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Adaptive quantum receiver designed by machine learning is demonstrated for discriminating multiple nonorthogonal coherent states, achieving reduced error rates of 20% (50%) over existing quantum (classical) receivers.

We experimentally demonstrate entanglement-enhanced optomechanical sensing in which entangled optical probes jointly read out the displacements of two mechanical membranes, enabling enhanced force sensitivities and enlarged measurement bandwidths.

Entanglement underpins a variety of quantum-enhanced communication, sensing, and computing capabilities. Entanglement-assisted communication (EACOMM) leverages entanglement pre-shared by communication parties to boost the rate of classical information transmission. Pioneering theory works showed that EACOMM can enable a communication rate well beyond the ultimate classical capacity of optical communications, but an experimental demonstration of any EACOMM advantage remains elusive. Here, we report the implementation of EACOMM surpassing the classical capacity over lossy and noisy bosonic channels. We construct a high-efficiency entanglement source and a phase-conjugate quantum receiver to reap the benefit of pre-shared entanglement, despite entanglement being broken by channel loss and noise. We show that EACOMM beats the Holevo-Schumacher-Westmoreland capacity of classical communication by up to 14.6%, when both protocols are subject to the same power constraint at the transmitter. As a practical performance benchmark, a classical communication protocol without entanglement assistance is implemented, showing that EACOMM can reduce the bit-error rate by up to 69% over the same bosonic channel. Our work opens a route to provable quantum advantages in a wide range of quantum information processing tasks., 12 pages, 5 figures. Comments are welcome

Variational quantum circuits (VQCs) built upon noisy intermediate-scale quantum (NISQ) hardware, in conjunction with classical processing, constitute a promising architecture for quantum simulations, classical optimization, and machine learning. However, the required VQC depth to demonstrate a quantum advantage over classical schemes is beyond the reach of available NISQ devices. Supervised learning assisted by an entangled sensor network (SLAEN) is a distinct paradigm that harnesses VQCs trained by classical machine-learning algorithms to tailor multipartite entanglement shared by sensors for solving practically useful data-processing problems. Here, we report the first experimental demonstration of SLAEN and show an entanglement-enabled reduction in the error probability for classification of multidimensional radio-frequency signals. Our work paves a new route for quantum-enhanced data processing and its applications in the NISQ era., 19 pages, 15 figures

We demonstrate quantum key distribution at 1.3-Gbit/s secret-key rates over a 10-dB-loss channel. By transmitting many photons per bit with multi-mode encoding our protocol overcomes channel loss to achieve gigabit-per-second rates without compromising security.

We report the first demonstration of floodlight quantum key distribution, which thrives on sending many photons per bit to overcome propagation loss. We achieve 52Mb/s secret-key rate over a channel with 10 dB loss.