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Policy Gradient Approach to Compilation of Variational Quantum Circuits
Quantum ( IF 6.4 ) Pub Date : 2022-09-08 , DOI: 10.22331/q-2022-09-08-797
David A. Herrera-Martí

We propose a method for finding approximate compilations of quantum unitary transformations, based on techniques from policy gradient reinforcement learning. The choice of a stochastic policy allows us to rephrase the optimization problem in terms of probability distributions, rather than variational gates. In this framework, the optimal configuration is found by optimizing over distribution parameters, rather than over free angles. We show numerically that this approach can be more competitive than gradient-free methods, for a comparable amount of resources, both for noiseless and noisy circuits. Another interesting feature of this approach to variational compilation is that it does not need a separate register and long-range interactions to estimate the end-point fidelity, which is an improvement over methods which rely on the Hilbert-Schmidt test. We expect these techniques to be relevant for training variational circuits in other contexts.

中文翻译:

编译变分量子电路的策略梯度方法

我们提出了一种基于策略梯度强化学习技术来寻找量子酉变换的近似编译的方法。随机策略的选择允许我们根据概率分布而不是变分门来重新表述优化问题。在这个框架中,通过优化分布参数而不是自由角度来找到最佳配置。我们在数值上表明,对于相当数量的资源,无论是无噪声电路还是有噪声电路,这种方法都比无梯度方法更具竞争力。这种变分编译方法的另一个有趣特征是它不需要单独的寄存器和远程交互来估计端点保真度,这是对依赖希尔伯特-施密特检验的方法的改进。我们期望这些技术与在其他情况下训练变分电路相关。
更新日期:2022-09-08
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