Marin Bukov

Reinforcement Learning to Harness Approximation Errors for Long-Time Quantum Simulation

Accurate digital quantum simulation at long times is limited by the accumulation of errors inherent to approximate simulation. Here we introduce RL-Trotter, a reinforcement-learning framework that treats unavoidable approximation errors as resources for error correction rather than merely imperfections to suppress. We...