Reinforcement Learning Assisted Quantum Simulation of Many-Body Excited States and Real-Time Dynamics

Researchers have successfully extended the reinforcement learning contracted quantum eigensolver (RL-CQE) method to simulate many-body excited states and real-time dynamics of quantum systems¹. This breakthrough enables the computation of electronic excited states and quantum dynamics in many-fermion systems, a crucial application for near-term quantum computing. By leveraging deep Q-networks, the RL-CQE method can efficiently approximate quantum wavefunctions, making it a promising tool for simulating complex quantum phenomena. The ability to simulate real-time dynamics is particularly significant, as it can help scientists better understand the behavior of quantum systems under various conditions. As quantum computing advances, the need for post-quantum cryptography (PQC) migration becomes increasingly urgent, and developments like this reinforce the importance of planning for a quantum-secure future. The integration of reinforcement learning with quantum simulation narrows the timeline for cryptographic migration, emphasizing the need for swift action to ensure the long-term security of sensitive data.