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arXiv Machine Learning

GFlowQEC: Generative Flow Networks that Learn to Sample Quantum LDPC Codes

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arXiv Machine Learning
Oct 15

Selective Growing Q-Networks (SGQN): An Analysis of Adaptive Action Grids in Decoupled Q-Learning

Selective Growing Q‑Networks (SGQN) extend value‑based Q‑learning for continuous control by replacing uniform action‑grid growth with an adaptive grid that adds bins near high‑Q regions and prunes low‑Q bins, while maintaining decoupled Q‑learning and epsilon‑greedy action selection. On eight DeepMind Control Suite tasks, SGQN achieves the highest mean final return on four tasks, matches the best method within 1.1% on three more, and uses on average 7.5 of 9 available bins per dimension. Experiments show that the adaptive grid’s growth and pruning have limited impact on performance, whereas starting from a coarse grid and using epsilon‑greedy selection are critical for success.

By Parham Nooranbakht, Seyed Mahdi Basiri Azad, Joschka B\"odecker
reinforcement-learningefficiency
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arXiv Machine Learning
Oct 9

Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers

The paper investigates whether the adaptive circuit‑growth method Q‑FLAIR, which stops adding gates when training loss ceases to improve, can be combined with Caro et al.’s bound on generalization to predict how circuit depth should scale with training data size. Experiments on full‑resolution MNIST 3‑vs‑5 classification with training sets from 2,000 to 10,000 samples show no clear relationship between dataset size and the final circuit size; both circuit size and test accuracy vary non‑monotonically, and seed‑to‑seed variance is large. While the empirical generalization gap never exceeds Caro et al.’s bound in 14 of 15 runs, the gap correlates only weakly with the bound’s value, indicating that the bound’s predictive power is limited.

By Saeefa Rubaiyet Nowmi, Md Mahmuduzzaman Kamol, Mohammad Saidur Rahman
fine-tuning
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arXiv Machine Learning
Oct 9

Beyond QAOA: A Review of AI and Quantum Computing for Adaptive Combinatorial Optimization

The review examines how machine learning can adapt quantum combinatorial optimization workflows by learning decisions such as formulation, penalties, shot budgets, and backend selection. It categorizes the literature into three paradigms—AI for quantum optimization, quantum for AI-driven optimization, and AI‑quantum co‑optimization—and analyzes 119 papers, finding stronger evidence for AI assisting quantum methods than for quantum enhancing learned optimizers. The authors highlight limited experimental controls, introduce an M0‑M5 evidence hierarchy, and argue that scaling is a systems problem requiring careful allocation of classical and quantum resources.

By Hoong Chuin LAU
benchmarks
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arXiv Machine Learning
Sep 3

Quantum MeanFlow: single-shot generative sampling on NISQ hardware

Quantum MeanFlow (QMF) is a new quantum generative sampling method that enables single‑step sample generation by learning an average velocity field over a time interval, unlike the multi‑step quantum flow matching (QFM) which requires sequential integration of an ordinary differential equation. Using parameterized quantum circuits, the authors benchmark QMF and QFM on the MNIST dataset, finding that QMF produces lower image quality than multi‑step QFM but outperforms single‑step QFM at every shot count. Both models were executed on IBM quantum computers, and best‑of‑N rejection sampling mitigates device noise without circuit modification, demonstrating QMF’s practicality for efficient single‑step quantum generative sampling.

By Ashish Joshi, Eshaan Mistry, Takahiko Koyama
diffusionbenchmarks
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arXiv Machine Learning
Sep 17

QEMScore: How Much Does the Measurement Add to Learned Quantum Error Mitigation?

The paper introduces QEMScore, a metric that compares learned quantum error mitigators to capacity‑matched controls that do not use measurement data. Using simulated circuits with exact ideal answers, the study finds that many mitigators gain little from measurement inputs, with a plain polynomial model often outperforming them. On real hardware data, however, measurement inputs can provide predictive benefits, highlighting that performance depends on representation and protocol specifics.

By Yue Zhao, Huayue Gu, Yushun Dong, Xiyang Hu
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arXiv AI
Aug 3

DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search

arXiv:2607. 29491v1 Announce Type: cross Abstract: Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circuit, although circuit construction and action legality are deterministic and known.

By Jiayang Niu, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, Muhammad Usman, Yongli Ren
reinforcement-learning
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arXiv AI
Aug 11

Multi-agent discovery of practical quantum LDPC codes

arXiv:2608. 08996v1 Announce Type: cross Abstract: Quantum low-density parity-check (qLDPC) codes can encode multiple logical qubits using sparse parity checks, yet searching for useful finite-length instances remains a challenging design problem because code performance must be optimized while satisfying practical constraints.

By Dongheng Qian, Tianyi Li
agents
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