Benchmarking NISQ and QEC Experiments with Tensor Networks

Explore cutting-edge applications of tensor networks in quantum computing through this comprehensive lecture by Benjamin Villalonga from Google Quantum AI. Delve into two key areas: improving adversarial methods for challenging NISQ experiments and developing a maximum-likelihood tensor network decoder for quantum error correction (QEC). Learn about optimizing tensor network contraction schemes within supercomputer constraints and discover how the presented decoder achieved the first experimental demonstration of error suppression on a surface code. Gain insights into evaluating alternative decoding strategies and their implications for scalable quantum computing. Recorded at IPAM's Tensor Networks Workshop, this talk offers a deep dive into the intersection of tensor networks, NISQ devices, and quantum error correction.

Benjamin Villalonga - Benchmarking NISQ and QEC experiments with tensor networks - IPAM at UCLA