TITLE:
Empirical Analysis of Decision Making of an AI Agent on IBM’s 5Q Quantum Computer
AUTHORS:
Wei Hu
KEYWORDS:
Quantum Computation, Quantum Machine Learning, Quantum Reinforcement Learning, Quantum Circuit
JOURNAL NAME:
Natural Science,
Vol.10 No.1,
January
30,
2018
ABSTRACT: A recent work has shown that using an ion trap quantum processor
can speed up the decision making of a reinforcement learning agent. Its quantum
advantage is observed when the external environment changes, and then agent
needs to relearn again. One character of this quantum hardware system
discovered in this study is that it tends to overestimate the values used to
determine the actions the agent will take. IBM’s five qubit superconducting quantum
processor is a popular quantum platform. The aims of our study are twofold.
First we want to identify the hardware characteristic features of IBM’s 5Q
quantum computer when running this learning agent, compared with the ion trap
processor. Second, through careful analysis, we observe that the quantum
circuit employed in the ion trap processor for this agent could be simplified.
Furthermore, when tested on IBM’s 5Q quantum processor, our simplified circuit
demonstrates its enhanced performance over the original circuit on one of the
hard learning tasks investigated in the previous work. We also use IBM’s
quantum simulator when a good baseline is needed to compare the performances.
As more and more quantum hardware devices are moving out of the laboratory and
becoming generally available to public use, our work emphasizes the fact that
the features and constraints of the quantum hardware could take a toll on the
performance of quantum algorithms.