Demonstration of quantum projective simulation on a single-photon-based quantum computer
arXiv:2404.12729 · doi:10.1103/PhysRevA.110.062613
Abstract
Variational quantum algorithms show potential in effectively operating on noisy intermediate-scale quantum devices. A novel variational approach to reinforcement learning has been recently proposed, incorporating linear-optical interferometers and a classical learning model known as projective simulation (PS). PS is a decision-making tool for reinforcement learning and can be classically represented as a random walk on a graph that describes the agent's memory. In its optical quantum version, this approach utilizes quantum walks of single photons on a mesh of tunable beamsplitters and phase shifters to select actions. In this work, we present the implementation of this algorithm on Ascella, a single-photon-based quantum computer from Quandela. The focus is drawn on solving a test bed task to showcase the potential of the quantum agent with respect to the classical agent.
10 pages, 6 figures, 1 table
References in corpus (15)
- Quantum Machine Learning
- Variational Quantum Algorithms
- Parameterized quantum circuits as machine learning models
- Challenges and Opportunities in Quantum Machine Learning
- Quantum speedup for active learning agents
- Simulating boson sampling in lossy architectures
- Hong-Ou-Mandel Interference with Imperfect Single Photon Sources
- Basic protocols in quantum reinforcement learning with superconducting circuits
- Perceval: A Software Platform for Discrete Variable Photonic Quantum Computing
- Coherent controlization using superconducting qubits
- Projective simulation for classical learning agents: a comprehensive investigation
- Quantum-enhanced deliberation of learning agents using trapped ions
- Speeding-up the decision making of a learning agent using an ion trap quantum processor
- Photonic architecture for reinforcement learning
- Strong Simulation of Linear Optical Processes