191 citations · 551 across the 134 of their papers we have counts for
141 papers
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…
Emergent Problem-Graph Alignment in RL-Discovered Entanglement Topologies for QAOA
Tobias Rohe, Federico Harjes Ruiloba, Markus Baumann +4
In the Quantum Approximate Optimization Algorithm (QAOA), the entanglement topology, where qubit pairs are connected by two-qubit gates, is conventionally set equal to the edge set…
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…
Detrimental Agnostic Entanglement: The Case Against Hardware-Efficient Ansätze for Combinatorial Optimization
Tobias Rohe, Markus Baumann, Federico Harjes Ruiloba +3
Variational quantum algorithms (VQAs) for combinatorial optimization routinely employ entangling gates as a default design choice, yet the role of entanglement, in its amount and s…
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…
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…
Reinforcement Learning for Parameterized Quantum State Preparation: A Comparative Study
Gerhard Stenzel, Isabella Debelic, Michael Kölle +4
We extend directed quantum circuit synthesis (DQCS) with reinforcement learning from purely discrete gate selection to parameterized quantum state preparation with continuous singl…
An Evaluation of the Remote CX Protocol under Noise in Distributed Quantum Computing
Leo Sünkel, Michael Kölle, Tobias Rohe +1
Quantum computers connected through classical and quantum communication channels can be combined to function as a single unit to run large quantum circuits that each device is unab…
Long Range Frequency Tuning for QML
Michael Poppel, Markus Baumann, Sebastian Wölckert +2
Angle-encoded variational quantum circuits admit a truncated Fourier series representation of their output, but approximating functions with maximum frequency using fixe…
Quantum Generator Kernels
Philipp Altmann, Maximilian Mansky, Maximilian Zorn +2
Quantum kernel methods offer significant theoretical benefits by rendering classically inseparable features separable in quantum space. Yet, the practical application of Quantum Ma…
Emergent Cooperation in Quantum Multi-Agent Reinforcement Learning Using Communication
Michael Kölle, Christian Reff, Leo Sünkel +3
Emergent cooperation in classical Multi-Agent Reinforcement Learning has gained significant attention, particularly in the context of Sequential Social Dilemmas (SSDs). While class…
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…
Dynamic Incentivized Cooperation under Changing Rewards
Philipp Altmann, Thomy Phan, Maximilian Zorn +2
Peer incentivization (PI) is a popular multi-agent reinforcement learning approach where all agents can reward or penalize each other to achieve cooperation in social dilemmas. Des…
Quantum King-Ring Domination in Chess: A QAOA Approach
Gerhard Stenzel, Michael Kölle, Tobias Rohe +4
The Quantum Approximate Optimization Algorithm (QAOA) is extensively benchmarked on synthetic random instances such as MaxCut, TSP, and SAT problems, but these lack semantic struct…
Topology-Guided Quantum GANs for Constrained Graph Generation
Tobias Rohe, Markus Baumann, Michael Poppel +3
Quantum computing (QC) promises theoretical advantages, benefiting computational problems that would not be efficiently classically simulatable. However, much of this theoretical s…