Quantum-enhanced deliberation of learning agents using trapped ions

TL;DR

This paper proposes a method to implement quantum-enhanced learning agents using trapped ions, demonstrating how classical agents can be upgraded to quantum versions with potential speed-ups and robustness in noisy environments.

Contribution

It introduces a modular architecture for quantum-enhanced learning agents in ion traps, utilizing nested coherent controlization to upgrade classical agents to quantum counterparts.

Findings

Numerical simulations show robustness under noise models.

The architecture enables flexible implementation of quantum agents.

Quantum walks provide potential speed-up over classical counterparts.

Abstract

A scheme that successfully employs quantum mechanics in the design of autonomous learning agents has recently been reported in the context of the projective simulation (PS) model for artificial intelligence. In that approach, the key feature of a PS agent, a specific type of memory which is explored via random walks, was shown to be amenable to quantization. In particular, classical random walks were substituted by Szegedy-type quantum walks, allowing for a speed-up. In this work we propose how such classical and quantum agents can be implemented in systems of trapped ions. We employ a generic construction by which the classical agents are `upgraded' to their quantum counterparts by nested coherent controlization, and we outline how this construction can be realized in ion traps. Our results provide a flexible modular architecture for the design of PS agents. Furthermore, we present numerical simulations of simple PS agents which analyze the robustness of our proposal under certain noise models.