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20232026
most citedOptimal joint cutting of two-qubit rotation gates

16 citations · 60 across the 23 of their papers we have counts for

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quant-ph2026

Scalable Circuit Cutting: A Framework for Combined Gate and Wire Cuts Using Gate Groups

Fiona Jiali Fröhler, Yannick Stade, Christian Ufrecht +2

Quantum circuit cutting enables the execution of large circuits on devices with a limited number of qubits by partitioning circuits into independent subcircuits. However, this intr…

quant-ph2026

A Transferable Machine Learning Approach to Predict Optimized Orbitals for Electronic Structure Problems

Lucas van der Horst, Maniraman Periyasamy, Abhishek Y. Dubey +3

Variational quantum eigensolver ansätze hold considerable promise for ground-state energy calculations on near-term quantum hardware, yet most promising ansatz designs currently st…

quant-ph2026

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-…

quant-ph2026

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…

quant-ph2026

Optimized Compilation of Logical Clifford Circuits

Alexander Popov, Nico Meyer, Daniel D. Scherer +1

Fault-tolerant quantum computing hinges on efficient logical compilation, in particular, translating high-level circuits into code-compatible implementations. Gate-by-gate compilat…

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

CutReg: A loss regularizer for enhancing the scalability of QML via adaptive circuit cutting

Maniraman Periyasamy, Christian Ufrecht, Daniel D. Scherer +1

Whether QML can offer a transformative advantage remains an open question. The severe constraints of NISQ hardware, particularly in circuit depth and connectivity, hinder both the…

quant-ph2025★ 2 cited

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…

quant-ph2025★ 1 cited

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-ph2025★ 3 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★ 1 cited

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…

quant-ph2024

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…

quant-ph2024

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…

quant-ph2024★ 3 cited

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…

quant-ph2024★ 9 cited

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…

quant-ph2024★ 10 cited

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…

quant-ph2024★ 8 cited

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…

quant-ph2024★ 1 cited

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…

quant-ph2024★ 2 cited

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…

quant-ph2024★ 1 cited

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…

quant-ph2023★ 16 cited

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,…