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20082026
most citedScrambling and thermalization in a diffusive quantum many-body system

229 citations · 2.1k across the 110 of their papers we have counts for

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Showing 2022 · quant-phShow all

6 papers · 2 filters

quant-ph2022★ 4 cited

Quantum Reinforcement Learning for Solving a Stochastic Frozen Lake Environment and the Impact of Quantum Architecture Choices

Theodora-Augustina Drăgan, Maureen Monnet, Christian B. Mendl +1

Quantum reinforcement learning (QRL) models augment classical reinforcement learning schemes with quantum-enhanced kernels. Different proposals on how to construct such models empi…

quant-ph2022★ 11 cited

Riemannian quantum circuit optimization for Hamiltonian simulation

Ayse Kotil, Rahul Banerjee, Qunsheng Huang +1

Hamiltonian simulation, i.e., simulating the real time evolution of a target quantum system, is a natural application of quantum computing. Trotter-Suzuki splitting methods can gen…

quant-ph2022★ 1 cited

On the Optimal Linear Contraction Order of Tree Tensor Networks, and Beyond

Mihail Stoian, Richard Milbradt, Christian B. Mendl

The contraction cost of a tensor network depends on the contraction order. However, the optimal contraction ordering problem is known to be NP-hard. We show that the linear contrac…

quant-ph2022★ 4 cited

Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learning

Simon Wiedemann, Daniel Hein, Steffen Udluft +1

We present a full implementation and simulation of a novel quantum reinforcement learning method. Our work is a detailed and formal proof of concept for how quantum algorithms can…

quant-ph2022★ 22 cited

Simulating quantum circuits using tree tensor networks

Philipp Seitz, Ismael Medina, Esther Cruz +2

We develop and analyze a method for simulating quantum circuits on classical computers by representing quantum states as rooted tree tensor networks. Our algorithm first determines…

quant-ph2022★ 7 cited

Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework

Raúl Berganza Gómez, Corey O'Meara, Giorgio Cortiana +2

The learning process of classical machine learning algorithms is tuned by hyperparameters that need to be customized to best learn and generalize from an input dataset. In recent y…