Speeding-up the decision making of a learning agent using an ion trap quantum processor

TL;DR

This paper demonstrates a quantum speed-up in decision-making for a learning agent using a small-scale ion trap quantum processor, showing quadratic improvement over classical agents in deliberation time.

Contribution

First experimental implementation of a quantum learning agent with ion trap technology, achieving quadratic speed-up in decision-making process.

Findings

Quadratic reduction in deliberation time compared to classical agents

Successful implementation of quantum decision-making in a two-qubit system

Potential for scalable quantum machine learning applications

Abstract

We report a proof-of-principle experimental demonstration of the quantum speed-up for learning agents utilizing a small-scale quantum information processor based on radiofrequency-driven trapped ions. The decision-making process of a quantum learning agent within the projective simulation paradigm for machine learning is implemented in a system of two qubits. The latter are realized using hyperfine states of two frequency-addressed atomic ions exposed to a static magnetic field gradient. We show that the deliberation time of this quantum learning agent is quadratically improved with respect to comparable classical learning agents. The performance of this quantum-enhanced learning agent highlights the potential of scalable quantum processors taking advantage of machine learning.