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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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Learning to Learn Variational Quantum Algorithm.

Rui Huang, Xiaoqing Tan, Qingshan Xu

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    We introduce a meta-learning variational quantum algorithm (meta-VQA) that uses recurrent units and adaptive learning rates to efficiently find approximate ground states. This hybrid approach enhances performance on near-term quantum processors.

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    Area of Science:

    • Quantum Computing
    • Machine Learning
    • Computational Chemistry

    Background:

    • Variational quantum algorithms (VQAs) leverage classical optimizers for quantum computations.
    • Hybrid quantum-classical approaches are crucial for near-term quantum applications.
    • Efficiently finding approximate ground states is a key challenge in quantum computing.

    Purpose of the Study:

    • To develop a meta-learning variational quantum algorithm (meta-VQA) for improved optimization.
    • To reduce sampling requirements and enhance efficiency in VQAs.
    • To demonstrate the algorithm's applicability on near-term quantum hardware.

    Main Methods:

    • Implementation of a meta-VQA utilizing recurrent units as a meta-learner.
    • Integration of quantum stochastic gradient descent with an adaptive learning rate.
    • Deployment on TensorFlow Quantum for optimization tasks.

    Main Results:

    • Successful application to approximate quantum optimization of the Ising model.
    • Accurate variational quantum eigensolver calculations for H2, LiH, and HeH+.
    • Demonstrated potential for scalability to larger systems and problems.

    Conclusions:

    • The meta-VQA offers enhanced performance on near-term quantum processors.
    • The adaptive learning rate and recurrent units improve optimization efficiency.
    • This approach shows promise for advancing quantum computation applications.