A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning
This work addresses a significant barrier for quantum machine learning practitioners by providing a scalable, noise-resilient solution for qubit-efficient encoding, though it appears incremental as it builds on existing dimensionality reduction and encoding techniques.
The paper tackled the problem of efficiently embedding high-dimensional datasets onto noisy, low-qubit quantum systems for quantum machine learning by proposing Quantum Principal Geodesic Analysis (qPGA), a non-invertible method that reduces qubit requirements and enhances resistance to reconstruction attacks, achieving over 99% accuracy and F1-score on MNIST and Fashion-MNIST in downstream classification tasks.
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.