1 citations · 1 across the 6 of their papers we have counts for
8 papers
Illuminating early ZrC oxidation using ensemble small-cell DFT with dynamic gas management
Philip Wurzner, Boyang Chen, Matthias Möller +3
During zirconium carbide oxidation, liberated carbon can remain beneath the oxide or escape as gas, complicating the interpretation of mass measurements. We adapt the small-cell en…
Generative Replay Mitigates Sample Starvation in Quantum Architecture Search
Akash Kundu, Amit Kumar Jaiswal, Sebastian Feld +1
Reinforcement learning (RL) can automate quantum architecture search, but its scalability is limited when useful circuit trajectories become rare in the rapidly expanding search sp…
MOSAIQC: Mixed-topology-aware Optimization for Scalable Approximate noise-Informed Quantum circuit Cutting
Koen Mesman, Yinglu Tang, Matthias Moller +2
Current quantum computers do not yet have the required qubit resources to meet the demands of most practical quantum algorithms. To circumvent this constraint, the practice of divi…
Automated Circuit Depth Reduction of Quantum Subroutines via Compilation
Folkert de Ronde, Stephan Wong, Sebastian Feld
Optimizing quantum circuits by reducing circuit depth is essential for improving the efficiency and scalability of quantum algorithms, particularly as quantum hardware continues to…
Rethinking How to Act: Action-Space Engineering for Reinforcement Learning-Based Circuit Routing in Distributed Quantum Systems
Joost Van Veen, Luise Prielinger, Sebastian Feld
As it becomes increasingly difficult to monolithically scale a quantum processor, distributed quantum computing (DQC) offers an alternative by distributing qubits across multiple s…
Replay-buffer engineering for noise-aware quantum circuit optimization
Akash Kundu, Sebastian Feld
Deep reinforcement learning for quantum circuit optimization faces three bottlenecks: replay buffers that overlook temporal difference (TD) target reliability, curriculum-based arc…
Quantum-assisted Stacking Sequence Retrieval and Laminated Composite Design
Arne Wulff, Swapan Madabhushi Venkata, Boyang Chen +3
We, the QAIMS lab lab at the Aerospace Faculty of TU Delft, participated as finalists in the Airbus/BMW Quantum Computing Challenge 2024. Stacking sequence retrieval, a complex com…
NN-AE-VQE: Neural network parameter prediction on autoencoded variational quantum eigensolvers
Koen Mesman, Yinglu Tang, Matthias Moller +2
A longstanding computational challenge is the accurate simulation of many-body particle systems. Especially for deriving key characteristics of high-impact but complex systems such…