Differentiable Analog Quantum Computing for Optimization and Control
Differentiable Analog Quantum Computing for Optimization and Control
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DOI：
10.48550/arxiv.2210.15812
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Jiaqi Leng;Yuxiang Peng;Yi-Ling Qiao;Ming-Chyuan Lin;Xiaodi Wu
中科院分区：
文献类型：
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作者：
Jiaqi Leng;Yuxiang Peng;Yi-Ling Qiao;Ming-Chyuan Lin;Xiaodi Wu
我们制定了第一个可微分模拟量子计算框架，在模拟信号（脉冲）水平上进行了特定的参数化设计，以便通过变分方法更好地利用近期量子器件。我们进一步提出了一种可扩展的方法来估计量子动力学的梯度，使用蒙特卡洛采样的前向传递，这导致了我们的框架中可扩展的基于梯度的训练的量子随机梯度下降算法。将我们的框架应用于量子优化和控制，我们观察到可微模拟量子计算相对于基于参数化数字量子电路的SOTA的显著优势。
We formulate the first differentiable analog quantum computing framework with a specific parameterization design at the analog signal (pulse) level to better exploit near-term quantum devices via variational methods. We further propose a scalable approach to estimate the gradients of quantum dynamics using a forward pass with Monte Carlo sampling, which leads to a quantum stochastic gradient descent algorithm for scalable gradient-based training in our framework. Applying our framework to quantum optimization and control, we observe a significant advantage of differentiable analog quantum computing against SOTAs based on parameterized digital quantum circuits by orders of magnitude.