39 citations · 60 across the 8 of their papers we have counts for
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Reinforcement Learning to Harness Approximation Errors for Long-Time Quantum Simulation
Yu-Bo Shi, Markus Heyl, Roderich Moessner +2
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-learnin…
Learning effective Hamiltonians for adaptive time-evolution quantum algorithms
Hongzheng Zhao, Ao Chen, Shu-Wei Liu +3
Digital quantum simulation of many-body dynamics relies on Trotterization to decompose the target time evolution into elementary quantum gates operating at a fixed equidistant time…
Adaptive Trotterization for time-dependent Hamiltonian quantum dynamics using piecewise conservation laws
Hongzheng Zhao, Marin Bukov, Markus Heyl +1
Digital quantum simulation relies on Trotterization to discretize time evolution into elementary quantum gates. On current quantum processors with notable gate imperfections, there…