Machine Learning of Noise-Resilient Quantum Circuits

作     者：Lukasz Cincio Kenneth Rudinger Mohan Sarovar Patrick J. Coles 

作者机构：Theoretical Division MS 213 Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Quantum Computer Science Sandia National Laboratories Albuquerque New Mexico 87185 USA Extreme-scale Data Science and Analytics Sandia National Laboratories Livermore California 94550 USA 

出 版 物：《PRX Quantum》 (PRX. Quantum.)

年 卷 期：2021年第2卷第1期

页      面：010324-010324页

核心收录：

基　　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] 

主　　题：Machine learning Quantum algorithms Quantum computation 

摘      要：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.