Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations
This addresses the problem of automated quantum circuit design for researchers in quantum computing, but it appears incremental as it applies existing reinforcement learning methods to this domain.
The paper tackles the challenge of automated quantum circuit design in the NISQ era by using Q-learning and DQN algorithms, aiming to provide an automatic and scalable approach over traditional heuristic methods, though no concrete results or numbers are reported.
Quantum computing promises advantages over classical computing. The manufacturing of quantum hardware is in the infancy stage, called the Noisy Intermediate-Scale Quantum (NISQ) era. A major challenge is automated quantum circuit design that map a quantum circuit to gates in a universal gate set. In this paper, we present a generic MDP modeling and employ Q-learning and DQN algorithms for quantum circuit design. By leveraging the power of deep reinforcement learning, we aim to provide an automatic and scalable approach over traditional hand-crafted heuristic methods.