论文标题
可区分的模拟量子计算用于优化和控制
Differentiable Analog Quantum Computing for Optimization and Control
论文作者
论文摘要
我们在模拟信号（脉冲）水平上使用特定的参数化设计制定了第一个可区分的模拟量子计算框架，以通过变异方法更好地利用近期量子设备。我们进一步提出了一种可扩展的方法，可以使用蒙特卡洛采样的前向通行证估算量子动力学的梯度，从而导致量子随机梯度下降算法，用于我们在框架中基于可扩展梯度的训练。将我们的框架应用于量子优化和控制中，我们观察到基于参数化数字量子电路对SOTA的可区分模拟量子计算的显着优势。
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.
