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.
Highlights
The past decade has seen the parallel advance of two research areas—quantum computation [1] and artificial intelligence [2]—from abstract theory to practical applications and commercial use
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 second set of measurements studies the behavior of the output probability ratio rf = b00 b01 as a function of input probability ratio ri = a00 a01
Summary
The past decade has seen the parallel advance of two research areas—quantum computation [1] and artificial intelligence [2]—from abstract theory to practical applications and commercial use. Artificial intelligence and machine learning have become integral parts of modern automated devices using classical processors [7,8,9,10] Despite this seemingly simultaneous emergence and promise to shape future technological developments, the overlap between these areas still offers a number of unexplored problems [11, 12]. The learning aspect is manifest in the reinforcement of the connections between the inputs and actions, where the correct association is (often implicitly) specified by a reward mechanism, which may be external to the agent In this very general context, an approach to explore the intersection of quantum computing and artificial intelligence is to equip autonomous learning agents with quantum processors for their
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