83 citations · 152 across the 11 of their papers we have counts for
11 papers
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…
Taming quantum systems: A tutorial for using shortcuts-to-adiabaticity, quantum optimal control, and reinforcement learning
Callum W. Duncan, Pablo M. Poggi, Marin Bukov +2
Precise manipulation of quantum effects at the atomic and nanoscale has become an essential task in ongoing scientific and technological endeavours. Quantum control methods are thu…
Topological Phase Transitions in a Constrained Two-Qubit Quantum Control Landscape
Nicolò Beato, Pranay Patil, Marin Bukov
In optimal quantum control, control landscape phase transitions (CLPTs) indicate sharp changes occurring in the set of optimal protocols, as a physical model parameter is varied. H…
Toward a Theory of Phase Transitions in Quantum Control Landscapes
Nicolò Beato, Pranay Patil, Marin Bukov
Control landscape phase transitions (CLPTs) occur as abrupt changes in the cost function landscape upon varying a control parameter, and can be revealed by non-analytic points in s…
Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits
Jiahao Yao, Haoya Li, Marin Bukov +2
Variational quantum algorithms stand at the forefront of simulations on near-term and future fault-tolerant quantum devices. While most variational quantum algorithms involve only…
Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks
Jiahao Yao, Paul Köttering, Hans Gundlach +2
Variational quantum eigensolvers have recently received increased attention, as they enable the use of quantum computing devices to find solutions to complex problems, such as the…
Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
Jiahao Yao, Lin Lin, Marin Bukov
The quantum alternating operator ansatz (QAOA) is a prominent example of variational quantum algorithms. We propose a generalized QAOA called CD-QAOA, which is inspired by the coun…
Policy Gradient based Quantum Approximate Optimization Algorithm
Jiahao Yao, Marin Bukov, Lin Lin
The quantum approximate optimization algorithm (QAOA), as a hybrid quantum/classical algorithm, has received much interest recently. QAOA can also be viewed as a variational ansatz…