Quenched Quantum Feature Maps

• URL: http://arxiv.org/abs/2508.20975v1
• Date: Thu, 28 Aug 2025 16:28:48 GMT
• Title: Quenched Quantum Feature Maps
• Authors: Anton Simen, Carlos Flores-Garrigos, Murilo Henrique De Oliveira, Gabriel Dario Alvarado Barrios, Juan F. R. Hernández, Qi Zhang, Alejandro Gomez Cadavid, Yolanda Vives-Gilabert, José D. Martín-Guerrero, Enrique Solano, Narendra N. Hegade, Archismita Dalal,
• Abstract summary: We propose a quantum feature mapping technique that leverages the quench dynamics of a quantum spin glass to extract complex data patterns.<n>Our work presents the first quantum ML demonstrations at the quantum-advantage level, bridging the gap between quantum supremacy and useful real-world academic and industrial applications.
• Score: 32.2069811127299
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a quantum feature mapping technique that leverages the quench dynamics of a quantum spin glass to extract complex data patterns at the quantum-advantage level for academic and industrial applications. We demonstrate that encoding a dataset information into disordered quantum many-body spin-glass problems, followed by a nonadiabatic evolution and feature extraction via measurements of expectation values, significantly enhances machine learning (ML) models. By analyzing the performance of our protocol over a range of evolution times, we empirically show that ML models benefit most from feature representations obtained in the fast coherent regime of a quantum annealer, particularly near the critical point of the quantum dynamics. We demonstrate the generalization of our technique by benchmarking on multiple high-dimensional datasets, involving over a hundred features, in applications including drug discovery and medical diagnostics. Moreover, we compare against a comprehensive suite of state-of-the-art classical ML models and show that our quantum feature maps can enhance the performance metrics of the baseline classical models up to 210%. Our work presents the first quantum ML demonstrations at the quantum-advantage level, bridging the gap between quantum supremacy and useful real-world academic and industrial applications.

Related papers

• Digitized Counterdiabatic Quantum Feature Extraction [35.670314643295036]
  We introduce a Hamiltonian-based quantum feature extraction method that generates complex features via the dynamics of $k$-local many-body spins Hamiltonians.<n>We assess the approach on high-dimensional, real-world datasets, including molecular toxicity classification and image recognition.<n>The results suggest that combining quantum and classical feature extraction can provide consistent improvements across diverse machine learning tasks.
  arXiv  Detail & Related papers  (2025-10-15T17:59:35Z)
• Quantum Visual Fields with Neural Amplitude Encoding [70.86293548779774]
  We introduce a new type of Quantum Implicit Neural Representation (QINR) for 2D image and 3D geometric field learning.<n>QVF encodes classical data into quantum statevectors using neural amplitude encoding grounded in a learnable energy manifold.<n>Our ansatz follows a fully entangled design of learnable parametrised quantum circuits, with quantum (unitary) operations performed in the real Hilbert space.
  arXiv  Detail & Related papers  (2025-08-14T17:59:52Z)
• Quantum-Accelerated Neural Imputation with Large Language Models (LLMs) [0.0]
  This paper introduces Quantum-UnIMP, a novel framework that integrates shallow quantum circuits into an LLM-based imputation architecture.<n>Our experiments on benchmark mixed-type datasets demonstrate that Quantum-UnIMP reduces imputation error by up to 15.2% for numerical features (RMSE) and improves classification accuracy by 8.7% for categorical features (F1-Score) compared to state-of-the-art classical and LLM-based methods.
  arXiv  Detail & Related papers  (2025-07-11T02:00:06Z)
• Characterizing Non-Markovian Dynamics of Open Quantum Systems [0.0]
  We develop a structure-preserving approach to characterizing non-Markovian evolution using the time-convolutionless (TCL) master equation.<n>We demonstrate our methodology using experimental data from a superconducting qubit at the Quantum Device Integration Testbed (QuDIT) at Lawrence Livermore National Laboratory.<n>These findings provide valuable insights into efficient modeling strategies for open quantum systems, with implications for quantum control and error mitigation in near-term quantum processors.
  arXiv  Detail & Related papers  (2025-03-28T04:43:24Z)
• Quantum Latent Diffusion Models [65.16624577812436]
  We propose a potential version of a quantum diffusion model that leverages the established idea of classical latent diffusion models.<n>This involves using a traditional autoencoder to reduce images, followed by operations with variational circuits in the latent space.<n>The results demonstrate an advantage in using a quantum version, as evidenced by obtaining better metrics for the images generated by the quantum version.
  arXiv  Detail & Related papers  (2025-01-19T21:24:02Z)
• Unsupervised Quantum Anomaly Detection on Noisy Quantum Processors [1.2325897339438878]
  We provide a systematic analysis of the generalization properties of the One-Class Support Vector Machine (OCSVM) algorithm. Results were theoretically simulated and experimentally validated on trapped-ion and superconducting quantum processors.
  arXiv  Detail & Related papers  (2024-11-25T22:42:38Z)
• Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
  We use FNOs to model the evolution of random quantum spin systems. We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
  arXiv  Detail & Related papers  (2024-09-05T07:18:09Z)
• Ground state-based quantum feature maps [17.857341127079305]
  We introduce a quantum data embedding protocol based on the preparation of a ground state of a parameterized Hamiltonian.<n>We show that ground state embeddings can be described effectively by a spectrum with degree that grows rapidly with the number of qubits.
  arXiv  Detail & Related papers  (2024-04-10T17:17:05Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits [70.97518416003358]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning on noisy intermediate-scale quantum (NISQ) devices. While tensor-train networks (TTNs) can enhance VQC representation and generalization, the resulting hybrid model, TTN-VQC, faces optimization challenges due to the Polyak-Lojasiewicz (PL) condition. To mitigate this challenge, we introduce Pre+TTN-VQC, a pre-trained TTN model combined with a VQC.
  arXiv  Detail & Related papers  (2023-05-18T03:08:18Z)
• Theory of Quantum Generative Learning Models with Maximum Mean Discrepancy [67.02951777522547]
  We study learnability of quantum circuit Born machines (QCBMs) and quantum generative adversarial networks (QGANs) We first analyze the generalization ability of QCBMs and identify their superiorities when the quantum devices can directly access the target distribution. Next, we prove how the generalization error bound of QGANs depends on the employed Ansatz, the number of qudits, and input states.
  arXiv  Detail & Related papers  (2022-05-10T08:05:59Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.