TITLE:
Training a Quantum Neural Network to Solve the Contextual Multi-Armed Bandit Problem
AUTHORS:
Wei Hu, James Hu
KEYWORDS:
Continuous-Variable Quantum Computers, Quantum Machine Learning, Quantum Reinforcement Learning, Contextual Multi-Armed Bandit Problem
JOURNAL NAME:
Natural Science,
Vol.11 No.1,
January
18,
2019
ABSTRACT: Artificial
intelligence has permeated all aspects of our lives today. However, to make AI
behave like real AI, the critical bottleneck lies in the speed of computing.
Quantum computers employ the peculiar and unique properties of quantum states
such as superposition, entanglement, and interference to process information in
ways that classical computers cannot. As a new paradigm of computation, quantum
computers are capable of performing tasks intractable for classical processors,
thus providing a quantum leap in AI research and making the development of real
AI a possibility. In this regard, quantum machine learning not only enhances
the classical machine learning approach but more importantly it provides an
avenue to explore new machine learning models that have no classical
counterparts. The qubit-based quantum computers cannot naturally represent the
continuous variables commonly used in machine learning, since the measurement
outputs of qubit-based circuits are generally discrete. Therefore, a
continuous-variable (CV) quantum architecture based on a photonic quantum computing
model is selected for our study. In this work, we employ machine learning and
optimization to create photonic quantum circuits that can solve the contextual
multi-armed bandit problem, a problem in the domain of reinforcement learning,
which demonstrates that quantum reinforcement learning algorithms can be
learned by a quantum device.