Benchmarking NISQ and QEC experiments with tensor networks

Benjamin Villalonga
Google Quantum AI

Advances in quantum computing hardware now enable running substantially large experiments, exploring both NISQ applications and demonstrations of quantum error correction (QEC). In this talk, I will present two applications of tensor networks we have recently used within these two contexts. First, I will discuss improvements to a powerful and widely used adversarial method employed to challenge the beyond-classical nature of certain NISQ experiments. This approach involves optimizing tensor network contraction schemes subject to realistic memory and performance constraints imposed by current supercomputers. Second, I will talk about our general-purpose maximum-likelihood tensor network decoder. We have used this optimal decoder to benchmark our recent QEC experiments, achieving the first experimental demonstration of error suppression on a surface code. In addition, we have leveraged this decoder to evaluate the performance of alternative, more scalable decoding strategies.

Presentation (PDF File)

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