论文标题
变分量子玻尔兹曼机器
Variational Quantum Boltzmann Machines
论文作者
论文摘要
这项工作为量子玻尔兹曼机器（QBM）提出了一种新颖的实现方法。所需Gibbs状态的制备以及对损失函数分析梯度的评估是基于变异量子假想时间演变的，该技术通常用于基态计算。与现有方法相反，这种实现有助于近期兼容的QBM培训，并具有任意参数化的汉密尔顿人的实际损失函数梯度，不一定必须完全可见，但也可能包括隐藏的单位。通过数值模拟和实验在IBM量子提供的实际量子硬件上运行，可以证明变异Gibbs状态的近似值。此外，我们说明了使用数值模拟的这种变异QBM方法在生成和歧视性学习任务中的应用。
This work presents a novel realization approach to Quantum Boltzmann Machines (QBMs). The preparation of the required Gibbs states, as well as the evaluation of the loss function's analytic gradient is based on Variational Quantum Imaginary Time Evolution, a technique that is typically used for ground state computation. In contrast to existing methods, this implementation facilitates near-term compatible QBM training with gradients of the actual loss function for arbitrary parameterized Hamiltonians which do not necessarily have to be fully-visible but may also include hidden units. The variational Gibbs state approximation is demonstrated with numerical simulations and experiments run on real quantum hardware provided by IBM Quantum. Furthermore, we illustrate the application of this variational QBM approach to generative and discriminative learning tasks using numerical simulation.
