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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3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
quant-ph 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Directly training soft-unitary matrices with a unitarity regularization term and converting them to circuits via alignment enables faster training and lower loss than gate-based optimization on small quantum classification and reinforcement learning tasks.
PAPUS is a pair-adaptive quantum classification method in Pauli space that reaches over 90% accuracy on 9 datasets with lower measurement and gate costs and only 1.67% accuracy drop under noise compared to 9.44% for baselines.
citing papers explorer
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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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Soft-Quantum Algorithms
Directly training soft-unitary matrices with a unitarity regularization term and converting them to circuits via alignment enables faster training and lower loss than gate-based optimization on small quantum classification and reinforcement learning tasks.
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PAPUS: Pauli-Space-Based Multiclass Quantum Classification
PAPUS is a pair-adaptive quantum classification method in Pauli space that reaches over 90% accuracy on 9 datasets with lower measurement and gate costs and only 1.67% accuracy drop under noise compared to 9.44% for baselines.