The readout and reset take up most of the cycle time, so the concurrent data qubit idling is a dominant source of error. Cycle duration is 921 ns, including 25-ns single-qubit gates, 34-ns two-qubit gates, 500-ns measurement and 160-ns reset (see Supplementary Information for compilation details). All stabilizers are measured in this manner concurrently. Data qubits perform dynamical decoupling (DD) while waiting for the measurement and reset. Each qubit participates in four CZ gates (black) with its four nearest neighbours, interspersed with Hadamard gates (H), and finally, the measure qubit is measured and reset to \(\left|0\right\rangle \) (MR). b, Illustration of a stabilizer measurement, focusing on one data qubit (labelled ψ) and one measure qubit (labelled 0), in perspective view with time progressing to the right. The upper right quadrant (red outline) is one of four subset distance-3 codes (the four quadrants) that we compare to distance-5. Representative logical operators Z L (black) and X L (green) traverse the array, intersecting at the lower-left data qubit. Each measure qubit is associated with a stabilizer (blue coloured tile, dark: X, light: Z). In this work we report a 72-qubit superconducting device supporting a 49-qubit distance-5 ( d = 5) surface code that narrowly outperforms its average subset 17-qubit distance-3 surface code, demonstrating a critical step towards scalable quantum error correction.Ī, Schematic of a 72-qubit Sycamore device with a distance-5 surface code embedded, consisting of 25 data qubits (gold) and 24 measure qubits (blue). In practice, demonstrating reduced logical error requires scaling up a device to support a code that can correct at least two errors, without sacrificing state-of-the-art performance. In theory, logical errors should be reduced if physical errors are sufficiently sparse in the quantum processor. However, a crucial question remains of whether scaling up the error-correcting code size will reduce logical error rates in a real device. Several works have reported quantum error correction on codes able to correct a single error, including the distance-3 Bacon–Shor 20, colour 21, five-qubit 22, heavy-hexagon 23 and surface 24, 25 codes, as well as continuous variable codes 26, 27, 28, 29. Fortunately, quantum error correction can exponentially suppress the operational error rates in a quantum processor, at the expense of temporal and qubit overhead 18, 19. These applications often require billions of quantum operations 9, 10, 11 and state-of-the-art quantum processors typically have error rates around 10 −3 per gate 12, 13, 14, 15, 16, 17, far too high to execute such large circuits. Since Feynman’s proposal to compute using quantum mechanics 3, many potential applications have emerged, including factoring 4, optimization 5, machine learning 6, quantum simulation 7 and quantum chemistry 8. These results mark an experimental demonstration in which quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation. We accurately model our experiment, extracting error budgets that highlight the biggest challenges for future systems. To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 × 10 −6 logical error per cycle floor set by a single high-energy event (1.6 × 10 −7 excluding this event). We find that our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, in terms of both logical error probability over 25 cycles and logical error per cycle ((2.914 ± 0.016)% compared to (3.028 ± 0.023)%). Here we report the measurement of logical qubit performance scaling across several code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low for logical performance to improve with increasing code size. Quantum error correction 1, 2 offers a path to algorithmically relevant error rates by encoding logical qubits within many physical qubits, for which increasing the number of physical qubits enhances protection against physical errors. Practical quantum computing will require error rates well below those achievable with physical qubits.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |