32 citations · 200 across the 28 of their papers we have counts for
22 papers · 1 filter
Learning Logical Operations for Arbitrary Quantum Error Correction Codes
Nico Meyer, Christopher Mutschler, Dominik Seuß +2
Logical operations are essential for quantum computation within quantum error-correcting codes. However, discovering their physical realizations is challenging, especially for non-…
Learning to Concatenate Quantum Codes
Nico Meyer, Christopher Mutschler, Dominik Seuß +2
Concatenating quantum error correction codes scales error correction capability by driving logical error rates down double-exponentially across levels. However, the noise structure…
Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
Nico Meyer, Christopher Mutschler, Andreas Maier +1
Quantum error correction is crucial for protecting quantum information against decoherence. Traditional codes like the surface code require substantial overhead, making them imprac…
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…
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…
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…
Quantum Wasserstein Compilation: Unitary Compilation using the Quantum Earth Mover's Distance
Marvin Richter, Abhishek Y. Dubey, Axel Plinge +3
Despite advances in the development of quantum computers, the practical application of quantum algorithms requiring deep circuit depths or high-fidelity transformations remains out…
Improving Quantum and Classical Decomposition Methods for Vehicle Routing
Laura S. Herzog, Friedrich Wagner, Christian Ufrecht +4
Quantum computing is a promising technology to address combinatorial optimization problems, for example via the quantum approximate optimization algorithm (QAOA). Its potential, ho…
SCIM MILQ: An HPC Quantum Scheduler
Philipp Seitz, Manuel Geiger, Christian Ufrecht +4
With the increasing sophistication and capability of quantum hardware, its integration, and employment in high performance computing (HPC) infrastructure becomes relevant. This ope…
Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule
Maniraman Periyasamy, Axel Plinge, Christopher Mutschler +2
The study of variational quantum algorithms (VQCs) has received significant attention from the quantum computing community in recent years. These hybrid algorithms, utilizing both…
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…
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…
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…
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…
BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading
Maniraman Periyasamy, Marc Hölle, Marco Wiedmann +3
Deep reinforcement learning (DRL) often requires a large number of data and environment interactions, making the training process time-consuming. This challenge is further exacerba…
An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients
Marco Wiedmann, Marc Hölle, Maniraman Periyasamy +5
VQA have attracted a lot of attention from the quantum computing community for the last few years. Their hybrid quantum-classical nature with relatively shallow quantum circuits ma…
Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning
Nico Meyer, Daniel D. Scherer, Axel Plinge +2
Reinforcement learning is a growing field in AI with a lot of potential. Intelligent behavior is learned automatically through trial and error in interaction with the environment.…
Cutting multi-control quantum gates with ZX calculus
Christian Ufrecht, Maniraman Periyasamy, Sebastian Rietsch +3
Circuit cutting, the decomposition of a quantum circuit into independent partitions, has become a promising avenue towards experiments with larger quantum circuits in the noisy-int…
Quantum Policy Gradient Algorithm with Optimized Action Decoding
Nico Meyer, Daniel D. Scherer, Axel Plinge +2
Quantum machine learning implemented by variational quantum circuits (VQCs) is considered a promising concept for the noisy intermediate-scale quantum computing era. Focusing on ap…
A Survey on Quantum Reinforcement Learning
Nico Meyer, Christian Ufrecht, Maniraman Periyasamy +3
Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. While we intend to provide a broad overview of the literature on…
Incremental Data-Uploading for Full-Quantum Classification
Maniraman Periyasamy, Nico Meyer, Christian Ufrecht +3
The data representation in a machine-learning model strongly influences its performance. This becomes even more important for quantum machine learning models implemented on noisy i…
Uncovering Instabilities in Variational-Quantum Deep Q-Networks
Maja Franz, Lucas Wolf, Maniraman Periyasamy +5
Deep Reinforcement Learning (RL) has considerably advanced over the past decade. At the same time, state-of-the-art RL algorithms require a large computational budget in terms of t…