论文标题
使用经典信号处理的变分量子算法景观的有效恢复
Efficient recovery of variational quantum algorithms landscapes using classical signal processing
论文作者
论文摘要
我们采用光谱分析和压缩传感来确定可以纯粹是经典或使用最少量子计算机访问的变量算法的成本函数的设置。我们提供了支持稀疏恢复技术生存能力的理论和数值证据。为了证明这种方法，我们使用基本追求denosising来有效地恢复了从极少数样品中的大型系统大小的模拟量子近似优化算法（QAOA）实例。我们的结果表明，稀疏恢复可以在优化变分算法时更有效地利用和分布量子资源。
We employ spectral analysis and compressed sensing to identify settings where a variational algorithm's cost function can be recovered purely classically or with minimal quantum computer access. We present theoretical and numerical evidence supporting the viability of sparse recovery techniques. To demonstrate this approach, we use basis pursuit denoising to efficiently recover simulated Quantum Approximate Optimization Algorithm (QAOA) instances of large system size from very few samples. Our results indicate that sparse recovery can enable a more efficient use and distribution of quantum resources in the optimisation of variational algorithms.
