Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance

TL;DR

This paper explores implementing quantum perceptrons on Rydberg atom arrays for multi-class classification and noise-resilient quantum machine learning, demonstrating high accuracy and discussing experimental realizations.

Contribution

It extends quantum perceptrons to multi-output and multi-class tasks using Rydberg atoms, enhancing noise tolerance and practical implementation insights.

Findings

Achieved high accuracy in phase classification tasks.

Demonstrated 95% accuracy in multi-class entanglement classification.

Discussed error bounds for approximating continuous functions.

Abstract

Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML). At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation. Canonically, a QP features $N$ input qubits and one output qubit, and is used to determine whether an input state belongs to a specific class. Rydberg atoms, with their extended coherence times and scalable spatial configurations, provide an ideal platform for implementing QPs. In this work, we explore the implementation of QPs on Rydberg atom arrays, assessing their performance in tasks such as phase classification between Z2, Z3, Z4 and disordered phases, achieving high accuracy, including in the presence of noise. We also perform multi-class entanglement classification by extending the QP model to include multiple output qubits, achieving 95\% accuracy in distinguishing noisy, high-fidelity states based on separability. Additionally, we discuss the experimental realization of QPs on Rydberg platforms using both single-species and dual-species arrays, and examine the error bounds associated with approximating continuous functions.