Quantum computing can become scalable through error correction, but logical error rates only decrease with system size when physical errors are sufficiently uncorrelated. During computation,unused high energy levels of the qubits can become excited, creating leakage states that are long-lived and mobile. Particularly for superconducting transmon qubits, this leakage opens a path to errors that are correlated in space and time. Here, we report a reset protocol that returns a qubit to the ground state from all relevant higher level states. We test its performance with the bit-flip stabilizer code, a simplified version of the surface code for quantum error correction. We investigate the accumulation and dynamics of leakage during error correction. Using this protocol, we find lower rates of logical errors and an improved scaling and stability of error suppression with increasing qubit number. This demonstration provides a key step on the path towards scalable quantum computing.
Machine learning techniques have led to broad adoption of a statistical model of computing. The statistical distributions natively available on quantum processors are a superset ofthose available classically. Harnessing this attribute has the potential to accelerate or otherwise improve machine learning relative to purely classical performance. A key challenge toward that goal is learning to hybridize classical computing resources and traditional learning techniques with the emerging capabilities of general purpose quantum processors. Here, we demonstrate such hybridization by training a 19-qubit gate model processor to solve a clustering problem, a foundational challenge in unsupervised learning. We use the quantum approximate optimization algorithm in conjunction with a gradient-free Bayesian optimization to train the quantum machine. This quantum/classical hybrid algorithm shows robustness to realistic noise, and we find evidence that classical optimization can be used to train around both coherent and incoherent imperfections.
We show that parametric coupling techniques can be used to generate selective entangling interactions for multi-qubit processors. By inducing coherent population exchange between adjacentqubits under frequency modulation, we implement a universal gateset for a linear array of four superconducting qubits. An average process fidelity of =93% is measured by benchmarking three two-qubit gates with quantum process tomography. In order to test the suitability of these techniques for larger computations, we prepare a six-qubit register in all possible bitstring permutations and monitor the performance of a two-qubit gate on another pair of qubits. Across all these experiments, an average fidelity of =91.6±2.6% is observed. These results thus offer a path to a scalable architecture with high selectivity and low crosstalk.
We propose and implement a family of entangling qubit operations activated by radio-frequency flux pulses. By parametrically modulating the frequency of a tunable transmon, these operationsselectively actuate resonant exchange of excitations with a statically coupled, but otherwise off-resonant, neighboring transmon. This direct exchange of excitations between qubits obviates the need for mediator qubits or resonator modes, and it allows for the full utilization of all qubits in a scalable architecture. Moreover, we are able to activate three highly-selective resonances, corresponding to two different classes of entangling gates that enable universal quantum computation: an iSWAP and a controlled-Z rotation. This selectivity is enabled by resonance conditions that depend both on frequency and amplitude, and is helpful in avoiding frequency crowding in a scalable architecture. We report average process fidelities of F = 0.93 for a 135 ns iSWAP, and F = 0.92 for 175 ns and 270 ns controlled-Z operations.