Benchmarking machine learning models for quantum state classification
This work addresses the problem of state classification for quantum computing researchers, but appears incremental as it benchmarks existing methods without introducing new approaches.
The authors benchmarked multiple classification techniques for distinguishing ground and excited states in real quantum devices, a crucial step in quantum computing calibration, but did not report specific performance numbers or results.
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.