A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning

• URL: http://arxiv.org/abs/2506.19275v1
• Date: Tue, 24 Jun 2025 03:09:16 GMT
• Title: A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning
• Authors: Hevish Cowlessur, Tansu Alpcan, Chandra Thapa, Seyit Camtepe, Neel Kanth Kundu,
• Abstract summary: Quantum Principal Geodesic Analysis (qPGA) is a non-invertible method for dimensionality reduction and qubit-efficient encoding.<n>We show that qPGA preserves local structure more effectively than both quantum and hybrid autoencoders.<n>In downstream QML classification tasks, qPGA can achieve over 99% accuracy and F1-score on MNIST and Fashion-MNIST, outperforming quantum-dependent baselines.
• Score: 11.861417859173859
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficiently embedding high-dimensional datasets onto noisy and low-qubit quantum systems is a significant barrier to practical Quantum Machine Learning (QML). Approaches such as quantum autoencoders can be constrained by current hardware capabilities and may exhibit vulnerabilities to reconstruction attacks due to their invertibility. We propose Quantum Principal Geodesic Analysis (qPGA), a novel, non-invertible method for dimensionality reduction and qubit-efficient encoding. Executed classically, qPGA leverages Riemannian geometry to project data onto the unit Hilbert sphere, generating outputs inherently suitable for quantum amplitude encoding. This technique preserves the neighborhood structure of high-dimensional datasets within a compact latent space, significantly reducing qubit requirements for amplitude encoding. We derive theoretical bounds quantifying qubit requirements for effective encoding onto noisy systems. Empirical results on MNIST, Fashion-MNIST, and CIFAR-10 show that qPGA preserves local structure more effectively than both quantum and hybrid autoencoders. Additionally, we demonstrate that qPGA enhances resistance to reconstruction attacks due to its non-invertible nature. In downstream QML classification tasks, qPGA can achieve over 99% accuracy and F1-score on MNIST and Fashion-MNIST, outperforming quantum-dependent baselines. Initial tests on real hardware and noisy simulators confirm its potential for noise-resilient performance, offering a scalable solution for advancing QML applications.

Related papers

• AQER: a scalable and efficient data loader for digital quantum computers [62.40228216126285]
  We develop AQER, a scalable AQL method that constructs the loading circuit by systematically reducing entanglement in target states.<n>We conduct systematic experiments to evaluate the effectiveness of AQER, using synthetic datasets, classical image and language datasets, and a quantum many-body state datasets with up to 50 qubits.
  arXiv  Detail & Related papers  (2026-02-02T14:39:42Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Neural Architecture Search for Quantum Autoencoders [30.784540105806784]
  We propose a neural architecture search (NAS) framework that automates the design of quantum autoencoders.<n>By systematically evolving variational quantum circuit (VQC) configurations, our method seeks to identify high-performing hybrid quantum-classical autoencoders.
  arXiv  Detail & Related papers  (2025-11-24T15:55:44Z)
• Vectorized Attention with Learnable Encoding for Quantum Transformer [0.6766416093990318]
  We propose the Vectorized Quantum Transformer (VQT), a model that supports ideal masked attention matrix computation.<n>Our noise intermediate-scale quantum friendly VQT approach unlocks a novel architecture for end-to-end machine learning in quantum computing.
  arXiv  Detail & Related papers  (2025-08-25T20:33:14Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
  Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
  Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
  arXiv  Detail & Related papers  (2025-05-24T02:51:34Z)
• Q-Fusion: Diffusing Quantum Circuits [2.348041867134616]
  We propose a diffusion-based algorithm leveraging the LayerDAG framework to generate new quantum circuits.<n>Our results demonstrate that the proposed model consistently generates 100% valid quantum circuit outputs.
  arXiv  Detail & Related papers  (2025-04-29T14:10:10Z)
• 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)
• Near-Term Fermionic Simulation with Subspace Noise Tailored Quantum Error Mitigation [0.0]
  We introduce the Subspace Noise Tailoring (SNT) algorithm, which efficiently combines Symmetry Verification (SV) and low bias of Probabilistic Error Cancellation (PEC) QEM techniques.<n>We study the performance of our method by simulating the Trotterized time evolution of the spin-1/2 Fermi-Hubbard model (FHM) using a variety of local fermion-to-qubit encodings.
  arXiv  Detail & Related papers  (2025-03-14T18:20:54Z)
• Quantum autoencoders for image classification [0.0]
  Quantum autoencoders (QAEs) leverage classical optimization solely for parameter tuning.<n>This study introduces a novel image-classification approach using QAEs, achieving classification without requiring additional qubits.
  arXiv  Detail & Related papers  (2025-02-21T07:13:38Z)
• Quantum Data Encoding and Variational Algorithms: A Framework for Hybrid Quantum Classical Machine Learning [0.0]
  Quantum Machine Learning (QML) integrates the calculational framework of quantum mechanics with the adaptive properties of classical machine learning.<n>This article suggests a broad architecture that allows the connection between classical data pipelines and quantum algorithms.
  arXiv  Detail & Related papers  (2025-02-17T16:04:04Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning [0.5181797490530444]
  We implement methods for the efficient preparation of quantum states representing encoded image data using variational, genetic and matrix product state based algorithms. Results show that these methods can approximately prepare states to a level suitable for QML using circuits two orders of magnitude shallower than a standard state preparation implementation.
  arXiv  Detail & Related papers  (2023-09-18T01:49:36Z)
• Deep Quantum Error Correction [73.54643419792453]
  Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. In this work, we efficiently train novel emphend-to-end deep quantum error decoders. The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2023-01-27T08:16:26Z)
• 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.