Strong single-photon optomechanical coupling in a hybrid quantum system

Bogoliubov polaritons mediated strong indirect interaction between distant whispering-gallery-mode resonators

We propose a method to achieve a strong indirect interaction between two distant whispering-gallery-mode (WGM) resonators in a hybrid quantum system at room temperature, even when the distance between them exceeds 40 wavelengths. By exploiting the quantum critical point, we can greatly enhance both the effective damping rate and the coupling strengths between a WGM resonator and a low-frequency polariton. We introduce a large effective frequency detuning to suppress the effective damping rate while maintaining the enhanced coupling strength. The strong indirect interaction between separated WGM resonators is mediated by a far-off-resonant low-frequency polariton through virtual excitations in a process similar to Raman process. This proposal provides a viable approach to building a quantum network based on strongly coupled WGM resonators.

Online quantum time series processing with random oscillator networks

Reservoir computing is a powerful machine learning paradigm for online time series processing. It has reached state-of-the-art performance in tasks such as chaotic time series prediction and continuous speech recognition thanks to its unique combination of high computational power and low training cost which sets it aside from alternatives such as traditionally trained recurrent neural networks, and furthermore is amenable to implementations in dedicated hardware, potentially leading to extremely compact and efficient reservoir computers. Recently the use of random quantum systems has been proposed, leveraging the complexity of quantum dynamics for classical time series processing. Extracting the output from a quantum system without disturbing its state too much is problematic however, and can be expected to become a bottleneck in such approaches. Here we propose a reservoir computing inspired approach to online processing of time series consisting of quantum information, sidestepping the measurement problem. We illustrate its power by generalizing two paradigmatic benchmark tasks from classical reservoir computing to quantum information and introducing a task without a classical analogue where a random system is trained to both create and distribute entanglement between systems that never directly interact. Finally, we discuss partial generalizations where only the input or only the output time series is quantum.