Efficient recovery of variational quantum algorithms landscapes using classical signal processing
Year: 2022
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Fontana, Enrico, Rungger, Ivan, Duncan, Ross, Cîrstoiu, Cristina
Institutions: National Physical Laboratory, Quantinuum (United Kingdom)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4291237851