Fidelity-Preserving Quantum Encoding for Quantum Neural Networks
Yuhu Lu, Jinjing Shi
TL;DR
The work tackles the problem of encoding high-dimensional visual data into quantum states for quantum neural networks on NISQ devices, where fidelity loss during transformation can degrade learning. It introduces Fidelity-Preserving Quantum Encoding (FPQE), a convolutional encoder–decoder trained to preserve both global structure and local textures, with the resulting latent representation mapped to quantum states via amplitude encoding. FPQE demonstrates superior downstream quantum classification performance, particularly on complex datasets like CIFAR-10, and shows fidelity metrics such as SSIM better predict QNN performance than standard pixel-wise measures. The findings imply a practical, hardware-efficient pathway for quantum representation learning, enabling more robust quantum vision tasks on near-term quantum hardware.
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.