6 papers · 2 filters
Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization
Nico Meyer, Julian Berberich, Christopher Mutschler +1
Quantum machine learning leverages quantum computing to enhance accuracy and reduce model complexity compared to classical approaches, promising significant advancements in various…
Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning
Sebastian Rietsch, Abhishek Y. Dubey, Christian Ufrecht +4
This paper presents a deep reinforcement learning approach for synthesizing unitaries into quantum circuits. Unitary synthesis aims to identify a quantum circuit that represents a…
Comprehensive Library of Variational LSE Solvers
Nico Meyer, Martin Röhn, Jakob Murauer +3
Linear systems of equations can be found in various mathematical domains, as well as in the field of machine learning. By employing noisy intermediate-scale quantum devices, variat…
Warm-Start Variational Quantum Policy Iteration
Nico Meyer, Jakob Murauer, Alexander Popov +4
Reinforcement learning is a powerful framework aiming to determine optimal behavior in highly complex decision-making scenarios. This objective can be achieved using policy iterati…
Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networks
Nico Meyer, Christian Ufrecht, Maniraman Periyasamy +4
Quantum computer simulation software is an integral tool for the research efforts in the quantum computing community. An important aspect is the efficiency of respective frameworks…
Optimal joint cutting of two-qubit rotation gates
Christian Ufrecht, Laura S. Herzog, Daniel D. Scherer +4
Circuit cutting, the partitioning of quantum circuits into smaller independent fragments, has become a promising avenue for scaling up current quantum-computing experiments. Here,…