{\bf{y}})}_{ij}=-{\partial }^{2}\log L({\boldsymbol{\theta }}\,;{\bf{m}})/\partial {\theta }_{i}\partial {\theta }_{j}\), the Fisher information matrix. Therefore, we can obtain lower bounds on the uncertainty of each parameter from the diagonal elements of the inverse of the Fisher information matrix. We used the Forward–Backward algorithm to compute the marginal likelihood defined in equation (1) needed to compute the Fisher information matrix.

    Finally, we use the Viterbi algorithm to compute the most likely set of true spin states that gave rise to the set of measurements given a set of model parameters69,71.

    Crosstalk correction

    The relatively small ΔEZ even at higher B0 requires cancellation of crosstalk between the two qubits, that is, the effect on the other qubit when one qubit is being driven. This can be addressed to the first order by considering the following aspects.

    To cancel off-resonance driving, we enforce

    $$\sqrt{\Delta {E}_{{\rm{Z}}}^{2}+{f}_{{\rm{Rabi}}}^{2}}=N{f}_{{\rm{Rabi}}},$$

    (3)

    where fRabi is the Rabi frequency of the target qubit, and N = 4, 8, 12, …. Consequently, each π/2 microwave pulse on the target qubit incurs a full 2πN off-resonance rotation on the ancilla qubit, as exemplified in Extended Data Fig. 7a. Failure to cancel the off-resonance driving can result in large errors under parity readout, as shown in Extended Data Fig. 7b. With N = 4, this cancellation criterion dictates the fastest Rabi possible and is therefore expected to limit the single-qubit gate fidelities, especially at low B0 where ΔEZ is small. The full set of fRabi used for single-qubit randomized benchmarking at different B0 is shown in Extended Data Fig. 7c. In this case, we can alternatively execute X(π/2) as a 3π/2 gate for faster driving at the cost of redundancy. We implemented this with the three- and five-electron qubit at 0.1 T, 1.2 K in Fig. 3d.

    In two-qubit sequence runs, it is also necessary to correct AC Stark shift by an amount of

    $$\frac{{f}_{{\rm{Rabi}}}^{2}}{2\Delta {E}_{{\rm{Z}}}},$$

    (4)

    apart from cancelling the off-resonance driving. Extended Data Fig. 7d measures the AC Stark shift on an ancilla qubit by preparing it on the equator, driving it off-resonantly and projecting the phase. Before the correction, the AC Stark shift is seen as the linear fringes that correspond to the phase accumulation given by equation (4).

    We note that the above cancellation of crosstalk does not prevent it from incurring errors. The perturbation on the ancilla qubit induces decoherence. At ultra-low B0 at which ΔEZ becomes diminishing, higher-order crosstalk terms cannot be neglected, and the control of individual qubits becomes unmanageable. However, these problems are circumvented in the SMART control scheme, which addresses all the qubits simultaneously.

    Randomized benchmarking

    Single-qubit randomized benchmarking sequences for Fig. 3d–e are constructed from elementary π/2 gates [X(π/2), Z(π/2), −X(π/2), −Z(π/2)], π gates [X(π), Z(π)] and an I gate. Each Clifford gate contains one physical elementary gate on average, excluding the virtual Z(π/2) and Z(π) gates.

    To optimize the single-qubit gate fidelity, we study different B0 (Fig. 3d) and tightly confine the qubits with low barrier gate voltages to reduce noise coupling. In single-qubit randomized benchmarking, the coherent driving decouples the qubit from noise to a certain extent72, and the random rotations of the qubit also have the effect of refocusing38,73. Here we optimize the microwave power and thus fRabi, such that the spins are driven quickly without excessive microwave-induced noise46.

    Two-qubit randomized benchmarking sequences for Fig. 4b are constructed from single-qubit elementary π/2 gates [X1(π/2), Z1(π/2), X2(π/2), Z2(π/2)] for Q1 and Q2, and a two-qubit elementary gate DCZ. Each Clifford gate contains 1.8 single-qubit elementary gates and 1.5 two-qubit elementary gates on average. All gates are sequentially executed, which means Q1 idles while X2(π/2) or Z2(π/2) takes place, and the same for Q2. The generated random sequences are used in both randomized benchmarking and FBT. In the case of IRB, we incorporate an interleaved DCZ gate between adjacent Clifford gates. The experimental implementation and the analysis protocol are shown in Extended Data Fig. 9a,b, and the IRB results are shown in Extended Data Fig. 9c.

    We then fit the randomized benchmarking decay curve to the formula38,41

    $$a{{\rm{e}}}^{-{(bx)}^{c}}+d,$$

    (5)

    from which 1 − 0.5b gives the Clifford fidelity in single-qubit randomized benchmarking, and 1 − 0.75b gives the Clifford fidelity in two-qubit randomized benchmarking. The term c represents the decay exponent and reflects the error Markovianity; a is subjected to the readout fidelity and d is close to 0.5.

    From the two-qubit IRB decays, we first obtain an IRB fidelity50 of 99.8 ± 0.2% at T = 0.1 K and 97.7 ± 1.5% at T = 1 K for the DCZ gate. This fidelity reflects the combined effect of dephasing during texchange and echoing in the DCZ gate and the results can be understood from the stronger temperature dependence of \({T}_{2}^{{\rm{Hahn}}}\) compared with that of \({T}_{2}^{{\rm{* }}}\). We also note the numerical instabilities in IRB fidelities, which result in large error bars.

    Fast Bayesian tomography

    FBT53 is an agile gate set process tomography protocol that can self-consistently reconstruct all gate set process matrices based on previous calibration. In principle, FBT learns and updates the model using the gate sequence information and its experimental outcome. In this work, we feed FBT with the variable-length two-qubit randomized benchmarking sequences and the corresponding experimental data. Clifford gates in the randomized benchmarking sequences are decomposed into their elementary gate implementation of X1(π/2), Z1(π/2), X2(π/2), Z2(π/2) and DCZ. The randomized benchmarking experiments at T = 0.1 K and T = 1 K run through 32,000 and 26,000 sequences, respectively, sufficient for FBT to reliably reconstruct the error channels. We feed the native parity readout results directly to FBT, without converting them to the standard two-qubit measurement basis.

    To initiate the FBT analysis, we must bootstrap the model from educated guesses to help the analysis converge with a finite amount of experiments. Here, we do this by injecting guessed fidelity numbers as introduced in refs. 53,74. FBT models each noisy gate \(\widetilde{G}\) as the product of the noise channel \(\mathop{G}\limits^{ \sim }=\varLambda G\) and the ideal gate G, in which the noise channel Λ is linearized about I by expressing it as Λ = I + ε. Each update of the FBT analysis is essentially on the statistics of the noise channel residuals ε. Extended Data Fig. 9d shows the reconstructed Pauli transfer matrices of the DCZ gate. Supplementary Information shows the reconstructed noise channel residuals of the three physical elementary gates DCZ, X1(π/2), and X2(π/2) at T = 0.1 K and T = 1 K.

    As FBT does not guarantee that the reconstructed channels are physical or flag any gauge ambiguity, we perform CPTP projection and gauge optimization over the entire gate set at the output stage.

    FBT extracts DCZ fidelities of 99.15 ± 0.13% at T = 0.1 K and 98.92 ± 0.67% at T = 1 K. Here, a single-qubit gate on one qubit always leaves the other qubit idling, which considerably limits the single-qubit process fidelities (Supplementary Information) and consequently the Clifford fidelity in two-qubit randomized benchmarking, even at T = 0.1 K (Fig. 4b). However, the reduction in the Clifford fidelity from 0.1 K to 1 K mainly originates from the degradation of the DCZ gate, exhibiting a similar factor.

    Error taxonomy with pyGSTi

    When examining the fidelity results, we are also interested in understanding the dominant error sources behind the DCZ gate infidelity and their variation at different temperatures. FBT is a flexible and efficient gate set process tomography that enables us to extract gate errors from randomized sequence runs53,74. To categorize the gate errors, we perform post-processing of the tomography results obtained by FBT using tools for decomposing errors implemented in the pyGSTi package59,60.

    Error taxonomy for FBT can be achieved by converting the noise channels Λ for each gate to their error generator \({\mathbb{L}}\) using the following relationship:

    $$G=\varLambda {G}_{0}={{\rm{e}}}^{{\mathbb{L}}}{G}_{0},$$

    (6)

    where G is the estimated noisy gate, and G0 is the ideal gate.

    Using the pyGSTi package59,60, we project \({\mathbb{L}}\) into the subspace of Hamiltonian and stochastic errors, extracting the coefficients of each elementary error generator. We perform this analysis on each of the gates [DCZ, X1(π/2) and X2(π/2)] for both temperatures of 0.1 K and 1 K. The coefficients of the elementary error generators are represented in the Pauli basis and presented in the Supplementary Information. The five largest components of the Hamiltonian and stochastic errors for the DCZ gate are shown in Fig. 4c.

    We also estimate the generator or entanglement infidelity \(1-{{\mathcal{F}}}_{{\rm{ent}}}\) based on these error coefficients, given by60

    $$1-{{\mathcal{F}}}_{{\rm{ent}}}\approx \sum _{P}{s}_{P}+\sum _{P}{h}_{P}^{2},$$

    (7)

    where the sum is performed over the extracted coefficients and P denotes non-identity Pauli elements. The approximation is validated by the domination of Hamiltonian errors over stochastic errors in magnitude. To obtain the average gate fidelities (\({{\mathcal{F}}}_{{\rm{avg}}}\)), which are the quantities quoted based on IRB and FBT measurements, it can be connected to \({{\mathcal{F}}}_{{\rm{ent}}}\) in the following way75:

    $${{\mathcal{F}}}_{{\rm{avg}}}=\frac{d\cdot {{\mathcal{F}}}_{{\rm{ent}}}+1}{d+1},$$

    (8)

    where d is the dimension of the Hilbert space (4 for a two-qubit system). This means that generally stochastic errors contribute more to the gate infidelities, even in the case in which the magnitudes of the Hamiltonian errors are larger.

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    Code construction

    We begin with a formal definition of BB codes. Let I and S be the identity matrix and the cyclic shift matrix of size  ×  respectively. The ith row of S has a single non-zero entry equal to one at the column \(i\,+\,1\,({\rm{mod}}\,\,{\ell })\). For example,

    $${S}_{2}=\left[\begin{array}{cc}0 & 1\\ 1 & 0\end{array}\right]\quad {\rm{and}}\quad {S}_{3}=\left[\begin{array}{ccc}0 & 1 & 0\\ 0 & 0 & 1\\ 1 & 0 & 0\end{array}\right].$$

    Consider matrices

    $$x={S}_{{\ell }}\otimes {I}_{m}\quad {\rm{and}}\quad y={I}_{{\ell }}\otimes {S}_{m}.$$

    Note that xy = yx, xTx = yTy = Im, and x = ym = Im. A BB code is defined by a pair of matrices

    $$A={A}_{1}+{A}_{2}+{A}_{3}\quad {\rm{and}}\quad B={B}_{1}+{B}_{2}+{B}_{3}$$

    (1)

    where each matrix Ai and Bj is a power of x or y. Here and below the addition and multiplication of binary matrices is performed modulo two, unless stated otherwise. Thus, we also assume the Ai are distinct and the Bj are distinct to avoid cancellation of terms. For example, one could choose A = x3 + y + y2 and B = y3 + x + x2. Note that A and B have exactly three non-zero entries in each row and each column. Furthermore, AB = BA because xy = yx. The above data defines a BB quantum code denoted QC(A, B) with length n = 2m and check matrices

    $${H}^{X}=\left[A| B\right]\quad {\rm{and}}\quad {H}^{Z}=\left[{B}^{T}| {A}^{T}\right].$$

    (2)

    Here the vertical bar indicates stacking matrices horizontally and T stands for the matrix transposition. Both matrices HX and HZ have size (n/2) × n. Each row \({\bf{v}}\,\in \,{{\mathbb{F}}}_{2}^{n}\) of HX defines an X-type check operator \(X({\bf{v}})={\prod }_{j=1}^{n}{X}_{j}^{{{\bf{v}}}_{j}}\). Each row \({\bf{v}}\,\in \,{{\mathbb{F}}}_{2}^{n}\) of HZ defines a Z-type check operator \(Z({\bf{v}})={\prod }_{j=1}^{n}{Z}_{j}^{{{\bf{v}}}_{j}}\). Any X and Z checks commute as they overlap on even number of qubits (note that \({H}^{X}{({H}^{Z})}^{T}=AB+BA=0\,({\rm{mod}}\,\,2)\)). By construction, the code QC(A, B) has weight-6 check operators and each qubit participates in six checks (three X-type plus three Z-type checks). Accordingly, the code QC(A, B) has a degree-6 Tanner graph. One can view the matrices A and B as bivariate polynomials over the variables x and y. Specializing BB codes to the case m = 1 and B = AT gives the original bicycle codes41 based on univariate polynomials. Likewise, BB codes are a specialization of the generalized bicycle codes35, two-block group-based codes37,42 and polynomial-based codes59. Given a binary matrix M, let \(\ker (M)\) be its nullspace spanned by all binary vectors v such that \(M{\bf{v}}=0\,({\rm{mod}}\,\,2)\). Let rs(M) be the row space of M spanned by rows of M.

    Lemma 1

    The code QC(A, B) has parameters [[n, k, d]], where

    $$\begin{array}{c}n=2{\ell }m,\,k=2\times \dim (\ker (A)\cap \ker (B))\,{\rm{a}}{\rm{n}}{\rm{d}}\,d\\ \,\,=\,\min \{|{\bf{v}}|:{\bf{v}}\in \ker ({H}^{X}){\rm{\backslash }}{\mathsf{r}}{\mathsf{s}}({H}^{Z})\}.\end{array}$$

    The code offers equal distance for X-type and Z-type errors.

    The proof, relying on elementary linear algebra, is deferred to the Supplementary Information. Extended Data Table 1 describes the polynomials A and B that give rise to examples of high-rate, high-distance BB codes found by a numerical search. This includes all codes from Table 1 and two examples of higher distance codes. To the best of our knowledge, all these examples are new. The code [[360, 12, ≤24]] improves on a code [[882, 24, ≤24]] with weight-6 checks found by Panteleev and Kalachev in ref. 36 (assuming that our distance upper bound is tight). Indeed, taking two independent copies of the 360-qubit code gives parameters [[720, 24, ≤24]]. Appendix C in ref. 36 also describes a code [[126, 12, 10]] that has parameters similar to ours. This code has a form QC(A, B) with A = 1 + x43 + x37, B = 1 + x59 + x31,  = 63 and m = 1. We note that the recent work by Wang, Lin and Pryadko37,38 described examples of group-based codes closely related to the codes considered here. Some of the group-based codes with weight-8 checks found in ref. 37 outperform our BB codes with weight-6 checks in terms of the parameters n, k, d. It remains to be seen whether group-based codes can achieve a similar or better level of error suppression for the circuit-based noise model.

    In the following, we partition the set of data qubits as [n] = LR, where Lq(L) and Rq(R) are the left and right blocks of n/2 = m data qubits. Then, data qubits L and R and checks X and Z may each be labelled by integers \({{\mathbb{Z}}}_{{\ell }m}=\{0,1,\ldots ,{\ell }m-1\}\), which are indices into the matrices A, B. Alternatively, qubits and checks can be labelled by monomials from \({\mathcal{M}}=\{1,y,\ldots ,{y}^{m-1},x,xy,\ldots ,x{y}^{m-1},\ldots ,{x}^{{\ell }-1}{y}^{m-1}\}\) in this order, so that \(i\in {{\mathbb{Z}}}_{{\ell }m}\) labels the same qubit or check as \({x}^{{a}_{i}}{y}^{i-m{a}_{i}}\) for ai = floor(i/m). Using the monomial labelling, L data qubit \(\alpha \in {\mathcal{M}}\) is part of X checks \({A}_{i}^{T}\alpha \) and Z checks Biα for i = 1, 2, 3. Similarly, R data qubit \(\beta \in {\mathcal{M}}\) is part of X checks \({B}_{i}^{T}\beta \) and Z checks Aiβ. A unified notation assigns each qubit or check a label q(T, α) where T {L, R, X, Z} denotes its type and \(\alpha \in {\mathcal{M}}\) its monomial label. (The monomial notations should not be confused with the matrix notations used earlier in this section. For example, multiplication of monomials such as Biα is different from multiplying a vector α by a matrix Bi).

    One drawback of high-rate LDPC codes is that their Tanner graphs may not be locally embeddable into the 2D grid60,61. This poses a challenge for hardware implementation with superconducting qubits coupled by microwave resonators. A useful very-large-scale integration (VLSI) design concept is graph thickness, see refs. 29,62 for details. A graph G = (V, E) is said to have thickness θ if one can partition its set of edges E into disjoint union of θ sets E1E2Eθ = E such that each subgraph (V, Ei) is planar. Informally, a graph with thickness θ can be viewed as a vertical stack of θ planar graphs. Qubit connectivity described by a planar graph (thickness θ = 1) is the simplest one from hardware perspective because the couplers do not cross.

    Here we show that the Tanner graph of any BB code has thickness-2. This result may be surprising as it is known that a general degree-6 graph can have thickness θ = 3 (ref. 62). Graphs with thickness θ = 2 might still be implementable with superconducting qubits because two planar layers of couplers and their control lines can be attached to the top and the bottom side of the chip hosting qubits.

    Lemma 2

    The Tanner graph G of the code QC(A, B) has thickness θ ≤ 2. A decomposition of G into two planar layers can be computed in time O(n). Each planar layer of G is a degree-3 graph.

    Proof

    Let G = (V, E) be the Tanner graph. Partition G into subgraphs GA = (V, EA) and GB = (V, EB) that describe CSS codes with check matrices

    $$\,\mathrm{Tanner\; graph}\,\,{G}_{{\rm{A}}}:\quad {H}_{A}^{X}=[{A}_{2}+{A}_{3}| {B}_{3}]\quad {\rm{and}}\quad {H}_{A}^{Z}=[{B}_{3}^{T}| {A}_{2}^{T}+{A}_{3}^{T}]$$

    (3)

    $$\,\mathrm{Tanner\; graph}\,\,{G}_{{\rm{B}}}:\quad {H}_{B}^{X}=[{A}_{1}| {B}_{1}+{B}_{2}]\quad {\rm{and}}\quad {H}_{B}^{Z}=[{B}_{1}^{T}+{B}_{2}^{T}| {A}_{1}^{T}].$$

    (4)

    As A = A1 + A2 + A3 and B = B1 + B2 + B3, every edge of G appears either in GA or GB, in which the two subgraphs are named by whether they contain more Ai edges or more Bi edges. Then GA and GB are regular degree-3 graphs (because Ai and Bj are permutation matrices).

    Consider the graph GA. Each X-check vertex is connected to a pair of data vertices i1, i2L by means of the matrices A2, A3 and a data vertex i3R by means of the matrix B3. Each Z-check vertex is connected to a pair of data vertices i1, i2R by means of the matrices \({A}_{2}^{T},{A}_{3}^{T}\) and a data vertex i3L by means of the matrix \({B}_{3}^{T}\).

    We claim that each connected component of GA can be represented by a ‘wheel graph’ illustrated in Extended Data Fig. 1. A wheel graph consists of two disjoint cycles of the same length p interconnected by p radial edges. The outer cycle alternates between X-check and L data vertices.

    Edges of the outer cycle alternate between those generated by A3 (as one moves from a check to a data vertex) and \({A}_{2}^{T}\) (as one moves from a data to a check vertex). The length of the outer cycle is equal to the order of the matrix \({A}_{3}{A}_{2}^{T}\), that is, the smallest integer Ord such that \({({A}_{3}{A}_{2}^{T})}^{{\rm{O}}{\rm{r}}{\rm{d}}}={I}_{{\ell }m}\). For example, consider the code [[144, 12, 12]] from Extended Data Table 1. Then A = x3 + y + y2, A2 = y and A3 = y2. Thus \({A}_{3}{A}_{2}^{T}={y}^{2}{y}^{-1}=y\) that has order m = 6. The inner cycle of a wheel graph alternates between Z-check and R-data vertices.

    Edges of the inner cycle alternate between those generated by \({A}_{3}^{T}\) (as one moves from a check to a data vertex) and A2 (as one moves from a data to a check vertex). The length of the inner cycle is equal to the order of the matrix \({A}_{3}^{T}{A}_{2}\) that is the transpose of \({A}_{3}{A}_{2}^{T}\) considered earlier. Thus both inner and outer cycles have the same length m. The two cycles are interconnected by m radial edges as shown in Extended Data Fig. 1a. Radial edges are generated by the matrix B3, as one moves towards the centre of the wheel. The wheel graph contains four cycles generated by tuples of edges \(({B}_{3},{A}_{2},{B}_{3}^{T},{A}_{2}^{T})\) and \(({B}_{3}^{T},{A}_{3},{B}_{3},{A}_{3}^{T})\). Commutativity between Ai and Bj ensures that traversing any of these four cycles implements the identity matrix, that is, the graph is well defined. Clearly, the wheel graph is planar. As GA is a disjoint union of wheel graphs, GA is planar. The same argument shows that GB is planar (Extended Data Fig. 1b). The visualization of the [[144, 12, 12]] code in Fig. 1b shows the edges of GA and GB as dashed ‘A’ edges and solid ‘B’ edges, respectively.

    We leave optimization of the code layout satisfying specific hardware constraints for future work. For now, it is sufficient to note that any planar graph admits a planar embedding without edge crossings for any prescribed vertex locations, see for example theorem 1 in ref. 63. Moreover, this embedding can be efficiently computed63. Accordingly, both planar layers in the thickness-2 decomposition of the Tanner graph can be simultaneously embedded into a plane for any fixed vertex locations such that edges do not cross within each layer.

    Another example of thickness-2 graphs in the literature is the bilayer architecture of ref. 64. This connectivity is described by two planar graphs with additional transversal edges between them. It can be verified that bilayer graphs are thickness-2 by placing transversally connected nodes next to each other in one of the two planes and placing the transversal edges in that same plane.

    The definition of code QC(A, B) does not guarantee that its Tanner graph is connected. Some choices of A and B lead to a code that is actually several separable code blocks. This manifests as a Tanner graph with several connected components. For instance, although all codes in Extended Data Table 1 are connected, taking any of them with even and replacing every instance of x with x2 creates a code with two connected components.

    Lemma 3

    The Tanner graph of the code QC(A, B) is connected if and only if \(S=\{{A}_{i}{A}_{j}^{T}:i,j\in \{1,2,3\}\}\cup \{{B}_{i}{B}_{j}^{T}:i,j\in \{1,2,3\}\}\) generates the group \({\mathcal{M}}\). The number of connected components in the Tanner graph is m/S, and all components are graph isomorphic to one another.

    Proof

    Extended Data Fig. 2 is helpful for following the arguments in this proof. We start by proving the reverse implication of the first statement. Note that there is a length 2 path in the Tanner graph from L qubit \(\alpha \in {\mathcal{M}}\) to L qubit \({A}_{i}{A}_{j}^{T}\alpha \) and another length 2 path to L qubit \({B}_{i}{B}_{j}^{T}\alpha \). These travel through X and Z checks, respectively. Thus, because the \({A}_{i}{A}_{j}^{T}\) and \({B}_{i}{B}_{j}^{T}\) generate \({\mathcal{M}}\), there is some path from α to any other L qubit β. A similar argument shows existence of a path connecting any pair of R qubits. As each X check and each Z check are connected to at least one L qubit and at least one R qubit, this implies that the entire Tanner graph is connected. The forward implication of the first statement follows after noticing that, for all T {L, R, X, Z}, the path from a type T node to any other T node is necessarily described as a product of elements from S. Connectivity of the Tanner graph implies the existence of all such paths, and so S must generate \({\mathcal{M}}\).

    If S does not generate \({\mathcal{M}}\), it necessarily generates a subgroup S and nodes in connected components of the Tanner graph are labelled by elements of the cosets of this subgroup. This implies the theorem’s second statement.

    For the next part, we establish some terminology. A spanning subgraph of a graph G is a subgraph containing all the vertices of G. Also, the undirected Cayley graph of a finite Abelian group \({\mathcal{G}}\) (with identity element 0) generated by set \(S\subset {\mathcal{G}}\) is the graph with vertex set \({\mathcal{G}}\) and undirected edges (g, g + s) for all \(g\in {\mathcal{G}}\) and all sS, s ≠ 0. We say the Cayley graph of \({{\mathbb{Z}}}_{a}\times {{\mathbb{Z}}}_{b}\) when we mean the Cayley graph of \({{\mathbb{Z}}}_{a}\times {{\mathbb{Z}}}_{b}\) generated by {(1, 0), (0, 1)}. The order ord(g) of an element g in a multiplicative group is the smallest positive integer such that gord(g) = 1.

    Definition 1

    Code QC(A, B) is said to have a toric layout if its Tanner graph has a spanning subgraph isomorphic to the Cayley graph of \({{\mathbb{Z}}}_{2\mu }\times {{\mathbb{Z}}}_{2\lambda }\) for some integers μ and λ.

    Note that only codes with connected Tanner graphs can have a toric layout according to this definition. An example toric layout is depicted in Fig. 1b.

    Lemma 4

    A code QC(A, B) has a toric layout if there exist i, j, g, h {1, 2, 3} such that

    1. 1.

      \(\langle {A}_{i}{A}_{j}^{T}\,,{B}_{g}{B}_{h}^{T}\rangle ={\mathcal{M}}\) and

    2. 2.

      \({\rm{ord}}({A}_{i}{A}_{j}^{T}\,){\rm{ord}}({B}_{g}{B}_{h}^{T})={\ell }m\).

    Proof

    We let \(\mu ={\rm{ord}}({A}_{i}{A}_{j}^{T}\,)\) and \(\lambda ={\rm{ord}}({B}_{g}{B}_{h}^{T})\). We associate qubits and checks in the Tanner graph of QC(A, B) with elements of \({\mathcal{G}}={{\mathbb{Z}}}_{2\mu }\times {{\mathbb{Z}}}_{2\lambda }\). For L qubit with label \(\alpha \in {\mathcal{M}}\), because of (1), there is \((a,b)\in {{\mathbb{Z}}}_{\mu }\times {{\mathbb{Z}}}_{\lambda }\) such that \(\alpha ={({A}_{i}{A}_{j}^{T})}^{a}{({B}_{g}{B}_{h}^{T})}^{b}\). Because of (2) and the pigeonhole principle, this choice of (a, b) is unique. We associate L qubit α with \((2a,2b)\in {\mathcal{G}}\). Similarly, an R qubit with label \(\alpha {A}_{j}^{T}{B}_{g}\) is associated with \((2a+1,2b+1)\in {\mathcal{G}}\), X-check \(\alpha {A}_{j}^{T}\) with (2a + 1, 2b) and Z-check αBg with (2a, 2b + 1). Edges in the Tanner graph \({A}_{i}\,,{A}_{j}^{T}\,,{B}_{g}\) and \({B}_{h}^{T}\) can now be drawn as in Extended Data Fig. 2b and correspond to edges in the Cayley graph of \({\mathcal{G}}\). For instance, to get from (2a + 1, 2b + 1), an R qubit, to (2a + 2, 2b + 1), a Z check, we apply Ai, taking R qubit labelled \(\alpha {A}_{j}^{T}{B}_{g}\) to the Z check labelled \((\alpha {A}_{j}^{T}{B}_{g}){A}_{i}=\alpha ({A}_{i}{A}_{j}^{T}\,){B}_{g}\).

    All codes in Extended Data Table 1 have a toric layout with μ = m and λ = . Most of these codes satisfy Lemma 4 with i = g = 2 and j = h = 3. The exception is the [[90, 8, 10]] code, for which we can take i = 2, g = 1 and j = h = 3.

    However, we also note two interesting cases. First, there are codes with connected Tanner graphs that do not satisfy the conditions for a toric layout given in Lemma 4. One example of such a code is QC(A, B) with , m = 28, 14, A = x26 + y6 + y8 and B = y7 + x9 + x20 that has parameters [[784, 24, ≤24]]. Second, for a code satisfying the conditions of Lemma 4, it need not be the case that the set \(\{{\rm{ord}}({A}_{i}{A}_{j}^{T}\,),{\rm{ord}}({B}_{g}{B}_{h}^{T})\}\) and the set {, m} are equal. For example, the [[432, 4, ≤22]] code with , m = 18, 12 and A = x + y11 + y3, B = y2 + x15 + x only satisfies Lemma 4 with μ, λ = 36, 6 (take i = g = 1 and j = h = 2 for instance).

    Summary of other capabilities

    For the remainder of this section, we summarize important details of additional capabilities of BB LDPC codes. For more details on these topics, see the Supplementary Information.

    Syndrome circuit

    Our syndrome measurement circuit relies on 2n physical qubits, comprising of n data qubits and n ancillary check qubits used to record the measured syndromes. It repeatedly measures the syndrome of each check operator. The single syndrome cycle is illustrated in Fig. 2. The entire syndrome measurement circuit was composed to simultaneously minimize the number of gates used, optimize depth (including parallelizing register measurement with state initialization and with gate application, whenever possible), limit the propagation of errors and comply with the qubit-to-qubit connectivity layout offered by the Tanner graph. We refer the interested reader to Supplementary Information for the complete circuit description and proof of its correctness.

    We used a computer search to find a total of 936 low-depth syndrome measurement circuit alternatives. For the [[144, 12, 12]] code, the circuit shown in Fig. 2 achieves circuit distance of less than or equal to ten and we conjecture it equals ten. This syndrome measurement circuit was used to compile the data for all codes reported in Fig. 3, leaving the possibility that tailoring each of the 936 alternatives to specific codes would yield better results.

    Decoder

    We adapt the BP-OSD36,65,66 to the circuit noise model. This involves both an offline and online stage. In the offline stage, we take as input the syndrome measurement circuit and the error rate p. For every distinct single fault, we simulate the circuit efficiently using the stabilizer formalism, tracking the probability of the fault, the syndrome observed, and a final ideal syndrome. We also record in each case the logical syndrome, which indicates the logical operators anticommuting with the final error. In the online stage, we take a syndrome instance and determine a likely set of faults that occurred. Using the results of the offline stage, we can formulate this as an optimization problem, which is solved heuristically by BP-OSD.

    We also leverage BP-OSD to perform two additional useful tasks that can be framed as appropriate optimization problems. First, given a code, we can find an upper bound on the code distance. Second, given a code and a syndrome measurement circuit, we can determine an upper bound on the circuit distance.

    Logical memory capabilities

    As depicted in Fig. 1c a BB LDPC code can be used as a data storage unit for, for example, a small surface code quantum computer. To this end we demonstrate two capabilities: joint logical XX measurements between a surface code qubit and any qubit within the BB code, and logical Z measurements on any qubit in the BB code. These measurements facilitate quantum teleportation circuits implementing load-store operations, transporting qubits into and out of the BB code.

    These measurements are facilitated by the combination of two techniques. A construction based on ref. 50 enables fault-tolerant logical measurement of one logical X and one logical Z operator. The main idea, as illustrated in Fig. 1c, is to extend the Tanner graph of the BB code to a larger code that features the desired logical operators as stabilizers. We show that this extended Tanner graph is compatible with a thickness-2 architecture while simultaneously connecting the logical X extension to an ancillary surface code.

    To extend the reach of these logical measurements beyond a single X and Z operator, we leverage techniques from ref. 67 to derive several fault-tolerant unitary operations. These operations achieve measurement of X and Z for all qubits by acting on the original measurement by conjugation, and have fault-tolerant circuit implementations within the existing connectivity of the Tanner graph.

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