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:2606. 07666v1 Announce Type: cross Abstract: Noisy intermediate-scale quantum (NISQ) processors are entering an early fault-tolerance regime where full quantum error correction carries prohibitive resource costs, yet lightweight error detection can meaningfully improve algorithmic success rates.
By Sumit Chongder (Indian Institute of Technology Jodhpur)
Quantum Reinforcement Learning for Cost and Delay Tradeoffs in Quantum Cloud Orchestration proposes QRLQ, a scheduling framework that integrates parameterised quantum circuits with a dueling double deep Q‑network to balance execution cost and delay in quantum‑as‑a‑service environments. Simulation results show QRLQ outperforms heuristic baselines, achieving 5‑11% lower mean cost and up to 82% lower mean delay while maintaining fidelity within 2% of a fidelity‑greedy policy. Compared to a classical deep reinforcement learning baseline, QRLQ delivers comparable performance with 72% fewer trainable parameters.
By An N. H. Phan, Dang Van Huynh, Muhammad Usman, Hoa T. Nguyen
arXiv:2606. 11620v1 Announce Type: cross Abstract: Approximate tensor-network simulators enable classical simulation of quantum circuits beyond the reach of exact methods, but selecting optimal approximation parameters -- such as bond dimension thresholds -- remains a costly trial-and-error process.
By Honjar Xing, Yehong Jiang, Xianbang Wang, Zehua Wang, Zhicheng Jiang
arXiv:2606. 12816v2 Announce Type: replace-cross Abstract: Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors.
By Yash Vardhan Tomar, Dheeraj Peddireddy
Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors. Routes that appear efficient by standard overhead metrics can still lose fidelity when they pass through poorly calibrated couplers.
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
The paper implements two quantum graph neural network architectures—Simplified Graph Convolution (SGC) and Linear Graph Convolution (LGC)—and evaluates them on benchmark graph datasets for semi‑supervised learning using quantum simulation. It compares their predictive performance and optimization behavior to classical baselines, finding that the quantum models achieve competitive results with fewer parameters. Additionally, the study provides a cost‑gradient analysis to identify trainable tasks and a classical simulability investigation to determine regimes where the circuits remain robust during training.
By Paul San Sebastian Sein, Theodor Iosif, Tilen G. Limb\"ack-Stokin, Kin Ian Lo, Yidong Liao
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. Quantum resource estimation (QRE) plays a central role in this transition, yet existing approaches remain largely compilation-heavy or domain-knowledge-guided symbolic annotations, and tightly coupled to long-term fault-tolerant assumptions, limiting their topical applicability.
The paper presents a reinforcement‑learning approach to schedule link‑level entanglement in quantum networks, using a Markov Decision Process and double deep Q‑networks with message‑passing neural networks. The resulting policies achieve 100% success rates even when the link activation probability is reduced by up to 71% compared to baseline heuristics, and maintain at least 80% success when task placements are hardware‑restricted. The authors also develop metrics to interpret the learned policy and employ a large language model to generate a heuristic that matches the DQN performance, suggesting a scalable method for extracting interpretable strategies in large quantum networks.
By Leon Rode, Sumeet Khatri, Supartha Podder
arXiv:2607. 20225v1 Announce Type: cross Abstract: While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging due to exponentially large search spaces.
By Seongmin Kim, Abhinav Rijal, Yuri Alexeev, Nora Bauer, Martin Roetteler, Mina Yoon, George Siopsis, In-Saeng Suh