论文标题
量子吉布斯状态准备的自适应变分算法
Adaptive variational algorithms for quantum Gibbs state preparation
论文作者
论文摘要
Gibbs热状态的制备是量子计算中的重要任务，该任务是量子模拟，量子优化和量子机学习中的应用。但是，许多用于准备吉布斯州的算法依赖于在近期硬件上难以实施的量子子例程。在这里，我们通过（i）引入一个目标函数来解决这个问题，该目标函数与自由能不同，并且（ii）使用动态生成的，解决问题的Ansätze。这允许使用低深度电路任意准确的Gibbs状态准备。为了验证我们的方法的有效性，我们从数值上证明我们的算法可以在广泛的温度和各种哈密顿人中准备高保真的吉布斯状态。
The preparation of Gibbs thermal states is an important task in quantum computation with applications in quantum simulation, quantum optimization, and quantum machine learning. However, many algorithms for preparing Gibbs states rely on quantum subroutines which are difficult to implement on near-term hardware. Here, we address this by (i) introducing an objective function that, unlike the free energy, is easily measured, and (ii) using dynamically generated, problem-tailored ansätze. This allows for arbitrarily accurate Gibbs state preparation using low-depth circuits. To verify the effectiveness of our approach, we numerically demonstrate that our algorithm can prepare high-fidelity Gibbs states across a broad range of temperatures and for a variety of Hamiltonians.
