The paper introduces a low‑overhead, fidelity‑aware scheduling framework for multi‑QPU quantum computing systems. It employs a Graph Neural Network to predict the expected execution fidelity of a given quantum circuit on each available QPU before compilation. Using these predictions, a tunable scheduler balances execution fidelity against parallelism, achieving near‑optimal fidelity assignments while reducing the computational cost compared to brute‑force compilation on every device.
By Innocenzo Fulginiti, Antonio Tudisco, Salvatore Zammuto, Patrick Hopf, Deborah Volpe, Helmut Seidl, Giovanna Turvani, Robert Wille, Christian B. Mendl, Martin Schulz
The paper introduces MQSS-Selector, a reinforcement‑learning guided pass selection system for an MLIR compilation pipeline aimed at unified High Performance Computing‑Quantum Computing (HPCQC) infrastructures. It addresses the challenges of Noisy Intermediate‑Scale Quantum (NISQ) devices by integrating device selection, compiler‑pass optimization, and job queue scheduling into a single learning‑based framework. The selector can simultaneously optimize multiple objectives—fidelity, compilation time, and scheduling latency—while adapting to circuit characteristics and device conditions.
By Andre Youssefi (Leibniz Supercomputing Centre), Erc\"ument Kaya (Leibniz Supercomputing Centre, Technical University of Munich), Minh Chung (Leibniz Supercomputing Centre), Jorge Echavarria (Munich Quantum Valley), Laura B. Schulz (Argonne National Laboratory), Martin Schulz (Leibniz Supercomputing Centre, Technical University of Munich)
arXiv:2607. 28422v1 Announce Type: new Abstract: Fault-tolerant quantum computing (FTQC) relies on quantum error correction to suppress physical errors and preserve logical information at scale.
By Ran Miao, Rui Luo, Xiaohan Shan, Xiaoming Sun
arXiv:2608. 12936v1 Announce Type: cross Abstract: As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system-level optimization across heterogeneous hardware and software stacks.
By Harshkumar Oza, Aritra Sarkar, Syed Naqi Abbas, Rahul Bhowmick, Aryan Prakash, Prateek P Kulkarni, Krishna Kumar Sabapathy
arXiv:2604.21863v2 Announce Type: replace-cross
Abstract: Deep reinforcement learning for quantum circuit optimization faces three bottlenecks: replay buffers that overlook temporal difference (TD) t...
By Akash Kundu, Sebastian Feld
arXiv:2508. 20134v2 Announce Type: replace Abstract: Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for domain-specific planning, iterative code synthesis, and low-level calibration.
By Zhenxiao Fu, Lei Jiang, Yilun Xu, Gang Huang, Fan Chen