6 papers
How to find expressible and trainable parameterized quantum circuits?
Peter Röseler, Dennis Willsch, Kristel Michielsen
Whether parameterized quantum circuits (PQCs) can be systematically constructed to be both trainable and expressive remains an open question. Highly expressive PQCs often exhibit b…
Quantum Deep Learning: A Comprehensive Review
Yanjun Ji, Zhao-Yun Chen, Marco Roth +10
Quantum deep learning (QDL) explores the use of both quantum and quantum-inspired resources to determine when deep learning's core capabilities, such as expressivity, generalizatio…
Learning-Driven Annealing with Adaptive Hamiltonian Modification for Solving Large-Scale Problems on Quantum Devices
Sebastian Schulz, Dennis Willsch, Kristel Michielsen
We present Learning-Driven Annealing (LDA), a framework that links individual quantum annealing evolutions into a global solution strategy to mitigate hardware constraints such as…
Towards a Digital Twin of Noisy Quantum Computers: Calibration-Driven Emulation of Transmon Qubits
Ronny Müller, Maximilian Zanner, Mika Schielein +8
We develop a parametric error model to construct a digital twin of a superconducting transmon qubit device. The model parameters are extracted from hardware calibration data and su…
Transfer learning of optimal QAOA parameters in combinatorial optimization
J. A. Montanez-Barrera, Dennis Willsch, Kristel Michielsen
Solving combinatorial optimization problems (COPs) is a promising application of quantum computation, with the Quantum Approximate Optimization Algorithm (QAOA) being one of the mo…
Observation of Josephson Harmonics in Tunnel Junctions
Dennis Willsch, Dennis Rieger, Patrick Winkel +27
Approaches to developing large-scale superconducting quantum processors must cope with the numerous microscopic degrees of freedom that are ubiquitous in solid-state devices. State…