2 citations · 2 across the 8 of their papers we have counts for
9 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…
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…
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…
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…
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…
IF-QAOA: A Penalty-Free Approach to Accelerating Constrained Quantum Optimization
David Bucher, Jonas Stein, Sebastian Feld +1
Traditional methods for handling (inequality) constraints in the Quantum Approximate Optimization Ansatz (QAOA) typically rely on penalty terms and slack variables, which increase…
CUAOA: A Novel CUDA-Accelerated Simulation Framework for the QAOA
Jonas Stein, Jonas Blenninger, David Bucher +4
The Quantum Approximate Optimization Algorithm (QAOA) is a prominent quantum algorithm designed to find approximate solutions to combinatorial optimization problems, which are chal…
Towards Robust Benchmarking of Quantum Optimization Algorithms
David Bucher, Nico Kraus, Jonas Blenninger +3
Benchmarking the performance of quantum optimization algorithms is crucial for identifying utility for industry-relevant use cases. Benchmarking processes vary between optimization…
Solving the Turbine Balancing Problem using Quantum Annealing
Arnold Unterauer, David Bucher, Matthias Knoll +6
Quantum computing has the potential for disruptive change in many sectors of industry, especially in materials science and optimization. In this paper, we describe how the Turbine…