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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Power Characterization of Noisy Quantum Kernels.

Yabo Wang, Bo Qi, Xin Wang

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    Noisy quantum kernel methods may offer quantum advantages but suffer from noise in current quantum computers. This study reveals that noise significantly degrades prediction capability, even with small generalization errors, warning against their use without careful consideration.

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    Area of Science:

    • Quantum computing
    • Quantum machine learning

    Background:

    • Quantum kernel methods are promising for quantum machine learning (QML).
    • Noisy Intermediate-Scale Quantum (NISQ) era introduces noise that degrades QML algorithm performance.

    Purpose of the Study:

    • Theoretically characterize the prediction capability of noisy quantum kernels.
    • Analyze the impact of depolarizing noise on quantum kernel methods.

    Main Methods:

    • Theoretical analysis of noisy quantum kernels.
    • Quantitative description of prediction capability degradation.

    Main Results:

    • Depolarizing noise severely degrades quantum kernel methods' prediction capability, even with small generalization errors.
    • Prediction capability decreases with noise rate, sample size, qubit count, and noisy layers.
    • A threshold in noisy layers drastically reduces prediction capability.

    Conclusions:

    • Noisy quantum kernels may have poor prediction capabilities in the NISQ era.
    • Provides a warning for employing noisy quantum kernel methods.
    • Offers theoretical guidelines for developing practical quantum kernel algorithms.