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Register a new userBenchmarks for approximate CNOTs based on a 17-Qubit Surface Code
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Scalable and fault-tolerant quantum computation will require error correction. This will demand constant measurement of many-qubit observables, implemented using a vast number of CNOT gates. Indeed, practically all operations performed by a fault-tolerant device will be these CNOTs, or equivalent two-qubit controlled operations. It is therefore important to devise benchmarks for these gates that explicitly quantify their effectiveness at this task. Here we develop such benchmarks, and demonstrate their use by applying them to a range of differently implemented controlled gates and a particular quantum error correcting code. Specifically, we consider spin qubits confined to quantum dots that are coupled either directly or via floating gates to implement the minimal 17-qubit instance of the surface code. Our results show that small differences in the gate fidelity can lead to large differences in the performance of the surface code. This shows that gate fidelity is not, in general, a good predictor of code performance.
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Leakage outside of the qubit computational subspace, present in many leading experimental platforms, constitutes a threatening error for quantum error correction (QEC) for qubits. We develop a leakage-detection scheme via Hidden Markov models (HMMs) for transmon-based implementations of the surface code. By performing realistic density-matrix simulations of the distance-3 surface code (Surface-17), we observe that leakage is sharply projected and leads to an increase in the surface-code defect probability of neighboring stabilizers. Together with the analog readout of the ancilla qubits, this increase enables the accurate detection of the time and location of leakage. We restore the logical error rate below the memory break-even point by post-selecting out leakage, discarding about 47% of the data. Leakage detection via HMMs opens the prospect for near-term QEC demonstrations, targeted leakage reduction and leakage-aware decoding and is applicable to other experimental platforms.
We present a set of practical benchmarks for $N$-qubit arrays that economically test the fidelity of achieving multi-qubit nonclassicality. The benchmarks are measurable correlators similar to 2-qubit Bell correlators, and are derived from a particular set of geometric structures from the $N$-qubit Pauli group. These structures prove the Greenberger-Horne-Zeilinger (GHZ) theorem, while the derived correlators witness genuine $N$-partite entanglement and establish a tight lower bound on the fidelity of particular stabilizer state preparations. The correlators need only $M leq N+1$ distinct measurement settings, as opposed to the $2^{2N}-1$ settings that would normally be required to tomographically verify their associated stabilizer states. We optimize the measurements of these correlators for a physical array of qubits that can be nearest-neighbor-coupled using a circuit of controlled-$Z$ gates with constant gate depth to form $N$-qubit linear cluster states. We numerically simulate the provided circuits for a realistic scenario with $N=3,...,9$ qubits, using ranges of $T_1$ energy relaxation times, $T_2$ dephasing times, and controlled-$Z$ gate-fidelities consistent with Googles 9-qubit superconducting chip. The simulations verify the tightness of the fidelity bounds and witness nonclassicality for all nine qubits, while also showing ample room for improvement in chip performance.
We realize a suite of logical operations on a distance-two logical qubit stabilized using repeated error detection cycles. Logical operations include initialization into arbitrary states, measurement in the cardinal bases of the Bloch sphere, and a universal set of single-qubit gates. For each type of operation, we observe higher performance for fault-tolerant variants over non-fault-tolerant variants, and quantify the difference through detailed characterization. In particular, we demonstrate process tomography of logical gates, using the notion of a logical Pauli transfer matrix. This integration of high-fidelity logical operations with a scalable scheme for repeated stabilization is a milestone on the road to quantum error correction with higher-distance superconducting surface codes.
We introduce a data bus, for reducing the qubit counts within quantum computations (protected by surface codes). For general computations, an automated trade-off analysis (software tool and source code are open sourced and available online) is performed to determine to what degree qubit counts are reduced by the data bus: is the time penalty worth the qubit count reductions? We provide two examples where the qubit counts are convincingly reduced: 1) interaction of two surface code patches on NISQ machines with 28 and 68 qubits, and 2) very large-scale circuits with a structure similar to state-of-the-art quantum chemistry circuits. The data bus has the potential to transform all layers of the quantum computing stack (e.g., as envisioned by Google, IBM, Riggeti, Intel), because it simplifies quantum computation layouts, hardware architectures and introduces lower qubits counts at the expense of a reasonable time penalty.
Topological quantum error correction codes are known to be able to tolerate arbitrary local errors given sufficient qubits. This includes correlated errors involving many local qubits. In this work, we quantify this level of tolerance, numerically studying the effects of many-qubit errors on the performance of the surface code. We find that if increasingly large area errors are at least moderately exponentially suppressed, arbitrarily reliable quantum computation can still be achieved with practical overhead. We furthermore quantify the effect of non-local two-qubit correlated errors, which would be expected in arrays of qubits coupled by a polynomially decaying interaction, and when using many-qubit coupling devices. We surprisingly find that the surface code is very robust to this class of errors, despite a provable lack of a threshold error rate when such errors are present.
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