Equivariant sp-QCNN encodes general symmetries with group theory, splits circuits at pooling layers to preserve symmetry while enabling parallel measurements, and shows improved efficiency and trainability over standard equivariant QCNNs in noisy quantum data classification.
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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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Resource-efficient equivariant quantum convolutional neural networks
Equivariant sp-QCNN encodes general symmetries with group theory, splits circuits at pooling layers to preserve symmetry while enabling parallel measurements, and shows improved efficiency and trainability over standard equivariant QCNNs in noisy quantum data classification.
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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.