Schmidt quantum compressor

TL;DR

The paper introduces the Schmidt quantum compressor, a deterministic quantum data compression method that outperforms variational autoencoders in fidelity and efficiency, with applications in quantum state reconstruction and classification.

Contribution

It presents a novel deterministic quantum compression technique based on Schmidt decomposition, reducing complexity and overcoming limitations of stochastic variational methods.

Findings

Achieves high fidelity in quantum state reconstruction

Reduces computational overhead compared to variational algorithms

Demonstrates utility in one-class classification tasks

Abstract

This work introduces the Schmidt quantum compressor, an innovative approach to quantum data compression that leverages the principles of Schmidt decomposition to encode quantum information efficiently. In contrast to traditional variational quantum autoencoders, which depend on stochastic optimization and face challenges such as shot noise, barren plateaus, and non-convex optimization landscapes, our deterministic method substantially reduces the complexity and computational overhead of quantum data compression. We evaluate the performance of the compressor through numerical experiments, demonstrating its ability to achieve high fidelity in quantum state reconstruction compared to variational quantum algorithms. Furthermore, we demonstrate the practical utility of the Schmidt quantum compressor in one-class classification tasks.