5 papers
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-robust quantum circuit optimization
Akash Kundu, Sebastian Feld
Deep reinforcement learning (RL) for quantum circuit optimization faces three fundamental bottlenecks: replay buffers that ignore the reliability of temporal-difference (TD) target…
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…