arXiv Machine Learning
Jun 9
Quantum feature-map learning with reduced resource overhead
arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.
A Quantum-Inspired Dequantization Method for Diagonally Weighted Matrix Functions: Application to Learning with Optimized Random Features
The paper introduces a classical algorithm that dequantizes a quantum sampler used for learning with optimized random features. By sampling heavy indices and reducing the transformation to a small principal block, the method produces a sparse classical representation with operator‑norm guarantees. This approach enables a classical sampler with specified accuracy and polynomial runtime, demonstrating that quantum block‑encoding factorizations can provide sufficient classical structure even when direct sampling access to the composite matrix is unavailable.
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arXiv Machine Learning
2d ago
Towards Surrogate Based Dequantization of Quantum Reinforcement Learning
The paper investigates whether quantum reinforcement learning algorithms can be matched by efficient classical methods. It focuses on a simplified reinforcement learning setting with a uniform generative model, providing finite‑sample guarantees for classical kernelized Fitted Q‑Iteration that uses kernels aligned with parameterized quantum circuits. The authors identify sufficient conditions on data encoding, kernel choice, and problem structure under which this classical approach dequantizes quantum Q‑learning, and suggest using kernelized Fitted Q‑Iteration as a heuristic when those conditions cannot be verified.
arXiv Machine Learning
Jul 8
Benign Overfitting with Quantum Kernels
arXiv:2503. 17020v2 Announce Type: replace-cross Abstract: Kernel methods compare inputs through feature maps.
arXiv Machine Learning
Jul 13
Is data-efficient learning feasible with quantum models?
arXiv:2508. 19437v2 Announce Type: replace-cross Abstract: The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for understanding dataset characteristics remains elusive.
arXiv Machine Learning
Jul 2
Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization
arXiv:2607. 01080v1 Announce Type: new Abstract: We investigate Gaussian process (GP) bandit optimization with quantum kernels, assuming the mean reward function lies in the reproducing kernel Hilbert space (RKHS) induced by the quantum kernel.
arXiv Machine Learning
Jul 7
Canonical quantization of neurons
arXiv:2607. 05000v1 Announce Type: cross Abstract: Canonical quantization provides a systematic procedure for constructing quantum models from classical Hamiltonians.
arXiv Machine Learning
Aug 5
Beyond the Gegenbauer Paradigm: q-Orthogonal Kernels for Machine Learning
arXiv:2608. 03482v1 Announce Type: new Abstract: The performance of Support Vector Machines (SVMs) critically depends on the kernel function choice, which enables implicit mapping of data into high-dimensional feature spaces.
arXiv Machine Learning
Sep 2
Quantum Sparse Autoencoders for Q-Matrix Estimation in Cognitive Diagnosis
arXiv:2609.01537v1 Announce Type: new Abstract: Q-matrices play a central role in cognitive diagnosis within educational data mining (EDM), specifying which latent skills each assessment item require...
arXiv Machine Learning
Jun 19
SMT-AD: a scalable quantum-inspired anomaly detection approach
arXiv:2604. 06265v2 Announce Type: replace Abstract: Quantum-inspired tensor networks algorithms have shown to be effective and efficient models for machine learning tasks, including anomaly detection.
arXiv AI
Jun 6
Quantum enhanced rare event discovery and sampling
arXiv:2606. 06316v1 Announce Type: cross Abstract: Financial crashes, cascading failures in infrastructure, and critical errors in AI systems are frequently triggered by events that occur with extremely small probability.
Hugging Face Trending Papers
Aug 20
TT-net: Quantum Inspired Tensor Network Denoising in Conditional GANs
TT‑Net introduces a two‑cut tensor‑train decomposition to replace the per‑channel SVD denoising block in conditional GANs, enabling direct cross‑channel information access. In controlled experiments, TT‑Net outperforms SVD‑Net on PSNR and SSIM for Gaussian, motion blur, and salt‑and‑pepper noise, and surpasses EigenGAN and Pix2pix for Gaussian noise. Training dynamics reveal that TT‑Net’s adversarial loss saturates while reconstruction quality continues to improve, raising questions about the role of the adversarial component.
arXiv Machine Learning
Jul 24
Cautious optimism for deep parameterized quantum circuits
arXiv:2607. 21409v1 Announce Type: cross Abstract: A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs).