Forward gradient framework for PQCs unifies SPSA and parameter-shift as limits, introduces QUIVER adaptive optimizer with closed-form measurement allocation, and demonstrates efficient training of 60-qubit circuits on ECG5000 and MNIST.
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QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
Framework using Butterfly circuits, layer-wise training and parallel parameter-shift reduces QNN training cost to O(log n) circuit evaluations, validated on MIMIC-III clinical data with hardware execution at 16 qubits and simulation at 32.
citing papers explorer
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Adaptive directional gradients for parameterised quantum circuits
Forward gradient framework for PQCs unifies SPSA and parameter-shift as limits, introduces QUIVER adaptive optimizer with closed-form measurement allocation, and demonstrates efficient training of 60-qubit circuits on ECG5000 and MNIST.
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QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
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Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation
Framework using Butterfly circuits, layer-wise training and parallel parameter-shift reduces QNN training cost to O(log n) circuit evaluations, validated on MIMIC-III clinical data with hardware execution at 16 qubits and simulation at 32.