A deep photonic QNN achieves nonlinear operations via virtual Hilbert space expansion on a linear chip with four entanglement sources, demonstrated on classification, generation, and state preparation tasks.
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Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.
A gate freezing method improves convergence of gradient-free optimizers Rotosolve, Fraxis, and FQS for parameterized quantum circuits by reallocating resources to poorly optimized gates using previous iteration information.
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Photonic-Implemented Efficient Deep Quantum Neural Network via Virtual-Driven Hilbert Space Expansion
A deep photonic QNN achieves nonlinear operations via virtual Hilbert space expansion on a linear chip with four entanglement sources, demonstrated on classification, generation, and state preparation tasks.
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Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.
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Gate Freezing Method for Gradient-Free Variational Quantum Algorithms in Circuit Optimization
A gate freezing method improves convergence of gradient-free optimizers Rotosolve, Fraxis, and FQS for parameterized quantum circuits by reallocating resources to poorly optimized gates using previous iteration information.