Gate Sequence Optimization for Parameterized Quantum Circuits using Reinforcement Learning

TL;DR

This paper presents a reinforcement learning method to optimize entangling gate sequences in parameterized quantum circuits, improving state preparation fidelity while reducing CNOT gate usage on noisy quantum devices.

Contribution

It introduces a reinforcement learning approach that optimizes entangling gate sequences in parameterized circuits, incorporating single-qubit unitaries for higher fidelity.

Findings

Achieves higher state preparation fidelities with fewer CNOT gates.

Effectively accounts for qubit connectivity in gate sequence optimization.

Extends RL techniques to parameterized gate sets for quantum circuits.

Abstract

Current experimental quantum computing devices are limited by noise, mainly originating from entangling gates. If an efficient gate sequence for an operation is unknown, one often employs layered parameterized quantum circuits, especially hardware-efficient ans\"atze, with fixed entangling layer structures. We demonstrate a reinforcement learning algorithm to improve on these by optimizing the entangling gate sequence in the task of quantum state preparation. This allows us to restrict the required number of CNOT gates while taking the qubit connectivity architecture into account. Recent advancements using reinforcement learning have already demonstrated the power of this technique when optimizing the circuit for a sequence of non-parameterized gates. We extend this approach to parameterized gate sets by incorporating general single-qubit unitaries, thus allowing us to consistently reach higher state preparation fidelities at the same number of CNOT gates compared to a hardware-efficient ansatz.