most citedBenchmarking Quantum Reinforcement Learning

3 citations · 3 across the 2 of their papers we have counts for

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quant-ph2025

Optimized Circuit Cutting for QAOA Sampling Tasks

Friedrich Wagner, Christian Ufrecht, Martin Braun +1

Circuit cutting was originally designed to retrieve the expectation value of an observable with respect to a large quantum circuit by executing smaller circuit fragments. In this w…

quant-ph2025

BenchQC -- Scalable and modular benchmarking of industrial quantum computing applications

Florian Geissler, Eric Stopfer, Christian Ufrecht +19

We present BenchQC, a research project funded by the state of Bavaria, which promotes an application-centric perspective for benchmarking real-world quantum applications. Diverse u…

quant-ph20253 cited

Benchmarking Quantum Reinforcement Learning

Georg Kruse, Rodrigo Coelho, Andreas Rosskopf +2

Quantum Reinforcement Learning (QRL) has emerged as a promising research field, leveraging the principles of quantum mechanics to enhance the performance of reinforcement learning…

quant-ph2025

Joint Cutting for Hybrid Schrödinger-Feynman Simulation of Quantum Circuits

Laura S. Herzog, Lukas Burgholzer, Christian Ufrecht +2

Despite the continuous advancements in size and robustness of real quantum devices, reliable large-scale quantum computers are not yet available. Hence, classical simulation of qua…

quant-ph2025

Benchmarking Quantum Reinforcement Learning

Nico Meyer, Christian Ufrecht, George Yammine +3

Benchmarking and establishing proper statistical validation metrics for reinforcement learning (RL) remain ongoing challenges, where no consensus has been established yet. The emer…