论文标题
使用自动差异化的量子计算忠诚度敏感性
Quantum computing fidelity susceptibility using automatic differentiation
论文作者
论文摘要
自动差异是机器学习和量子机学习软件库的宝贵特征。在这项工作中，可以显示如何使用量子自动分化来解决计算保真度易感性的凝结物问题，该数量的价值可能表明系统中的相变。使用模拟提出了结果，包括用于横向场模型的小实例的硬件噪声，并突出显示了许多可以应用的优化。自动化框架内将误差缓解（零噪声外推）应用于计算保真度易感性和相关数量的许多梯度值，即能量的第二个导数。发现此类计算对缓解误差方法产生的其他统计噪声非常敏感
Automatic differentiation is an invaluable feature of machine learning and quantum machine learning software libraries. In this work it is shown how quantum automatic differentiation can be used to solve the condensed-matter problem of computing fidelity susceptibility, a quantity whose value may be indicative of a phase transition in a system. Results are presented using simulations including hardware noise for small instances of the transverse-field Ising model, and a number of optimizations that can be applied are highlighted. Error mitigation (zero-noise extrapolation) is applied within the autodifferentiation framework to a number of gradient values required for computation of fidelity susceptibility and a related quantity, the second derivative of the energy. Such computations are found to be highly sensitive to the additional statistical noise incurred by the error mitigation method
