AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications
Toshiaki Koike–Akino, Pu Wang, Ye Wang
Abstract
Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment. In this paper, we investigate a proof-of-concept approach using automated quantum machine learning (AutoQML) framework called AutoAnsatz to recognize human gesture. We address how to efficiently design quantum circuits to configure quantum neural networks (QNN). The effectiveness of AutoQML is validated by an in-house experiment for human pose recognition, achieving state-of-the-art performance greater than 80% accuracy for a limited data size with a significantly small number of trainable parameters.
Topics & Concepts
Computer scienceGestureQuantumArtificial neural networkComputer architectureState (computer science)Artificial intelligenceHuman–computer interactionMachine learningAlgorithmPhysicsQuantum mechanicsQuantum Computing Algorithms and ArchitectureNeural Networks and Reservoir ComputingQuantum Information and Cryptography