Speeding-up the decision making of a learning agent using an ion trap quantum processor
This proof-of-principle demonstration addresses the challenge of computational efficiency in machine learning for potential applications in scalable quantum computing, though it is incremental due to the small-scale experimental setup.
The researchers tackled the problem of speeding up decision-making in learning agents by implementing a quantum learning agent using a two-qubit ion trap quantum processor, achieving a quadratic improvement in deliberation time compared to classical agents.
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.