Adaptive quantum computation in changing environments using projective simulation

TL;DR

This paper demonstrates how an intelligent agent with a projective simulator can adapt measurement strategies in quantum computing to counteract external noise, improving robustness in dynamic environments.

Contribution

It introduces an adaptive control method using an intelligent agent with a projective simulator for measurement-based quantum computation under noise.

Findings

Agent effectively adapts to static and dynamic stray fields

Improved performance in quantum algorithms with adaptive measurement control

Path laid for intelligent adaptive controllers in quantum information processing

Abstract

Quantum information processing devices need to be robust and stable against external noise and internal imperfections to ensure correct operation. In a setting of measurement-based quantum computation, we explore how an intelligent agent endowed with a projective simulator can act as controller to adapt measurement directions to an external stray field of unknown magnitude in a fixed direction. We assess the agent's learning behavior in static and time-varying fields and explore composition strategies in the projective simulator to improve the agent's performance. We demonstrate the applicability by correcting for stray fields in a measurement-based algorithm for Grover's search. Thereby, we lay out a path for adaptive controllers based on intelligent agents for quantum information tasks.