Quantum Architecture Search via Continual Reinforcement Learning

Understand

Quantum computing has promised significant improvement in solving difficult computational tasks over classical computers.

• Designing quantum circuits for practical use, however, is not a trivial objective and requires expert-level knowledge.
• To aid this endeavor, this paper proposes a machine learning-based method to construct quantum circuit architectures.
• Previous works have demonstrated that classical deep reinforcement learning (DRL) algorithms can successfully construct quantum circuit architectures without encoded physics knowledge.