论文标题
使用加固学习的量子退火处理器的纠正后校正
Post-Error Correction for Quantum Annealing Processor using Reinforcement Learning
论文作者
论文摘要
在凝结物理学中，找到伊辛旋转玻璃的基态是一个重要且具有挑战性的问题（实际上是NP-HARD）。但是，由于其与各种组合优化问题（例如旅行推销员或蛋白质折叠）的深入关系，其应用远远超出了物理。解决方案实例的复杂且有希望的新方法依赖于量子资源。特别是，量子退火是一种量子计算范式，特别适合二次无约束的二进制优化（QUBO）。然而，市售的量子退火器（即D波）容易出现各种错误，并且它们找到低能能状态（对应于优质质量的解决方案）的能力受到限制。这自然要求采用后处理程序来纠正错误（能够降低退火器发现的能量）。作为概念验证，这项工作将围绕Dirac架构的最新想法与嵌合式拓扑结合在一起，并将它们应用于现实环境中，作为量子退火器的错误校正方案。我们的初步结果表明，如何使用增强学习来纠正量子退火器的状态输出。这种方法具有出色的可扩展性，因为它可以在小实例上进行培训并为大型实例部署。但是，其在嵌合图上的性能仍然不如在这种情况下可以合并的典型算法，例如模拟退火。
Finding the ground state of the Ising spin-glass is an important and challenging problem (NP-hard, in fact) in condensed matter physics. However, its applications spread far beyond physic due to its deep relation to various combinatorial optimization problems, such as travelling salesman or protein folding. Sophisticated and promising new methods for solving Ising instances rely on quantum resources. In particular, quantum annealing is a quantum computation paradigm, that is especially well suited for Quadratic Unconstrained Binary Optimization (QUBO). Nevertheless, commercially available quantum annealers (i.e., D-Wave) are prone to various errors, and their ability to find low energetic states (corresponding to solutions of superior quality) is limited. This naturally calls for a post-processing procedure to correct errors (capable of lowering the energy found by the annealer). As a proof-of-concept, this work combines the recent ideas revolving around the DIRAC architecture with the Chimera topology and applies them in a real-world setting as an error-correcting scheme for quantum annealers. Our preliminary results show how to correct states output by quantum annealers using reinforcement learning. Such an approach exhibits excellent scalability, as it can be trained on small instances and deployed for large ones. However, its performance on the chimera graph is still inferior to a typical algorithm one could incorporate in this context, e.g., simulated annealing.
