Abstract
Future largescale quantum computers will rely on quantum error correction (QEC) to protect the fragile quantum information during computation^{1,2}. Among the possible candidate platforms for realizing quantum computing devices, the compatibility with mature nanofabrication technologies of siliconbased spin qubits offers promise to overcome the challenges in scaling up device sizes from the prototypes of today to largescale computers^{3,4,5}. Recent advances in siliconbased qubits have enabled the implementations of highquality onequbit and twoqubit systems^{6,7,8}. However, the demonstration of QEC, which requires three or more coupled qubits^{1}, and involves a threequbit gate^{9,10,11} or measurementbased feedback, remains an open challenge. Here we demonstrate a threequbit phasecorrecting code in silicon, in which an encoded threequbit state is protected against any phaseflip error on one of the three qubits. The correction to this encoded state is performed by a threequbit conditional rotation, which we implement by an efficient singlestep resonantly driven iToffoli gate. As expected, the error correction mitigates the errors owing to onequbit phaseflip, as well as the intrinsic dephasing mainly owing to quasistatic phase noise. These results show successful implementation of QEC and the potential of a siliconbased platform for largescale quantum computing.
Main
Quantum computing takes advantage of quantum superposition and entanglement to accelerate the computational tasks^{12,13}. However, these quantum properties are sensitive to decoherence errors owing to energy relaxation and dephasing. As the number of qubits increases and/or the computational tasks become more complex, the errors cause exponential reduction of the accuracy of computational results. QEC is a protocol to circumvent this problem by distributing the quantum information across a larger multiqubit entangled state so that the errors can be detected and corrected^{14}. Its basic concept has been demonstrated in various platforms, such as nuclear magnetic resonance^{9,15}, trapped ions^{10,16}, nitrogen vacancy centres^{17} and superconducting circuits^{11,18,19}, and has served as an important benchmark of the qubit systems. Siliconbased spin qubits have emerged as a qubit platform in the past decade, and there has been rapid progress in long coherence times^{20,21}, highfidelity universal quantum gates^{6,7,8}, hightemperature operation^{22,23} and generation of threequbit entanglement^{24,25}.
Our threequbit system (Fig. 1a) comprises one data qubit (Q_{2}) to be corrected and two ancilla qubits (Q_{1} and Q_{3}). The sequence starts from encoding the data qubit state to a threequbit entangled state. Then the phaseflip errors that occurred in the encoded state are mapped to the ancilla qubit states by the decoding. The original data qubit state can finally be restored by a correcting logic gate based on the ancilla qubit states. Most commonly, this correction can be performed by a projective measurement of ancilla qubits followed by a feedback quantum gate on the data qubit. However, this requires a capability to perform highfidelity qubit measurement much faster than the coherence time, which is still challenging with spins in silicon. Although this measurementbased operation is a key component for fault tolerance, in the case of threequbit QEC, it can alternatively be performed by a multiqubit conditional qubit rotation. In this Article, we take this approach by using a threequbit iToffoli gate, which coherently rotates the data qubit conditioned on the ancilla spin polarization. We synthesize a threequbit phaseflip code and demonstrate that onequbit phaseflip error can be corrected and the intrinsic ensemble spin dephasing can be mitigated.
Our sample is a gatedefined triple quantum dot in an isotopically natural silicon/silicongermanium (Si/SiGe) heterostructure. Three layers of overlapping aluminium gates^{26} are used to control the tripledot confinement. A micromagnet is fabricated on top of the aluminium gates to provide a local magnetic field gradient^{27}. As schematically shown in Fig. 1b, we configure the gate voltages so that only one electron is confined under each of the plunger gates (P1, P2 and P3) and the interdot tunnel coupling is controlled by the barrier gates (B2 and B3). Measurement of the tripledot charge configuration is performed by monitoring the conductance of the nearby charge sensor quantum dot using the radiofrequency reflectometry technique^{28,29}. An inplane external magnetic field of B_{ext} = 0.607 T is applied using a superconducting magnet. We use the Zeemansplit spin1/2 states of the single electrons as our spin qubits (labelled Q_{1}, Q_{2} and Q_{3} in Fig. 1b,c). The Zeeman energy splitting (about 20 GHz) much larger than the thermal excitation energy (about 0.8 GHz or 40 mK) enables initialization and readout of the threespin state by the combination of energyselective tunnelling^{30}, shuttling^{31} and controlled rotation (see Methods and Extended Data Fig. 1 for the full details of the sequence).
The singlequbit rotations are performed by applying resonant microwave pulses (see Methods and Extended Data Fig. 2). The microwave pulse displaces the quantum dot position, effectively creating an oscillating transverse magnetic field that induces electricdipole spin resonance^{27}. The twoqubit controlled phase (CZ) gate is implemented by adiabatically pulsing the exchange couplings J_{12} and J_{23} by the barrier gates B2 and B3, respectively (see Methods and Extended Data Fig. 3). To operate the qubit close to the chargesymmetry point, the effect of capacitive crosstalk between the plunger and barrier gates is suppressed by the virtual gate technique (see Methods). The spin qubits herein have an average T_{1} relaxation time of 22 ms, inhomogeneous dephasing time \({T}_{2}^{* }\) of 1.8 μs and Hahn echo dephasing time \({T}_{2}^{{\rm{H}}}\) of 43 μs (Extended Data Fig. 4). Because electron spins have orders of magnitude longer T_{1} times compared with the dephasing times \({T}_{2}^{* }\) and \({T}_{2}^{{\rm{H}}}\), we focus on the implementation of a phaseflip correction code in this work, whereas a bitflip correction code can easily be assembled by introducing further singlequbit rotations.
First we demonstrate the ability to encode and decode the data qubit state. For simplicity, here we perform encoding of an input state on the equator of the Bloch sphere, \({{\rm{Q}}}_{2}=( \downarrow \rangle +{{\rm{e}}}^{{\rm{i}}\varphi } \uparrow \rangle )/\sqrt{2}\) (Fig. 2a, ϕ is an azimuthal angle), which is encoded to a maximally entangled threequbit Greenberger–Horne–Zeilinger (GHZ) state \( {{\rm{GHZ}}}_{\varphi }\rangle \,=\) \(( \downarrow \downarrow \downarrow \rangle +{{\rm{e}}}^{{\rm{i}}\varphi } \uparrow \uparrow \uparrow \rangle )/\sqrt{2}\). The controlled NOT (CNOT) gates used in the encoding are decomposed to native CZ gates combined with the decoupling pulses to mitigate the local quasistatic phase noise. For the QEC implementation, a crucial property is that the encoded state is a genuine threequbit GHZclass state. We confirm this by characterizing the generated state using threequbit quantum state tomography (Methods). In Fig. 2b (2c), the real part of the measured experimental density matrix ρ for ϕ = 0 (π) is plotted. We evaluate the state fidelities \(F=\left\langle {{\rm{GHZ}}}_{\varphi } \rho  {{\rm{GHZ}}}_{\varphi }\right\rangle \) for various ϕ (Fig. 2d) and confirm that all the states have fidelities above 0.75, the threshold to witness genuine GHZclass states.
For correcting the decoded state, we implement a Toffoliclass threequbit gate. The standard threequbit Toffoli gate can be synthesized from 12 CNOT and two singlequbit gates^{32,33} (excluding T gates that can be implemented in software), albeit that decoherence in our device does not allow this implementation with a reasonable fidelity. Alternatively, we use a singlestep, resonantly driven iToffoli gate implemented by a resonant π pulse in the presence of simultaneous nearestneighbour exchange couplings (Fig. 2e). Without the exchange couplings (left side of Fig. 2e), the four transitions associated with the Q_{2} rotation are degenerate with a resonance frequency of f_{0}. The finite exchange couplings shift downward the energy levels of the antiparallel spin configurations. As a result, the resonance frequency of Q_{2} is modulated as f_{0} + s_{1}J_{12} + s_{3}J_{23}, in which s_{i} = ±1/2 is the spin number of Q_{i}. Under the condition of J_{12} = J_{23} required for conditional phase synchronization (see Methods), a rotation of Q_{2} with \({{\rm{Q}}}_{1}{{\rm{Q}}}_{3}= \downarrow \downarrow \rangle \;{\rm{or}}\, \uparrow \uparrow \rangle \) corresponds to a controlledcontrolledrotation.
Figure 2f shows the spectra of Q_{2} with four different ancilla qubit states \({{\rm{Q}}}_{1}{{\rm{Q}}}_{3}= \downarrow \downarrow \rangle , \downarrow \uparrow \rangle , \uparrow \downarrow \rangle \;{\rm{and}}\; \uparrow \uparrow \rangle \) at J_{12} = J_{23} = 4.5 MHz, in which we observe the peak positions as expected from the exchange couplings. We use a resonant π pulse at \({f}_{{\rm{MW}}}={f}_{1}({{\rm{Q}}}_{1}{{\rm{Q}}}_{3}= \downarrow \downarrow \rangle )\) to implement our iToffoli gate, as this transition yields the highest visibility^{34}. The iToffoli gate is a threequbit gate equivalent to a Toffoli gate with an extra phase factor of i on the ancilla qubits. To characterize its property, we prepare the eight possible threespin eigenstates, apply the iToffoli gate and perform threespin projective measurement (Fig. 2g,h). The readout errors are removed from the data based on the measured readout fidelities (see Methods). The Rabi frequency is chosen so that the offresonant rotations for the \({{\rm{Q}}}_{1}{{\rm{Q}}}_{3}= \downarrow \uparrow \rangle / \uparrow \downarrow \rangle \) subspaces are synchronized (see Methods). In Fig. 2h, as expected, the populations of \(\downarrow \downarrow \downarrow \rangle \) and \(\downarrow \uparrow \downarrow \rangle \) states are swapped, whereas the other states are essentially unaffected. From this result, we obtain a population transfer fidelity of our iToffoli gate as Tr(U_{expt}U_{ideal})/8 = 0.96, in which U_{expt} (U_{ideal}) represents the experimental (ideal) gate action on the eigenstates (see Methods and Extended Data Fig. 5e–g for the result of the full quantum process tomography). In addition, we perform a calibration of the pulse duration and timing to eliminate unwanted phase accumulation on Q_{2} (see Methods). Note that the dephasing and phase accumulation on the ancilla qubits do not affect the error correction outcome.
We then turn to the implementation of the phaseflip correcting code. Figure 3a shows the quantum circuit diagram. The threequbit operation U serves to encode the data qubit state \(\psi \rangle \) to the threequbit entangled state. The exact implementation of U is shown in the bottom half of the figure, and it is equivalent to the two CNOT gates shown in Fig. 2a, except for the singlequbit gates that do not affect the function of the QEC. Here the data qubit state \(\psi \rangle =\alpha \downarrow \rangle +\beta \uparrow \rangle \) is encoded to a phasesensitive threequbit state \(\alpha +++\rangle +\beta \rangle \), in which \(\pm \rangle =(\downarrow \rangle \,\pm \,\uparrow \rangle )/\sqrt{2}\) are the eigenstates of the Pauli X operator. For a phaseflip error with a flip rate of p on Q_{2}, the decoded state is \(\sqrt{1p}\downarrow \rangle (\alpha \downarrow \rangle +\beta \uparrow \rangle )\downarrow \rangle +\sqrt{p}\uparrow \rangle (\beta \downarrow \rangle +\alpha \uparrow \rangle )\uparrow \rangle \) (see Extended Data Table 1 for the cases with an error on ancilla). The correcting procedure is implemented so that Q_{2} is flipped only when \({{\rm{Q}}}_{1}{{\rm{Q}}}_{3}= \uparrow \uparrow \rangle \) by applying π pulses on the ancilla qubits followed by the iToffoli gate, resulting in a product state of \({{\rm{Q}}}_{2}=\alpha  \downarrow \rangle +\beta  \uparrow \rangle \) and \({{\rm{Q}}}_{1}{{\rm{Q}}}_{3}=\sqrt{1p} \uparrow \uparrow \rangle \,+\) \({\rm{i}}\sqrt{p}\downarrow \downarrow \rangle \). Now the data qubit state is the same as the input state regardless of p. This is demonstrated in Fig. 3b, in which we estimate the process fidelity of the data qubit for various intentional onequbit errors (see Methods for details of the quantum process tomography). We implement the onequbit error as a phase rotation with a known rotation angle θ, which is equivalent to a phaseflip error with p = sin^{2}(θ/2). Therefore, without the correction, the process fidelity oscillates as a function of θ, shown as the black points. With the correction, the oscillation vanishes, and it confirms the basic function of the phaseflip correcting code (corrected Q_{i} error means that a phaseflip error is applied to only Q_{i} and the correction is performed). When there is no error (θ = 0), the process fidelity slightly decreases after the correction. This can be attributed to the infidelity of the iToffoli gate projected to the data qubit subspace. Furthermore, we show that the state of ancilla qubits reflects the error on the encoded qubit state (error detection). We measure the joint probability of the ancilla qubits Q_{1} and Q_{3} for the four possible cases with no error or a full π flip error. We observe that the measured ancilla states correctly reflect the error occurring on the encoded threequbit state (Fig. 3c).
Errors in actual quantum computers probably occur on all qubits simultaneously rather than on only one of the qubits. We verify the performance of our error correcting code in such a case in which all errors have the same effective error rate of p as per the common assumption in QEC^{14} (Fig. 4a). Without the correction, the data qubit process fidelity linearly decreases as p is increased. When the error correction is applied, errors on two and three qubits remain uncorrected, resulting in a process fidelity insensitive to p up to the first order, F(p) = 1 − 3p^{2} + 2p^{3} (ref. ^{14}) (see Fig. 4b inset). The quadratic dependence on p is a crucial property of QEC and it ideally results in an improvement of the fidelity for p < 0.5. We confirm this crucial property in Fig. 4b, in which the measured process fidelity with the correction is plotted as the cyan curve. A quadratic function fits well to the data (see Extended Data Fig. 6 for a comparison between different fitting models). If we allow the firstorder term in the fit, it is 0.0 ± 0.1 (the error is 1σ), representing a marked reduction of the firstorder sensitivity as compared with the uncorrected case. As for the fidelity enhancement, the corrected qubit shows improvements in the range p < 0.429 ± 0.003 (the threshold is obtained by comparing the two fitted curves in Fig. 4b, the error is 1σ). Although the corrected fidelities are always lower than those of the ideal uncorrected qubit in the present experiment (dashed grey line in Fig. 4b), improvement of the coherence times and thereby the fidelity of the iToffoli gate, which primarily limits the performance in the corrected case, would ameliorate the situation. In silicon spin qubits, the intrinsic phase error is more like a quasistatic phase shift rather than a sudden phase flip. In our device, the phase shift is mainly caused by the fluctuating spins of surrounding ^{29}Si nuclei. To demonstrate the effectiveness of our error correcting code to this type of phase error, we measure the dephasing of the encoded threequbit state (Fig. 4c,d). As predicted from the ability to correct small phase errors in Fig. 4b, the initial slope of the fidelity decay is suppressed as compared with that of an uncorrected encoded qubit. Overall, these results show a successful implementation of threequbit phasecorrecting code in silicon.
In conclusion, we have demonstrated the generation of the various threequbit entangled states, the effective singlestep resonantly driven iToffoli gate and the fundamental properties of threequbit QEC in silicon. Extending the experiment to a larger scale would require a more flexible feedbackbased correcting rotation. This would be limited by the slow spin measurement and initialization by energyselective tunnelling, which also pose a challenge to complete the error correction (or detection) before the phase coherence is completely lost. Substantial improvements should be possible by switching to the singlettriplet readout, in which highfidelity spin measurements in a few μs (refs. ^{35,36}), orders of magnitude shorter than the phase coherence time with dynamical decoupling^{21}, are routinely achieved. Along with the recent advances in scalable device design^{37}, electronics^{38} and gate fidelities^{6,7,8}, we anticipate that it will become possible to demonstrate more sophisticated quantum error correcting codes in a largescale siliconbased quantum processor.
Methods
Quantum dot device
The triplequantumdot device is identical to the one characterized in ref. ^{24}. The device is fabricated using an isotopically natural, undoped Si/SiGe heterostructure. The ohmic contacts are made by phosphorus ion implantation. Standard electronbeam lithography and liftoff techniques are used to fabricate the overlapping aluminium gates and the micromagnet.
Experimental setup
The GHZ state tomography and the iToffoli gate characterization (Fig. 2) are performed using the experimental setup as described in ref. ^{24}. In what follows, we detail the modified experimental setup used for the QEC experiments in Figs. 3 and 4. The sample is cooled down in a dry dilution refrigerator (Oxford Instruments Triton 300) to a base electron temperature of around 40 mK. The configuration of d.c. lines is the same as in the previous report^{24}. Control pulses are generated by four Keysight M3201A arbitrary waveform generator modules in a Keysight M9019A PXIe chassis (16 channels running at 500 MSa s^{−1}). The plunger (P1, P2 and P3), barrier (B2 and B3) and sensor plunger gates are connected to the outputs of the arbitrary waveform generator, each of which is filtered by a Mini Circuits SBLP39+ Bessel lowpass filter. The filtering results in a minimum pulse rise/fall time of approximately 15 ns. Microwave signals are generated by three vector microwave signal generators (two Keysight E8267D and a Rohde & Schwarz SGS100A with an SGU100A upconverter). Each microwave signal is single sideband I/Q modulated to prevent unintentional spin rotations owing to microwave carrier leakage. Furthermore, we use pulse modulation to further suppress the bleedthrough signal during the initialization and readout stages. The outputs of the three signal generators are combined at room temperature and connected to the lower screening gate. Radiofrequency reflectometry is used for fast measurement of the charge sensor conductance. The right reservoir of the charge sensor quantum dot in Fig. 1b is connected to a tank circuit with an inductance of 1.2 μH and a resonance frequency of 181 MHz. The reflected signal is amplified and demodulated, then digitized using an AlazarTech ATS9440 digitizer card.
Threespin initialization and measurement
The threespin initialization and measurement are performed as follows. The numbers (n_{1}n_{2}n_{3}) indicate the respective number of electrons in the left, centre and right quantum dots. We collect 400 to 3,000 singleshot outcomes to obtain the measured probabilities. The labels A–E represent the gate voltage configurations depicted in Extended Data Fig. 1c.

1.
Unload electrons in the left and centre quantum dots by biasing gate voltages so that the ground state charge configuration is (001) (A). The duration is 100 μs.

2.
Initialize Q_{1} by means of spinselective tunnelling by biasing the voltages so that the charge configuration is near the (101)–(001) boundary (B). The duration is 750 μs.

3.
Shuttle the electron in the left quantum dot to the centre quantum dot by biasing the voltages so that the ground charge configuration is (011) (C). No intentional gate voltage ramp is used. The typical pulse rise time is 15 ns owing to the lowpass filter. We wait for 1 μs in the (011) configuration.

4.
Initialize Q_{1} by means of spinselective tunnelling by biasing the voltages so that the charge configuration is near the (011)–(111) boundary (D). The duration is 750 μs.

5.
Initialize Q_{3} by means of spinselective tunnelling by biasing the voltages so that the charge configuration is near the (110)–(111) boundary (E). The duration is 750 μs.

6.
Qubit manipulation in the (111) configuration (F). The typical duration is 5 μs. There is an extra waiting time of 50 μs to reduce the effect of heating by the microwave pulses.

7.
Read out Q_{1} by means of spinselective tunnelling by biasing the voltages so that the charge configuration is near the (011)–(111) boundary (D). The total duration is 600 μs. The data for readout is collected for the first 200 μs. The extra waiting time of 400μs duration facilitates the initialization of Q_{1}.

8.
Perform controlled rotation between Q_{1} and Q_{2} to project the Q_{2} state to Q_{1} in (111). Here we pulse the virtual B2 gate to turn on J_{12} at the chargesymmetry point. Because Q_{1} is initialized to a spindown state during the previous readout stage, for a Q_{2} input state \({\rm{\alpha }}\uparrow \rangle +\beta \downarrow \rangle \), the resulting Q_{1}Q_{2} state is \(\alpha \uparrow \downarrow \rangle +{{\rm{e}}}^{{\rm{i}}\theta }\beta \downarrow \uparrow \rangle \), in which \({{\rm{e}}}^{{\rm{i}}\theta }\) is a phase factor that does not affect the readout. The duration is 1 μs. There is an extra waiting time of 50 μs to reduce the effect of heating by the microwave pulse.

9.
Read out Q_{2} by means of spinselective tunnelling of Q_{1} by biasing the voltages so that the charge configuration is near the (011)–(111) boundary (D). The duration is 200 μs.

10.
Read out Q_{3} by means of spinselective tunnelling by biasing the voltages so that the charge configuration is near the (110)–(111) boundary (E). The duration is 500 μs.
Virtual gate
The effect of capacitive couplings between the gates is suppressed by the virtual gate technique. We measure the capacitive couplings between the gates and construct the virtual gate as follows. The crosstalk between the exchange couplings is not taken into account. The virtual gate voltages vB2 and vB3 are used to control the exchange couplings.
Singlequbit and twoqubit gates
The singlequbit rotations about the x and y axes are performed by applying microwave voltage pulses resonant with the Zeeman splitting of each spin qubit. The microwave voltage results in an effectiveoutofplane a.c. magnetic field by the micromagnet, which induces electricdipole spin resonance. The spin qubits have typical resonance frequencies of 19,942.6 MHz (Q_{1}), 20,372.6 MHz (Q_{2}) and 20,923.2 MHz (Q_{3}). We use a shaped raisedcosine pulse with a duration of 124 (62) ns to implement a singlequbit π (π/2) pulse. For the spectroscopy measurements in Fig. 2f, we use a Gaussian pulse (truncated at ±2σ). The phase rotation is virtually implemented by shifting the reference phase of the I/Q modulation waveform. Wherever possible, the singlequbit gates are applied in parallel. The twoqubit CZ gate is implemented by adiabatically pulsing the exchange coupling by the barrier gates. To guarantee the adiabaticity, we use a shaped cosine pulse^{6} with a duration of 50 ns to implement the CZ/2 gates, which results in a nominal peak exchange coupling of 10 MHz. During the experiments in the main text, the coupling strengths are finetuned to account for the conditional phase accumulation owing to the residual couplings of about 0.2 MHz (Extended Data Fig. 3d–f). We set the minimum interval between pulses to 20 ns to avoid the pulse interference owing to reflection.
Threequbit iToffoli gate
The resonantly driven iToffoli gate consists of the three stages in Extended Data Fig. 5a. In the main text, the population transfer property of the iToffoli gate is shown. For that, we set \({f}_{{\rm{Rabi}}}=J/\sqrt{3}\) (J = J_{12} = J_{23}) so that the offresonant rotation in the \({{\rm{Q}}}_{1}{{\rm{Q}}}_{3}= \uparrow \downarrow \rangle / \downarrow \uparrow \rangle \) subspaces is a 2π rotation. Furthermore, to obtain a correct quantum action, any unwanted phase accumulations on the threequbit state have to be calibrated out. This can be achieved by setting an appropriate exchange pulse duration of t_{tot} = t_{dc1} + t_{MW} + t_{dc2} and pulse timing of δt = t_{dc1} − t_{dc2} (ref. ^{33}). In theory, by setting the optimal exchange pulse duration to \({t}_{{\rm{tot}}}=\pi (4+\sqrt{3}\sqrt{13})/J\), the conditional phases between the \({{\rm{Q}}}_{1}{{\rm{Q}}}_{3}= \uparrow \downarrow \rangle , \downarrow \uparrow \rangle \;{\rm{and}}\; \uparrow \uparrow \rangle \) subspaces can be eliminated^{32}. For an exchange coupling of 4.5 MHz, it is 473 ns. In the experiment, we typically use a 460nslong rectangular pulse, which is shorter than the theoretical length owing to the finite pulse bandwidth. The microwave pulse timing δt is then adjusted to eliminate the conditional phase between the \({{\rm{Q}}}_{1}{{\rm{Q}}}_{3}= \downarrow \downarrow \rangle \) and the other subspaces. For the subspaces in which Q_{2} spin flip does not occur, shifting δt does not affect the outcome. In the case in which Q_{2} flips, when δt = 0, (quasi)static phase accumulation is fully cancelled out by the spinecho effect. The conditional phase in this case can be adjusted by varying δt because for finite δt, the echo works only partially and there is a phase accumulation of 2π(f_{1} − f_{0})δt. The remaining singlequbit phase offset is removed by a virtual singlequbit phase rotation. The phase offsets on the ancilla qubits are uncalibrated in the QEC experiments, although they can be calibrated out similarly. In Extended Data Fig. 5b, we illustrate the experimental sequence to calibrate the iToffoli gate phase accumulation. Extended Data Fig. 5c shows an example of an uncalibrated iToffoli gate and Extended Data Fig. 5d shows a phase measurement after the calibration. In the QEC experiments, this calibration is performed just before the data acquisition to minimize the influence of the slow drift of the resonance frequencies.
Readout error removal
For each of the experiments in which the readout errors are removed, we perform a reference measurement to obtain the readout fidelities. The spindown (spinup) readout fidelity \({F}_{\downarrow i}\) (\({F}_{\uparrow i}\)) is directly obtained by preparing a spindown (spinup) state and a projective measurement of Q_{i}. Using the measured readout fidelities, we correct the raw probabilities \({{\bf{P}}}_{{\rm{M}}}=({P}_{\downarrow \downarrow \downarrow },\ldots ,\,{P}_{\uparrow \uparrow \uparrow })\) as \({\bf{P}}{\boldsymbol{=}}{\left({F}_{1}\bigotimes {F}_{2}\bigotimes {F}_{3}\right)}^{1}{{\bf{P}}}_{{\rm{M}}},\) in which \({F}_{i}=\left(\begin{array}{cc}{F}_{\downarrow i} & 1{F}_{\uparrow i}\\ 1{F}_{\downarrow i} & {F}_{\uparrow i}\end{array}\right)\) and P is the corrected probabilities used for maximum likelihood estimation.
Measurement of the iToffoli gate truth table
To constrain all the elements of the truth table to be nonnegative, we use a maximum likelihood procedure as follows. The input is a set of 64 measured probabilities P_{ij}, in which the input is the ith eigenstate and the measurement is projected at the jth eigenstate. The readout errors are removed following the procedure above. We then minimize a cost function \(C\left({P}_{11}^{{\rm{MLE}}},\ldots ,{P}_{88}^{{\rm{MLE}}}\right)=\mathop{\sum }\limits_{i,j=1}^{8}{\left({P}_{ij}^{{\rm{MLE}}}{P}_{ij}\right)}^{2}\) for nonnegative parameters \({P}_{{ij}}^{{\rm{MLE}}}\). We constrain \({P}_{{ij}}^{{\rm{MLE}}}\) so that the sum of probabilities in one cycle of data acquisition is unity, that is, \(\mathop{\sum }\limits_{j=1}^{8}{P}_{{ij}}^{{\rm{MLE}}}=1\).
Quantum state tomography
Owing to the noise in the experiment, the density matrix obtained by a linear inversion is not always physical. Therefore, we use a maximum likelihood estimation to restrict the density matrix to be physical. We start from a Cholesky decomposition of a physical density matrix \(\rho ={T}^{\dagger }T/{\rm{Tr}}({T}^{\dagger }T)\), in which T is a complex lower triangular matrix with real diagonal elements. T has 2^{2D} (D is the number of qubits; D = 1 in Fig. 4d and D = 3 in Fig. 2d) real parameters \({\bf{t}}=({t}_{1},\ldots ,\,{t}_{{2}^{2D}})\) and we minimize the cost function
in which P_{ν} is the measured probability projected at a basis \({\psi }_{\nu }\rangle \). To determine the 2^{2D} parameters, the projection outcomes for linearly independent prerotations \({({\rm{I}},{\rm{X}}/2,{\rm{Y}}/2,{\rm{X}})}^{\bigotimes D}\) are used. To remove the error that could be introduced by the X prerotation, the projection outcomes for the X prerotations are calculated from the corresponding I rotation outcomes^{39}.
Quantum process tomography
We perform quantum process tomography to verify the process matrix and fidelity (Figs. 3 and 4 and Extended Data Fig. 5). The input state \(\psi \rangle \) is prepared by a spindown initialization followed by a singlequbit rotation \({R}_{i}\in {({\rm{I}},{\rm{X}}/2,{\rm{Y}}/2,{\rm{X}})}^{\bigotimes D}\,\) (D is the number of qubits; D = 1 in Fig. 4b and D = 3 in Extended Data Fig. 5e). After the quantum operations, tomographic readout of the resulting state is performed similarly to the case of quantum state tomography. For a quantum operation E acting on an input density matrix \({\rho }_{{\rm{in}}}^{k}\), the density matrix of the output state can be written as follows,
in which χ is the process matrix defined with respect to the Pauli operators \(B={({\rm{I}},{\sigma }_{{\rm{x}}},{\sigma }_{{\rm{y}}},{\sigma }_{{\rm{z}}})}^{\bigotimes D}\). Linear inversion of equation (1) can be performed to obtain a process matrix. However, the process matrix obtained in this way does not necessarily satisfy the physical conditions owing to the noise in the experiment. As in the state tomography, we can obtain an estimate of the physical process matrix by a maximum likelihood estimation. We start from a Cholesky decomposition \(\chi ={S}^{\dagger }S/{\rm{Tr}}({S}^{\dagger }S)\), in which S is a lower triangular matrix with real diagonal elements. S is parametrized by 2^{4D} real parameters \({\bf{s}}=({s}_{1},\cdots ,{s}_{{2}^{4D}})\) and we use a cost function L(s) as follows,
in which P^{kl} is the measured probability projected at \(\downarrow \rangle \) (D = 1) or \(\downarrow \downarrow \downarrow \rangle \) (D = 3) when an input state \({\rho }_{{\rm{in}}}^{k}\) is prepared and an observable M_{l} is measured. We numerically minimize the cost function to obtain the most probable estimate of physical χ. Then the process fidelity is calculated as Tr(χ_{ideal}χ), in which χ_{ideal} is an ideal process matrix.
Data availability
The data that support findings in this study are available from the Zenodo repository at https://doi.org/10.5281/zenodo.6601051.
References
Shor, P. W. Scheme for reducing decoherence in quantum computer memory. Phys. Rev. A 52, 2493–2496 (1995).
Fowler, A. G., Stephens, A. M. & Groszkowski, P. Highthreshold universal quantum computation on the surface code. Phys. Rev. A 80, 052312 (2009).
GonzalezZalba, M. F. et al. Scaling siliconbased quantum computing using CMOS technology. Nat. Electron. 4, 872–884 (2021).
Camenzind, L. C. et al. A hole spin qubit in a fin fieldeffect transistor above 4 kelvin. Nat. Electron. 5, 178–183 (2022).
Zwerver, A. M. J. et al. Qubits made by advanced semiconductor manufacturing. Nat. Electron. 5, 184–190 (2022).
Xue, X. et al. Quantum logic with spin qubits crossing the surface code threshold. Nature 601, 343–347 (2022).
Noiri, A. et al. Fast universal quantum gate above the faulttolerance threshold in silicon. Nature 601, 338–342 (2022).
Mills, A. R. et al. Twoqubit silicon quantum processor with operation fidelity exceeding 99%. Sci. Adv. 8, eabn5130 (2022).
Cory, D. G. et al. Experimental quantum error correction. Phys. Rev. Lett. 81, 2152–2155 (1998).
Schindler, P. et al. Experimental repetitive quantum error correction. Science (1979) 332, 1059–1061 (2011).
Reed, M. D. et al. Realization of threequbit quantum error correction with superconducting circuits. Nature 482, 382–385 (2012).
Feynman, R. P. Simulating physics with computers. Int. J. Theor. Phys. 21, 467–488 (1982).
Shor, P. W. Polynomialtime algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM J. Comput. 26, 1484–1509 (1997).
Nielsen, M. A. & Chuang, I. L. Quantum Computation and Quantum Information (Cambridge Univ. Press, 2000).
Knill, E., Laflamme, R., Martinez, R. & Negrevergne, C. Benchmarking quantum computers: the fivequbit error correcting code. Phys. Rev. Lett. 86, 5811–5814 (2001).
Egan, L. et al. Faulttolerant control of an errorcorrected qubit. Nature 598, 281–286 (2021).
Waldherr, G. et al. Quantum error correction in a solidstate hybrid spin register. Nature 506, 204–207 (2014).
Kelly, J. et al. State preservation by repetitive error detection in a superconducting quantum circuit. Nature 519, 66–69 (2015).
Chen, Z. et al. Exponential suppression of bit or phase errors with cyclic error correction. Nature 595, 383–387 (2021).
Veldhorst, M. et al. An addressable quantum dot qubit with faulttolerant controlfidelity. Nat. Nanotechnol. 9, 981–985 (2014).
Yoneda, J. et al. A quantumdot spin qubit with coherence limited by charge noise and fidelity higher than 99.9%. Nat. Nanotechnol. 13, 102–106 (2018).
Yang, C. H. et al. Silicon quantum processor unit cell operation above one Kelvin. Nature 580, 350–354 (2020).
Petit, L. et al. Universal quantum logic in hot silicon qubits. Nature 580, 355–359 (2020).
Takeda, K. et al. Quantum tomography of an entangled threequbit state in silicon. Nat. Nanotechnol. 16, 965–969 (2021).
Philips, S. G. J. et al. Universal control of a sixqubit quantum processor in silicon. Preprint at https://arxiv.org/abs/2202.09252 (2022).
Angus, S. J., Ferguson, A. J., Dzurak, A. S. & Clark, R. G. Gatedefined quantum dots in intrinsic silicon. Nano Lett. 7, 2051–2055 (2007).
PioroLadrière, M. et al. Electrically driven singleelectron spin resonance in a slanting Zeeman field. Nat. Phys. 4, 776–779 (2008).
Connors, E. J., Nelson, J. & Nichol, J. M. Rapid highfidelity spin state readout in Si/SiGe quantum dots via RF reflectometry. Phys. Rev. Appl. 10, 024019 (2020).
Noiri, A. et al. Radiofrequency detected fast charge sensing in undoped silicon quantum dots. Nano Lett. 20, 947–952 (2020).
Elzerman, J. M. et al. Singleshot readout of an individual electron spin in a quantum dot. Nature 430, 431–435 (2004).
Baart, T. A. et al. Singlespin CCD. Nat. Nanotechnol. 11, 330–334 (2016).
Barenco, A. et al. Elementary gates for quantum information. Phys. Rev. A 52, 3457–3467 (1995).
Gullans, M. J. & Petta, J. R. Protocol for a resonantly driven threequbit Toffoli gate with silicon spin qubits. Phys. Rev. B 100, 85419 (2019).
Ma¸dzik, M. T. et al. Conditional quantum operation of two exchangecoupled singledonor spin qubits in a MOScompatible silicon device. Nat. Commun. 12, 181 (2021).
Nakajima, T. et al. Robust singleshot spin measurement with 99.5% fidelity in a quantum dot array. Phys. Rev. Lett. 119, 017701 (2017).
Blumoff, J. Z. et al. Fast and highfidelity state preparation and measurement in triplequantumdot spin qubits. PRX Quantum 3, 10352 (2022).
Ha, W. et al. A flexible design platform for Si/SiGe exchangeonly qubits with low disorder. Nano Lett. 22, 1443–1448 (2021).
Xue, X. et al. CMOSbased cryogenic control of silicon quantum circuits. Nature 593, 205–210 (2021).
Watson, T. F. et al. A programmable twoqubit quantum processor in silicon. Nature 555, 633–637 (2018).
Acín, A., Bruß, D., Lewenstein, M. & Sanpera, A. Classification of mixed threequbit states. Phys. Rev. Lett. 87, 040401 (2001).
Barends, R. et al. Superconducting quantum circuits at the surface code threshold for fault tolerance. Nature 508, 500–503 (2014).
Cramer, J. et al. Repeated quantum error correction on a continuously encoded qubit by realtime feedback. Nat. Commun. 7, 11526 (2016).
Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978).
Akaike, H. in Selected Papers of Hirotugu Akaike. Springer Series in Statistics (eds Parzen, E., Tanabe, K. & Kitagawa, G.) 199–213 (Springer, 1998).
Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).
Acknowledgements
This work was supported financially by Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST) (JPMJCR15N2 and JPMJCR1675), JST Moonshot R&D grant no. JPMJMS2065, MEXT Quantum Leap Flagship Program (MEXT QLEAP) grant no. JPMXS0118069228 and JSPS KAKENHI grant nos. 18H01819, 19K14640 and 20H00237. T.N. acknowledges support from a Murata Science Foundation Research grant and JST PRESTO grant no. JPMJPR2017.
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K.T. and A.N. fabricated the device and performed the measurements. T.N. and T.K. contributed the data acquisition and discussed the results. K.T. wrote the paper with input from all coauthors. S.T. supervised the project.
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Extended data figures and tables
Extended Data Fig. 1 Threespin initialization and measurement.
The numbers (n_{1}n_{2}n_{3}) represent the respective electron occupations in the right, centre and left quantum dots. The light blue circles with labels A–F show the initialization, readout and manipulation bias configurations. a, Charge stability diagram measured as a function of the P1 and P3 gate voltages. The variation of the background signal is due to the Coulomb oscillation of the sensor quantum dot. b, Charge stability diagram measured as a function of the P1 and P2 gate voltages. The dashed white lines are guides to the eye for the position of faint charge transition lines, which could be visible by retuning of the sensor quantum dot. c, Schematic of the threespin initialization and measurement.
Extended Data Fig. 2 Singlequbit rotations.
All measurements are performed with all qubits initialized to spindown and the exchange couplings turned off. a, Rabi oscillation measurement sequence. t_{p} is the duration of the microwave pulse. b–d, Rabi oscillation measurement results. The microwave amplitude is adjusted so that the Rabi frequency is 4 MHz. e, Schematic sequence of the randomized benchmarking measurement. We prepare 16 randomly generated Clifford gate sequences and average the outcomes to obtain the sequence fidelities. f–h, Randomized benchmarking results. The implementation is the same as in, for example, refs. ^{7,24}. We perform two sets of benchmarking measurements, one designed to obtain an ideal spinup outcome and the other designed to obtain an ideal spindown outcome, wherein both cases the measurement is projected at a spinup state. The sequence fidelity F(m) is then defined as \(F(m)={F}_{\uparrow }(m){F}_{\downarrow }(m)\), in which \({F}_{\uparrow }(m)\) (\({F}_{\downarrow }(m)\)) is the measured sequence fidelity for the spinup (spindown) final state. Each dataset is fit by an exponential decay F(m) = Vp^{m} to extract the depolarizing parameter p and visibility V. The primitive gate fidelity shown in each figure is obtained as \(1(1p)/(2\times 1.875)\), in which the factor 1.875 is the average number of primitive gates per Clifford gate. The errors are 1σ from the mean.
Extended Data Fig. 3 Twoqubit couplings.
All measurements are performed with all qubits initialized to spindown. a, Schematic sequence of the exchange spectroscopy measurement. To narrow the resonance peaks, the microwave power for the controlled rotation is decreased by 12 dB from the values used for singlequbit rotations. vBi (i = 2, 3) represents a virtual barrier gate voltage. b,c, Results of the exchange spectroscopy measurements. In each figure, the separation of the two peaks corresponds to the exchange coupling. The background slope of the resonance frequency is due to the displacement of the quantum dot position in the micromagnet field gradient. The frequency offset from the values in Methods is due to the decay of the persistent current in the superconducting magnet. d, Schematic sequence of the residual exchangecoupling measurement. e,f, Results of the measurement of residual exchange couplings between neighbouring qubits. Each dataset is fit with a sinusoidal function P(t_{evol}) = Vsin(πt_{evol}J_{off}) to extract the residual exchange coupling J_{off}. V is the visibility of the oscillation. The errors are 1σ from the mean. g, Schematic sequence of the decoupled CZ oscillation measurement. h,i Typical decoupled CZ oscillations. The solid lines show the fit to a Gaussian decay. The decay times are 3.27 ± 0.08 μs (h) and 5.2 ± 0.3 μs (i). Here we adjust the virtual barrier gate voltages so that the exchange coupling is roughly 10 MHz. All errors are 1σ from the mean.
Extended Data Fig. 4 Coherence times.
All measurements are performed with all qubits initialized to spindown and the exchange couplings turned off. All errors are 1σ from the mean. a, Schematic sequence of the T_{1} measurement. The qubit state is measured after the preparation of a spinup excited state and an idle time of t_{w}. b–d, T_{1} measurement results. Each dataset is fit by an exponential decay to extract the T_{1} relaxation time. e, Schematic sequence of the Ramsey interferometry. Instead of detuning the microwave frequency, we vary the phase of the second microwave pulse as θ = 2πt_{evol} × (2 MHz) such that we observe an oscillation at about 2 MHz. f–h, Ramsey interferometry measurement results. To extract the \({T}_{2}^{* }\) inhomogeneous dephasing time, each dataset is fitted with a Gaussian decay function \(P\left({t}_{{\rm{evol}}}\right)=A{\rm{\exp }}\left({\left(\frac{{t}_{{\rm{evol}}}}{{T}_{2}^{* }}\right)}^{2}\right){\rm{\cos }}(2\pi \left({\rm{\delta }}f\right){t}_{{\rm{evol}}}+\varphi )+B\), in which A and B are the constants to account for the readout fidelities, δf is the oscillation frequency and ϕ is the phase offset. The integration time is about 70 s for all traces. The larger scattering of the data points for Q_{2} (g) is due to the longer pulse cycle and less averaging. i, Schematic sequence of the Hahn echo measurement. j–l, Hahn echo results. For each dataset, the echo time \({T}_{2}^{{\rm{H}}}\) is extracted by fitting with an exponential decay function \(P\left({t}_{{\rm{evol}}}\right)=V{\rm{\exp }}\left({\left(\frac{{t}_{{\rm{evol}}}}{{T}_{2}^{{\rm{H}}}}\right)}^{\gamma }\right)\), in which V is the visibility and γ is the exponent. The exponents are γ = 0.98 ± 0.09 (Q_{1}), 1.46 ± 0.05 (Q_{2}) and 1.83 ± 0.07 (Q_{3}).
Extended Data Fig. 5 iToffoli gate characterizations.
a, Schematic of the iToffoli gate implementation. The iToffoli gate can be realized by a combination of an exchange pulse and a microwave pulse. The exchange pulse duration (t_{dc1} + t_{MW} + t_{dc2}), microwave pulse duration (t_{MW}) and timing (t_{dc1} − t_{dc2}) are finetuned to obtain a correct phase evolution. b, Quantum circuit used to measure the phase accumulation during the iToffoli gate operation. The iToffoli gate is interleaved between two π/2 pulses to realize Ramseytype phase detection. Only when \({{\rm{Q}}}_{1}{{\rm{Q}}}_{3}= \downarrow \downarrow \rangle \) does a spin flip occur, which is detected as a π phase shift for a correct iToffoli gate. For the other ancilla qubit configurations, the phase accumulation should be zero. c, Example phase measurement result before the iToffoli gate phase calibration. The resonance frequency and microwave amplitude are calibrated. d, Phase measurement after the calibration of both conditional and unconditional phases. In the calibration procedure, we optimize the duration of the exchange pulse and the timing of the microwave pulse (see Methods). We obtain correct phase evolution for all ancilla qubit configurations. The phase offsets are (1.03 ± 0.01)π, (0.04 ± 0.01)π, (0.03 ± 0.01)π and (0.05 ± 0.01)π for \({{\rm{Q}}}_{1}{{\rm{Q}}}_{3}= \downarrow \downarrow \rangle ,\; \uparrow \downarrow \rangle ,\; \downarrow \uparrow \rangle \;{\rm{and}}\; \uparrow \uparrow \rangle \), respectively. The errors are 1σ from the mean. e, Experimental process matrix (χ matrix) of the iToffoli gate obtained by threequbit quantum process tomography (see Methods). The labels represent threequbit Pauli operators. We obtain a gate fidelity of 0.67 from the data. f, Ideal process matrix of iToffoli gate. g, Simulated process matrix of iToffoli gate under quasistatic singlequbit phase noise. Here we assume \({T}_{2}^{* }\) = 1.2, 1.2 and 1.3 μs for Q_{1}, Q_{2} and Q_{3}, respectively (ergodic \({T}_{2}^{* }\) measured for long integration time). The effect of chargenoiseinduced exchange fluctuation (noise in ZZ term) is not taken into account. The simulation reproduces some features in the experimental data. The gate fidelity estimated from the simulation is 0.69, which agrees well with the experimental result.
Extended Data Fig. 6 Comparison of different models for QEC result.
Comparison of the fitting results of quadratic (a + cp^{2} + dp^{3}), thirdorder polynomial (a + bp + cp^{2} + dp^{3}) and linear functions (a + bp). The coefficients a, b, c and d are the fitting parameters. We obtain a = 0.879 ± 0.006, c = −2.72 ± 0.06 and d = 1.89 ± 0.06 (quadratic function), a = 0.88 ± 0.01, b = 0.0 ± 0.1, c = −2.75 ± 0.24 and d = 1.91 ± 0.16 (thirdorder polynomial function), and a = 0.94 ± 0.02 and b = −1.00 ± 0.03 (linear function) from the fitting results. To compare the fitting results by different models, we calculate the Bayesian information criterion^{43} (BIC) and the Akaike information criterion^{44} (AIC). Models with lower BIC are preferred, whereas models with lower AIC provide better prediction of the experimental behaviour. The difference between the linear fit and the others is found to be decisive^{45} (ΔBIC ≈ 50). In addition, although the difference is not as large (ΔBIC ≈ 3), the quadratic fit without the firstorder term is more preferred than the polynomial function including the firstorder term. The errors are 1σ from the mean.
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Takeda, K., Noiri, A., Nakajima, T. et al. Quantum error correction with silicon spin qubits. Nature 608, 682–686 (2022). https://doi.org/10.1038/s41586022049866
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DOI: https://doi.org/10.1038/s41586022049866
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