Fidelity-Preserving Quantum Encoding for Quantum Neural Networks

TL;DR

This paper introduces a Fidelity-Preserving Quantum Encoding framework that enhances quantum neural network data encoding by maintaining high fidelity, leading to improved accuracy on complex visual datasets.

Contribution

The proposed FPQE framework uses a convolutional encoder-decoder for near lossless data compression and quantum encoding, outperforming traditional methods on complex datasets.

Findings

FPQE achieves up to 10.2% higher accuracy on Cifar-10.

FPQE performs comparably to traditional methods on MNIST.

Performance improves with data complexity.

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.