Analog Quantum Feature Selection with Neutral-Atom Quantum Processors

• URL: http://arxiv.org/abs/2510.20798v1
• Date: Thu, 23 Oct 2025 17:57:34 GMT
• Title: Analog Quantum Feature Selection with Neutral-Atom Quantum Processors
• Authors: Jose J. Orquin-Marques, Carlos Flores-Garrigos, Alejandro Gomez Cadavid, Anton Simen, Enrique Solano, Narendra N. Hegade, Jose D. Martin-Guerrero, Yolanda Vives-Gilabert,
• Abstract summary: We present a quantum-native approach to quantum feature selection (QFS) based on analog quantum simulation with neutral atom arrays.<n>The protocol is evaluated through simulations on three benchmark binary classification datasets: Adult Income, Bank Marketing, and Telco Churn.<n>For compact subsets of 2-5 features, analog QFS improves mean AUC scores by 1.5-2.3% while reducing the number of features by 75-84%.
• Score: 31.458406135473805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a quantum-native approach to quantum feature selection (QFS) based on analog quantum simulation with neutral atom arrays, adaptable to a variety of academic and industrial applications. In our method, feature relevance-measured via mutual information with the target-is encoded as local detuning amplitudes, while feature redundancy is embedded through distance-dependent van der Waals interactions, constrained by the Rydberg blockade radius. The system is evolved adiabatically toward low-energy configurations, and the resulting measurement bitstrings are used to extract physically consistent subsets of features. The protocol is evaluated through simulations on three benchmark binary classification datasets: Adult Income, Bank Marketing, and Telco Churn. Compared to classical methods such as mutual information ranking and Boruta, combined with XGBoost and Random Forest classifiers, our quantum-computing approach achieves competitive or superior performance. In particular, for compact subsets of 2-5 features, analog QFS improves mean AUC scores by 1.5-2.3% while reducing the number of features by 75-84%, offering interpretable, low-redundancy solutions. These results demonstrate that programmable Rydberg arrays offer a viable platform for intelligent feature selection with practical relevance in machine learning pipelines, capable of transforming computational quantum advantage into industrial quantum usefulness.

Related papers

• Parallel Multi-Circuit Quantum Feature Fusion in Hybrid Quantum-Classical Convolutional Neural Networks for Breast Tumor Classification [0.0]
  We present a hybrid Quantum-Classical Convolutional Neural Network (QCNN) architecture designed for the binary classification of the BreastMNIST dataset.<n>Our results indicate that hybrid QCNN architectures can leverage entanglement and quantum feature fusion to enhance medical image classification tasks.
  arXiv  Detail & Related papers  (2025-11-29T17:47:14Z)
• 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 Reinforcement Learning by Adaptive Non-local Observables [10.617463958884528]
  We introduce an adaptive non-local observable (ANO) paradigm within variational quantum circuits (VQCs)<n>ANO-VQC architecture serves as the function approximator in Deep Q-Network (DQN) and Asynchronous Advantage Actor-Critic (A3C) algorithms.<n>Our results demonstrate that adaptive multi-qubit observables can enable practical quantum advantages in reinforcement learning.
  arXiv  Detail & Related papers  (2025-07-25T18:57:16Z)
• An Efficient Quantum Classifier Based on Hamiltonian Representations [50.467930253994155]
  Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks.<n>We propose an efficient approach that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings.<n>We evaluate our approach on text and image classification tasks, against well-established classical and quantum models.
  arXiv  Detail & Related papers  (2025-04-13T11:49:53Z)
• Quantum SMOTE with Angular Outliers: Redefining Minority Class Handling [1.6590638305972631]
  Quantum-SMOTEV2 synthesizes data samples using swap tests and quantum rotation centered around a single data centroid.<n> Experimental results show significant enhancements in model performance metrics at moderate SMOTE levels (30-36%)<n>The method is scalable, utilizing compact swap tests and low depth quantum circuits to accommodate a large number of features.
  arXiv  Detail & Related papers  (2025-01-31T10:10:36Z)
• Enhanced feature encoding and classification on distributed quantum hardware [0.0]
  We propose a novel feature map optimization strategy for Quantum Support Vector Machines (QSVMs)<n>We take into account backend-specific parameters, including qubit connectivity, native gate sets, and circuit depth, which are critical factors in noisy quantum devices.<n>The study was carried out by partitioning each quantum processing unit (QPU) into several sub-units with the same topology to implement individual QSVM instances.
  arXiv  Detail & Related papers  (2024-12-02T16:14:37Z)
• Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
  Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML) At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
  arXiv  Detail & Related papers  (2024-11-13T23:56:20Z)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)
• When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing [75.75419308975746]
  This work proposes a vertical federated learning architecture based on variational quantum circuits to demonstrate the competitive performance of a quantum-enhanced pre-trained BERT model for text classification. Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
  arXiv  Detail & Related papers  (2022-02-17T09:55:21Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

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.