Quantum property estimation sped up by new parallel processing method
KrMM Recurrences: HPC-Enhanced Estimators for Nonlinear Classical Shadows
Distributed, Parallel, and Cluster ComputingEmerging Technologies
Summary
Measuring quantum systems is tricky because looking at them changes them, so it’s hard to learn many things at once. The authors study a method called classical shadows, which helps estimate many properties from random measurements. They find a pattern in some hard-to-compute estimates, letting them split the calculation in a way that computers can run much faster using parallel processing. This makes it quicker to estimate certain complex quantum properties, which could help in developing and testing quantum hardware.
What this means in practice
- •For quantum hardware engineers: Speed up estimation of complex quantum state properties during device testing using a parallelized classical shadows approach.
- •For high performance computing teams: Optimize computation of nonlinear estimators in quantum software by exploiting structure for SIMD vectorization and parallel scaling.
Authors
Matthias Gruber, Sabrina Herbst, Ivona Brandic, Vincenzo De Maio
Abstract
While quantum technologies are rapidly evolving, near-term applications heavily rely on hybrid quantum-classical paradigms. Among these, the classical shadows framework has gained a lot of traction, not only for quantum information science, but also for the development and verification of quantum hardware. It allows to efficiently classically estimate many properties of quantum states from randomized measurements alone, a task that is otherwise intractable due to the destructive nature of measurements in quantum systems. There are, however, many properties of interest that are, due to their very problem formulation, not efficiently estimable. In this work, we identify a computational kernel occurring in estimators of polynomial functions, a class of problems that are in general computationally difficult to solve. We show that this structure allows a row-wise partition that, together with a custom SIMD vectorization, enables massive parallelization and can reduce the computational cost significantly. We show applicability and scalability of the approach on different tasks, and our results showcase that the approach allows high sequential throughput and close-to-ideal parallel scaling. The work thus provides a new way of identifying and exploiting structure in classical shadow estimators, resulting in practical advantages on inherently difficult problems.