Work

Towards Faster Reinforcement Learning of Quantum Circuit Optimisation: Exponential Reward Functions

Year: 2023

Type: conference-paper

Abstract: Reinforcement learning for the optimization of quantum circuits uses an agent whose goal is to maximize the value of a reward function that decides what is correct and what is wrong during the exploration of the search space. It is an open problem how to formulate reward functions that lead to fast and efficient learning. We propose an exponential reward function which is sensitive to structural properties of the circuit. We benchmark our function on circuits with known optimal depths, and conclude that our function is reducing the learning time and improves the optimization. Our results are a next step towards fast, large scale optimization of quantum circuits.

Source: IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH)

DOI: 10.1145/3611315.3633259

Authors: Ioana Moflic, Alexandru Paler

Institution: Aalto University

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: Nvidia, Advanced Research Projects Agency

OpenAlex ID: W4388962824

FWCI: 0.355

Cites: 2

Cited by: 2

Related to: 10