Fidelity-Preserving Quantum Encoding for Quantum Neural Networks

• URL: http://arxiv.org/abs/2511.15363v1
• Date: Wed, 19 Nov 2025 11:44:39 GMT
• Title: Fidelity-Preserving Quantum Encoding for Quantum Neural Networks
• Authors: Yuhu Lu, Jinjing Shi,
• Abstract summary: Existing encoding schemes discard spatial and semantic information when adapting high-dimensional images to the limited qubits of Noisy Intermediate-Scale QuantumNISQ devices.<n>We propose a Fidelity-Preserving Quantum Preserving framework that performs near lossless data compression and quantum encoding.<n> Experimental results show that FPQE performs comparably to conventional methods on simple datasets such as MNIST, while achieving clear improvements on more complex ones.
• Score: 2.9621136443259872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficiently encoding classical visual data into quantum states is essential for realizing practical quantum neural networks (QNNs). However, existing encoding schemes often discard spatial and semantic information when adapting high-dimensional images to the limited qubits of Noisy Intermediate-Scale Quantum (NISQ) devices. We propose a Fidelity-Preserving Quantum Encoding (FPQE) framework that performs near lossless data compression and quantum encoding. FPQE employs a convolutional encoder-decoder to learn compact multi-channel representations capable of reconstructing the original data with high fidelity, which are then mapped into quantum states through amplitude encoding. Experimental results show that FPQE performs comparably to conventional methods on simple datasets such as MNIST, while achieving clear improvements on more complex ones, outperforming PCA and pruning based encodings by up to 10.2\% accuracy on Cifar-10. The performance gain grows with data complexity, demonstrating FPQE's ability to preserve high-level structural information across diverse visual domains. By maintaining fidelity during classical to quantum transformation, FPQE establishes a scalable and hardware efficient foundation for high-quality quantum representation learning.

Related papers

• Towards Explainable Quantum AI: Informing the Encoder Selection of Quantum Neural Networks via Visualization [13.887583052955277]
  Quantum Neural Networks (QNNs) represent a promising fusion of quantum computing and neural network architectures.<n>A crucial component of QNNs is the encoder, which maps classical input data into quantum states.<n>XQAI-Eyes is a novel visualization tool that enables QNN developers to compare classical data features with their corresponding encoded quantum states.
  arXiv  Detail & Related papers  (2025-12-16T08:21:47Z)
• 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)
• 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)
• A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning [11.861417859173859]
  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.
  arXiv  Detail & Related papers  (2025-06-24T03:09:16Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• 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)
• Tensor-Based Binary Graph Encoding for Variational Quantum Classifiers [3.5051814539447474]
  We propose a novel quantum encoding framework for graph classification using Variational Quantums (VQCs)<n>By constructing slightly more complex circuits tailored for graph encoding, we demonstrate that VQCs can effectively classify graphs within the constraints of current quantum hardware.
  arXiv  Detail & Related papers  (2025-01-24T02:26:21Z)
• Quantum Sparse Coding and Decoding Based on Quantum Network [1.0683439960798695]
  We propose a symmetric quantum neural network for realizing sparse coding and decoding algorithms. Our networks consist of multi-layer, two-level unitary transformations that are naturally suited for optical circuits. We achieve sparse coding and decoding of binary and grayscale images in classical problems, as well as that of complex quantum states in quantum problems separately.
  arXiv  Detail & Related papers  (2024-06-10T04:21:27Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• 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)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)

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.