The paper investigates how hardware noise influences the generalization ability of quantum circuit Born machines (QCBMs) trained with maximum mean discrepancy. It establishes that the generalization gap is bounded by the complexity of distributions reachable under any noise process, and shows that scrambled local Pauli noise reduces this complexity while biasing the model toward uniformity, thereby acting as a regularizer with an optimal intermediate strength. The authors introduce GENIE, a framework that implements a target noise level with capped error mitigation and dynamically selects the optimal noise during training using held‑out data, achieving provably bounded regret and outperforming fixed‑noise strategies in simulations on synthetic and census datasets.
arXiv:2608.24229v1 Announce Type: cross Abstract: Quantum noise is expected to degrade quantum machine learning by driving circuits away from their noiseless implementations. Yet recent studies show...
The paper proposes using the inherent shot‑noise from quantum measurements as a resource for diffusion models, replacing part of the stochastic noise normally injected during generation. By modeling measurement error as multinomial sampling noise and mapping the diffusion process onto a bounded hyperplane, the authors derive a state‑dependent adaptive shot budget that matches the mean and covariance of a shot‑noise‑free update. Experiments on an eight‑dimensional Gaussian mixture and MNIST latent diffusion demonstrate that this approach achieves comparable generation quality with dramatically fewer shots—down to 303 shots per sample versus 1.6 × 10⁶ in a high‑shot baseline.
arXiv:2607. 20045v1 Announce Type: cross Abstract: Physical noise in near-term quantum hardware is usually treated as a nuisance to suppress.
arXiv:2606. 10179v1 Announce Type: cross Abstract: Quantum Circuit Born Machines (QCBMs) offer a natural approach to generative machine learning by leveraging the Born rule.
arXiv:2607. 21409v1 Announce Type: cross Abstract: A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs).
arXiv:2608.31117v1 Announce Type: cross Abstract: Generative models have become central across science and industry, from image and text synthesis to the design of molecules and materials. Quantum ge...
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.
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.
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.
Circuit cutting lets a large quantum neural network (QNN) run as independent subcircuits on small devices, but rebuilding its outputs by reconstruction carries a classical sampling overhead exponential in the number of cuts - the dominant runtime cost in prior work. We ask whether, for machine-learning tasks, this step is necessary, and replace it with late fusion: each subcircuit is trained and measured independently, and a small classical head combines their outputs - a linear-cost, decision-level combination borrowed from multimodal learning.
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...
arXiv:2607. 01197v1 Announce Type: new Abstract: Quantum computing has emerged as a promising computational paradigm for machine learning (ML), with the potential to offer computational advantages over classical approaches.