Quantum circuit optimization with deep reinforcement learning

Understand

A central aspect for operating future quantum computers is quantum circuit optimization, i.e., the search for efficient realizations of quantum algorithms given the device capabilities.

• In recent years, powerful approaches have been developed which focus on optimizing the high-level circuit structure.
• However, these approaches do not consider and thus cannot optimize for the hardware details of the quantum architecture, which is especially important for near-term devices.
• To address this point, we present an approach to quantum circuit optimization based on reinforcement learning.