Benchmarking machine learning models for quantum state classification

TL;DR

This paper benchmarks various machine learning classification techniques on real quantum devices to improve the accuracy of quantum state discrimination amidst noise and decoherence.

Contribution

It provides a comparative analysis of multiple ML models for quantum state classification on actual hardware, highlighting their effectiveness.

Findings

Certain models outperform others in classification accuracy

Noise significantly impacts model performance

Benchmark results guide future quantum measurement strategies

Abstract

Quantum computing is a growing field where the information is processed by two-levels quantum states known as qubits. Current physical realizations of qubits require a careful calibration, composed by different experiments, due to noise and decoherence phenomena. Among the different characterization experiments, a crucial step is to develop a model to classify the measured state by discriminating the ground state from the excited state. In this proceedings we benchmark multiple classification techniques applied to real quantum devices.