期刊
PRX QUANTUM
卷 2, 期 1, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PRXQuantum.2.010324
关键词
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资金
- Laboratory Directed Research and Development program of Los Alamos National Laboratory [20180628ECR, 20190065DR]
- LANL ASC Beyond Moore's Law project
- U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research
- U.S. Department of Energy's National Nuclear Security Administration [DE-NA0003525]
The paper introduces a machine learning framework, Noise-Aware Circuit Learning (NACL), to reduce the impact of quantum hardware noise on quantum circuits. By minimizing task-specific cost functions, NACL outputs optimized circuits to accomplish tasks in the presence of noise.
Noise mitigation and reduction will be crucial for obtaining useful answers from near-term quantum computers. In this work, we present a general framework based on machine learning for reducing the impact of quantum hardware noise on quantum circuits. Our method, called noise-aware circuit learning (NACL), applies to circuits designed to compute a unitary transformation, prepare a set of quantum states, or estimate an observable of a many-qubit state. Given a task and a device model that captures information about the noise and connectivity of qubits in a device, NACL outputs an optimized circuit to accomplish this task in the presence of noise. It does so by minimizing a task-specific cost function over circuit depths and circuit structures. To demonstrate NACL, we construct circuits resilient to a fine-grained noise model derived from gate set tomography on a superconducting-circuit quantum device, for applications including quantum state overlap, quantum Fourier transform, and W-state preparation.
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