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

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.

21 pages, 7 figures, 2 tables. Author names now spelled correctly; sections rearranged; changes in the wording of the manuscript

References in corpus (11)

• Quantum Machine Learning
• Demonstration of Two-Qubit Algorithms with a Superconducting Quantum Processor
• Quantum-enhanced machine learning
• Active learning machine learns to create new quantum experiments
• Experimental Realization of Quantum Artificial Intelligence
• Complete 3-Qubit Grover Search on a Programmable Quantum Computer
• Arbitrarily Accurate Pulse Sequences for Robust Dynamical Decoupling
• Flexible resources for quantum metrology
• Supervised Quantum Learning without Measurements
• State selective detection of hyperfine qubits
• Radio-frequency sideband cooling and sympathetic cooling of trapped ions in a static magnetic field gradient

Cited by in corpus (17)

• Experimental quantum speed-up in reinforcement learning agents
• Machine learning for long-distance quantum communication
• Quantum Deep Reinforcement Learning for Robot Navigation Tasks
• Revealing quantum chaos with machine learning
• Universal Composite Pulses for Efficient Population Inversion with an Arbitrary Excitation Profile
• Efficient and robust signal sensing by sequences of adiabatic chirped pulses
• Quantum reinforcement learning in continuous action space
• High-fidelity composite quantum gates for Raman qubits
• A hybrid classical-quantum approach to speed-up Q-learning
• How to enhance quantum generative adversarial learning of noisy information
• Adaptive Bayesian phase estimation for quantum error correcting codes
• Robust Two-Qubit Gates Using Pulsed Dynamical Decoupling
• Robust Noise Suppression and Quantum Sensing by Continuous Phased Dynamical Decoupling
• Learning entanglement breakdown as a phase transition by confusion
• Performance analysis of a hybrid agent for quantum-accessible reinforcement learning
• Quantum-accessible reinforcement learning beyond strictly epochal environments
• Demonstration of quantum projective simulation on a single-photon-based quantum computer