activity
20242026
collaborators

9 papers

cs.LG2026

Illustration of Barren Plateaus in Quantum Computing

Gerhard Stenzel, Tobias Rohe, Michael Kölle +3

Variational Quantum Circuits (VQCs) have emerged as a promising paradigm for quantum machine learning in the NISQ era. While parameter sharing in VQCs can reduce the parameter spac…

quant-ph2025

Quantum Optimization Algorithms

Jonas Stein, Maximilian Zorn, Leo Sünkel +1

Quantum optimization allows for up to exponential quantum speedups for specific, possibly industrially relevant problems. As the key algorithm in this field, we motivate and discus…

quant-ph2025

Evaluating Variational Quantum Circuit Architectures for Distributed Quantum Computing

Leo Sünkel, Jonas Stein, Jonas Nüßlein +2

Scaling quantum computers, i.e., quantum processing units (QPUs) to enable the execution of large quantum circuits is a major challenge, especially for applications that should pro…

quant-ph2025

Evolutionary-Based Circuit Optimization for Distributed Quantum Computing

Leo Sünkel, Jonas Stein, Gerhard Stenzel +3

In this work, we evaluate an evolutionary algorithm (EA) to optimize a given circuit in such a way that it reduces the required communication when executed in the Distributed Quant…

quant-ph2025

The Questionable Influence of Entanglement in Quantum Optimisation Algorithms

Tobias Rohe, Daniëlle Schuman, Jonas Nüßlein +3

The performance of the Variational Quantum Eigensolver (VQE) is promising compared to other quantum algorithms, but also depends significantly on the appropriate design of the unde…

quant-ph2025

Time-Aware Qubit Assignment and Circuit Optimization for Distributed Quantum Computing

Leo Sünkel, Jonas Stein, Maximilian Zorn +2

The emerging paradigm of distributed quantum computing promises a potential solution to scaling quantum computing to currently unfeasible dimensions. While this approach itself is…