GQKAE uses quantum-inspired Kolmogorov-Arnold networks to reduce parameters by 66% in generative quantum eigensolvers while achieving chemical accuracy on H4, N2, LiH, and other molecules.
Effect of data encoding on the expressive power of variational quantum-machine-learning models
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Parity-moment supervision improves forward-KL fit and unseen high-value-state recovery over coordinate-wise MSE in a controlled 12-qubit IQP Born-machine benchmark.
Diagonal ANOs are mathematically equivalent to full ANOs modulo unitary similarity, reducing k-local observable complexity from O(4^k) to O(2^k) and lowering measurement-side classical computation while including conventional VQCs as a special case.
Fully-connected VQCs reach 90–96% of quantum-transformer R² on tabular benchmarks with 40–50% fewer parameters, and expressibility saturates by circuit depth about three.
Magnitude-only encoding reaches 99.57% accuracy on 3-class and 71.19% on 8-class SAR tasks in hybrid models, beating phase-inclusive alternatives, while phase boosts pure quantum models by up to 21.65 points.
citing papers explorer
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Generative Quantum-inspired Kolmogorov-Arnold Eigensolver
GQKAE uses quantum-inspired Kolmogorov-Arnold networks to reduce parameters by 66% in generative quantum eigensolvers while achieving chemical accuracy on H4, N2, LiH, and other molecules.
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Parity Supervision as a Driver of Generalization in Quantum Generative Modeling
Parity-moment supervision improves forward-KL fit and unseen high-value-state recovery over coordinate-wise MSE in a controlled 12-qubit IQP Born-machine benchmark.
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Diagonal Adaptive Non-local Observables on Quantum Neural Networks
Diagonal ANOs are mathematically equivalent to full ANOs modulo unitary similarity, reducing k-local observable complexity from O(4^k) to O(2^k) and lowering measurement-side classical computation while including conventional VQCs as a special case.
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Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks
Fully-connected VQCs reach 90–96% of quantum-transformer R² on tabular benchmarks with 40–50% fewer parameters, and expressibility saturates by circuit depth about three.
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Magnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data
Magnitude-only encoding reaches 99.57% accuracy on 3-class and 71.19% on 8-class SAR tasks in hybrid models, beating phase-inclusive alternatives, while phase boosts pure quantum models by up to 21.65 points.