压缩传感
算法
量子
计算机科学
信号恢复
功能(生物学)
基础(线性代数)
数学优化
数学
物理
量子力学
几何学
进化生物学
生物
作者
Enrico Fontana,Ivan Rungger,Daniel Ross,Cristina Cîrstoiu
出处
期刊:Cornell University - arXiv
日期:2022-01-01
标识
DOI:10.48550/arxiv.2208.05958
摘要
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.
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