论文标题
在约束变化量子优化中利用构成能量
Exploiting In-Constraint Energy in Constrained Variational Quantum Optimization
论文作者
论文摘要
将近期量子优化算法应用于工业相关问题的核心挑战是需要纳入复杂的约束。通常，此类约束不能轻易在电路中编码，并且不能保证量子电路测量结果尊重约束。因此，优化必须通过增加对目标的约束侵犯的惩罚来兑换企业内解决方案的概率和质量。我们提出了一种新方法，以解决不受限制，易于实现的量子Ansatze的约束优化问题。我们的方法利用构成能量作为目标，并对优化器的构成概率增加了较低的约束。我们证明了解决方案质量的显着增长，而不是直接优化刑罚能量。我们在Qvoice中实现了我们的方法，Qvoice是一个与Qiskit接口的Python软件包，以在模拟器和Quantum硬件上快速进行原型制作。
A central challenge of applying near-term quantum optimization algorithms to industrially relevant problems is the need to incorporate complex constraints. In general, such constraints cannot be easily encoded in the circuit, and the quantum circuit measurement outcomes are not guaranteed to respect the constraints. Therefore, the optimization must trade off the in-constraint probability and the quality of the in-constraint solution by adding a penalty for constraint violation into the objective. We propose a new approach for solving constrained optimization problems with unconstrained, easy-to-implement quantum ansatze. Our method leverages the in-constraint energy as the objective and adds a lower-bound constraint on the in-constraint probability to the optimizer. We demonstrate significant gains in solution quality over directly optimizing the penalized energy. We implement our method in QVoice, a Python package that interfaces with Qiskit for quick prototyping in simulators and on quantum hardware.
