While error-corrected scalable quantum computation is not yet available, researchers are seeking quantum advantages in practical tasks using so-called noisy intermediate-scale quantum devices: quantum processors without error correction, where noise limits their size. We explore how a noisy intermediate-scale quantum device can enhance machine-learning tasks that are related to quantum-sensing scenarios in a photonic quantum information-processing platform.

By using quantum circuits to generate and detect entanglement, we design an entangled sensor network to perform classification and compression tasks, such as support-vector machine classification and principal component analysis, beyond the precision of classical schemes. Unlike various quantum algorithms that rely on a priori classical data as the input, we model scenarios where sensing data are the inputs to the quantum channels and perform calculations directly on the physical layer.

Our work opens a new paradigm for quantum-enhanced machine learning and sensing. This method requires only off-the-shelf quantum optical components and is therefore ready for experimental demonstrations. As a machine-learning platform, the scheme can be widely applied to enhance tasks such as deep-space communication, biosensing, and chemical-compound detection.