论文标题
使用机器学习提高量子退火器的性能
Boosting the Performance of Quantum Annealers using Machine Learning
论文作者
论文摘要
嘈杂的中间量子量子（NISQ）设备率先率先进行第二次量子革命。其中，量子退火器是当前提供现实世界，多达5000 QUAT的商业应用的唯一产品。量子退火器可以解决的问题的大小主要受环境噪声和处理器内在缺陷引起的错误限制。我们基于机器学习方法，采用一种新颖的误差校正方法来解决固有缺陷的问题。我们的方法调整了输入的哈密顿量，以最大程度地提高找到解决方案的可能性。在我们的实验中，提出的误差校正方法提高了退火的性能，最多三个数量级，并允许解决先前棘手的最大复杂问题。
Noisy intermediate-scale quantum (NISQ) devices are spearheading the second quantum revolution. Of these, quantum annealers are the only ones currently offering real world, commercial applications on as many as 5000 qubits. The size of problems that can be solved by quantum annealers is limited mainly by errors caused by environmental noise and intrinsic imperfections of the processor. We address the issue of intrinsic imperfections with a novel error correction approach, based on machine learning methods. Our approach adjusts the input Hamiltonian to maximize the probability of finding the solution. In our experiments, the proposed error correction method improved the performance of annealing by up to three orders of magnitude and enabled the solving of a previously intractable, maximally complex problem.
