40 citations · 132 across the 64 of their papers we have counts for
57 papers · 1 filter
Same Spectrum, Harder First-Order Training: Phase-Locking in Single- versus Multi-Qubit Re-Uploading Circuits
Michael Poppel, David Bucher, Maximilian Zorn +5
Angle-encoded variational quantum circuits implement truncated Fourier series. For one-dimensional inputs, a single-qubit re-uploading (serial) circuit represents every function th…
When Can Quantum Extreme Learning Machines Replace Quantum Reservoirs?
Markus Baumann, Gerhard Stenzel, Jonas Stein +1
Quantum reservoir computers (QRCs) retain information from earlier inputs, whereas reset-window quantum extreme learning machines (QELMs) start afresh and receive only a recent inp…
The Organization of Environmental Coupling Shapes What Quantum Reservoirs Remember
Markus Baumann, Itamar Fink, Johannes Wittmann +2
For an open quantum reservoir, how the system forgets is part of how it computes. Quantum reservoir computing processes input streams with fixed quantum dynamics and trains only a…
Implicit Differentiation for Measurement-Efficient Bilevel Quantum-Classical Optimization
Tobias Rohe, Markus Baumann, Federico Harjes Ruiloba +3
Quantum optimization has shown promising results for quadratic unconstrained binary optimization (QUBO) problems. Real-world applications, however, often involve polynomial coeffic…
Symmetry Alone Is Not an Ansatz: Task-Aligned Interactions in Equivariant Quantum Circuits
Markus Baumann, Claudia Linnhoff-Popien, Jonas Stein
The success of variational quantum learning models crucially depends on choosing parametrizations that reflect the structure of the problem at hand. Symmetries provide one of the c…
Where a Quantum Reservoir Works: A Transferable Operating Band
Markus Baumann, Itamar Fink, Johannes Wittmann +2
In quantum reservoir computing, a fixed quantum system transforms an input signal, while learning reduces to training a simple linear readout on its measured outputs. Since the qua…
Exploiting Symmetry in Quantum Reservoir Computing
Markus Baumann, Michael Poppel, Thomas Gabor +3
Quantum reservoir computing (QRC) uses a quantum processor without training it. The input is encoded into a quantum state, a fixed random circuit evolves it, selected observables a…
Architecture Shape Governs QNN Trainability: Jacobian Null Space Growth and Parameter Efficiency
Michael Poppel, David Bucher, Maximilian Zorn +5
Variational quantum circuits with angle encoding implement truncated Fourier series, and architectures arranging qubits with encoding layers each -- sharing encoding budget…
Parity Supervision as a Driver of Generalization in Quantum Generative Modeling
Markus Baumann, Daniel Hein, Steffen Udluft +3
Generative models learn probability distributions in order to produce new samples beyond a finite training set. Their usefulness therefore depends on assigning probability to valid…
Quantum Optimization Methods for the Generalized Traveling Salesman Problem
Maximilian Zorn, Melinda Braun, Michael Ertl +4
This paper studies quantum optimization baselines for the Generalized Traveling Salesman Problem (GTSP), a clustered routing problem that naturally models variant selection and seq…
From Quantum Shots to Training Data: Reorganizing Measurement Records in Quantum Machine Learning
Markus Baumann, Maximilian Zorn, Thomas Gabor +2
Between keeping every shot as a noisy training example and averaging all shots into one clean feature lies an entire spectrum of data organizations that quantum machine learning us…
Constrained Quantum Optimization via Iterative Warm-Start XY-Mixers
David Bucher, Maximilian Janetschek, Michael Poppel +3
The Quantum Approximate Optimization Algorithm (QAOA) is a leading hybrid heuristic for combinatorial optimization, but efficiently handling hard constraints remains a significant…
Quantum Computing -- Strategic Recommendations for the Industry
Marvin Erdmann, Lukas Karch, Abhishek Awasthi +9
This whitepaper surveys the current landscape and short- to mid-term prospects for quantum-enabled optimization and machine learning use cases in industrial settings. Grounded in t…
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…
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…
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…
From Classical Data to Quantum Advantage -- Quantum Policy Evaluation on Quantum Hardware
Daniel Hein, Simon Wiedemann, Markus Baumann +7
Quantum policy evaluation (QPE) is a reinforcement learning (RL) algorithm which is quadratically more efficient than an analogous classical Monte Carlo estimation. It makes use of…
Quantum Computer Benchmarking: An Explorative Systematic Literature Review
Tobias Rohe, Federico Harjes Ruiloba, Sabrina Egger +3
As quantum computing (QC) continues to evolve in hardware and software, measuring progress in this complex and diverse field remains a challenge. To track progress, uncover bottlen…
Mitigating Exponential Mixed Frequency Growth through Frequency Selection
Michael Poppel, David Bucher, Maximilian Zorn +4
Angle encoding has emerged as a popular feature map for embedding classical data into quantum models, naturally generating truncated Fourier series with universal function approxim…
Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification
Daniëlle Schuman, Mark V. Seebode, Tobias Rohe +5
Exploiting the fact that samples drawn from a quantum annealer inherently follow a Boltzmann-like distribution, annealing-based Quantum Boltzmann Machines (QBMs) have gained increa…
Efficient QAOA Architecture for Solving Multi-Constrained Optimization Problems
David Bucher, Daniel Porawski, Maximilian Janetschek +4
This paper proposes a novel combination of constraint encoding methods for the Quantum Approximate Optimization Ansatz (QAOA). Real-world optimization problems typically consist of…
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…
Clique detection using symmetry-restricted quantum circuits
Maximilian Balthasar Mansky, Tobias Rohe, Dmytro Bondarenko +2
We show the application of permutation-invariant quantum circuits to the clique problem. The experiment asks to label a clique through identification of the nodes in a larger subgr…
End-to-End Speedup for Quantum Simulation-Based Optimization in Power Grid Management
Jonas Stein, Jannis Lutz, Moritz Sölderer +4
Quantum Simulation-based Optimization (QuSO) is a recently proposed class of optimization problems that entails industrially relevant problems characterized by cost functions or co…