Realizing a long coherence time quantum memory is a major challenge of current quantum technology. Until now, the longest coherence-time of a single qubit was reported as 660 s in a single 171Yb+ ion-qubit through the technical developments of sympathetic cooling and dynamical decoupling pulses, which addressed heating-induced detection inefficiency and magnetic field fluctuations. However, it was not clear what prohibited further enhancement. Here, we identify and suppress the limiting factors, which are the remaining magnetic-field fluctuations, frequency instability and leakage of the microwave reference-oscillator. Then, we observe the coherence time of around 5500 s for the 171Yb+ ion-qubit, which is the time constant of the exponential decay fit from the measurements up to 960 s. We also systematically study the decoherence process of the quantum memory by using quantum process tomography and analyze the results by applying recently developed resource theories of quantum memory and coherence. Our experimental demonstration will accelerate practical applications of quantum memories for various quantum information processing, especially in the noisy-intermediate-scale quantum regime.
Intelligent sensors have attracted substantial attention for various applications, including wearable electronics, artificial intelligence, healthcare monitoring, and human−machine interactions. However, there still remains a critical challenge in developing a multifunctional sensing system for complex signal detection and analysis in practical applications. Here, we develop a machine learning-combined flexible sensor for real-time tactile sensing and voice recognition through laser-induced graphitization. The intelligent sensor with a triboelectric layer can convert local pressure to an electrical signal through a contact electrification effect without external bias, which has a characteristic response behavior when exposed to various mechanical stimuli. With the special patterning design, a smart human−machine interaction controlling system composed of a digital arrayed touch panel is constructed to control electronic devices. Based on machine learning, the real-time monitoring and recognition of the changes of voice are achieved with high accuracy. The machine learning-empowered flexible sensor provides a promising platform for the development of flexible tactile sensing, real-time health detection, human−machine interaction, and intelligent wearable devices.
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