16 citations · 60 across the 23 of their papers we have counts for
24 papers
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…
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…
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…
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…
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…
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…
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…
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…
Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networks
Alexander Mattick, Maniraman Periyasamy, Christian Ufrecht +4
Quantum computing is currently strongly limited by the impact of noise, in particular introduced by the application of two-qubit gates. For this reason, reducing the number of two-…
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…
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…
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…
Fourier Analysis of Variational Quantum Circuits for Supervised Learning
Marco Wiedmann, Maniraman Periyasamy, Daniel D. Scherer
VQC can be understood through the lens of Fourier analysis. It is already well-known that the function space represented by any circuit architecture can be described through a trun…
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…
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,…