83 citations · 152 across the 11 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…
(Non-)Traversable Quantum Phase Transitions
Federico Balducci, Paul M. Schindler, Andrea Solfanelli +1
Quantum phase transitions manifest as an abrupt change in the ground state of a many-body system; yet it is an open question whether this sudden change necessarily precludes a cont…
Protecting Quantum Simulations of Lattice Gauge Theories through Engineered Emergent Hierarchical Symmetries
Zhanpeng Fu, Wei Zheng, Roderich Moessner +2
We present a strategy for the quantum simulation of many-body lattice models with constrained Hilbert spaces. We focus on lattice gauge theories (LGTs), which underlie a wide range…
Reinforcement Learning for Quantum Technology
Marin Bukov, Florian Marquardt
Many challenges arising in Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL), based on a…