Entanglement-enhanced machine learning with quantum data acquisition

    公开(公告)号:US12067456B2

    公开(公告)日:2024-08-20

    申请号:US16953270

    申请日:2020-11-19

    CPC classification number: G06N10/00 G06N20/00

    Abstract: A system for entanglement-enhanced machine learning with quantum data acquisition includes a first variational circuit that generates a plurality of entangled probe light fields that interacts with a sample and is then processed by a second variational quantum circuit to produce at least one detection light field, a detector is used to measure a property of the at least one detection light field, and the first and second variational quantum circuits are optimized though machine learning. A method for entanglement-enhanced machine learning with quantum data acquisition includes optimizing a setting of a first and second variational quantum circuits, which includes probing a training-set with a plurality of entangled probe light fields generated by the first variational quantum circuit, and measuring a phase property of at least one detection light fields generated by the second variational quantum circuit from the plurality of entangled probe light fields after interaction with the training-set.

    VIRTUAL REALITY NEUROPSYCHOLOGICAL ASSESSMENT

    公开(公告)号:US20240257971A1

    公开(公告)日:2024-08-01

    申请号:US18430445

    申请日:2024-02-01

    CPC classification number: G16H50/20

    Abstract: A virtual reality neuropsychological assessment (VRNA) system uses a deep learning network and a VR headset to administer multi-domain assessments of human cognitive performance. The deep learning network is trained to identify features in sensor data indicative of neuropsychological performance and classify users based on the features identified in the sensor data. The VR headset provides a user with a virtual simulation of an activity involving decision-making scenarios. During the virtual simulation, sensor data via a plurality of sensors of the VR headset is captured. The sensor data is applied to the deep learning network to identify features of the user and classify the user based on the features into a neuropsychological domains, such as attention, memory, processing speed, and executive function. Sensor data includes eye-tracking, hand-eye motor coordination, reaction time, working memory, learning and delayed memory, and inhibitory control.

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