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20172026
most citedReinforcement Learning assisted Quantum Optimization

71 citations · 309 across the 21 of their papers we have counts for

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11 papers · 1 filter

quant-ph2026

Engineering of non-Hermitian interactions in digital qudit quantum simulators

Matteo M. Wauters, Paolo Boschetto, Edoardo Ballini +2

Non-Hermitian Hamiltonians are a fascinating class of many-body models that describe the effective dynamics of quantum systems interacting with the environment through particle, en…

quant-ph2025

Quantum Resources in Non-Abelian Lattice Gauge Theories: Nonstabilizerness, Multipartite Entanglement, and Fermionic Non-Gaussianity

Gopal Chandra Santra, Julius Mildenberger, Edoardo Ballini +3

Lattice gauge theories (LGTs) represent one of the most ambitious goals of quantum simulation. From a practical implementation perspective, non-Abelian theories present significant…

quant-ph2024★ 3 cited

Symmetry verification for noisy quantum simulations of non-Abelian lattice gauge theories

Edoardo Ballini, Julius Mildenberger, Matteo M. Wauters +1

Non-Abelian gauge theories underlie our understanding of fundamental forces of modern physics. Simulating them on quantum hardware is an outstanding challenge in the rapidly evolvi…

quant-ph2024★ 3 cited

A Monte Carlo Tree Search approach to QAOA: finding a needle in the haystack

Andoni Agirre, Evert Van Nieuwenburg, Matteo M. Wauters

The search for quantum algorithms to tackle classical combinatorial optimization problems has long been one of the most attractive yet challenging research topics in quantum comput…

quant-ph2024★ 1 cited

Symmetry-protection Zeno phase transition in monitored lattice gauge theories

Matteo M. Wauters, Edoardo Ballini, Alberto Biella +1

Quantum measurements profoundly influence system dynamics. They lead to complex nonequilibrium phenomena like the quantum Zeno effect, and they can be used for mitigating errors in…

quant-ph2022★ 5 cited

Reusability Report: Comparing gradient descent and monte carlo tree search optimization of quantum annealing schedules

Matteo M. Wauters, Evert van Nieuwenburg

We provide a reusability report of the method presented by Chen et al. in "Optimizing quantum annealing schedules with Monte Carlo tree search enhanced with neural networks" and ad…

quant-ph2021★ 35 cited

Two-dimensional lattice gauge theory on a near-term quantum simulator: variational quantum optimization, confinement, and topological order

Luca Lumia, Pietro Torta, Glen B. Mbeng +4

We propose an implementation of a two-dimensional lattice gauge theory model on a shallow quantum circuit, involving a number of single and two-qubits gates comparab…

quant-ph2021★ 64 cited

Symmetries and conserved quantities of boundary time crystals in generalized spin models

Giulia Piccitto, Matteo Wauters, Franco Nori +1

We investigate how symmetries and conserved quantities relate to the occurrence of the boundary time crystal (BTC) phase in a generalized spin model with Lindblad dissipation. BTCs…

quant-ph2020★ 71 cited

Reinforcement Learning assisted Quantum Optimization

Matteo M. Wauters, Emanuele Panizon, Glen B. Mbeng +1

We propose a reinforcement learning (RL) scheme for feedback quantum control within the quan-tum approximate optimization algorithm (QAOA). QAOA requires a variational minimization…

quant-ph2020★ 7 cited

Polynomial scaling of QAOA for ground-state preparation of the fully-connected p-spin ferromagnet

Matteo M. Wauters, Glen Bigan Mbeng, Giuseppe E. Santoro

We show that the quantum approximate optimization algorithm (QAOA) can construct with polynomially scaling resources the ground state of the fully-connected p-spin Ising ferromagne…

quant-ph2017★ 28 cited

Direct comparison of quantum and simulated annealing on a fully-connected Ising ferromagnet

Matteo M. Wauters, Rosario Fazio, Hidetoshi Nishimori +1

We compare the performance of quantum annealing (QA, through Schrödinger dynamics) and simulated annealing (SA, through a classical master equation) on the -spin infinite range…