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.
Expressibility and entan- gling capability of parameterized quantum circuits for hybrid quantum- classical algorithms,
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5representative citing papers
The hardware-compatible Brick-Circuit generator produces quantum test states with higher expressibility and entanglement than existing generators at shallower circuit depths.
A 4-qubit quantum feature pyramid gating architecture raises mean IoU from 0.8404 to 0.9389 over classical addition in controlled ablations on the TGS salt segmentation dataset.
HyFuHAD fuses classical Einstein fuzzy detection from multiple membership functions with quantum fuzzy detection to achieve claimed state-of-the-art performance in unsupervised hyperspectral anomaly detection.
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.
citing papers explorer
-
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.
-
Randomized and Diverse Input State Generation for Quantum Program Testing
The hardware-compatible Brick-Circuit generator produces quantum test states with higher expressibility and entanglement than existing generators at shallower circuit depths.
-
Quantum Feature Pyramid Gating for Seismic Image Segmentation
A 4-qubit quantum feature pyramid gating architecture raises mean IoU from 0.8404 to 0.9389 over classical addition in controlled ablations on the TGS salt segmentation dataset.
-
Hyperspectral Anomaly Detection Using Einstein Fuzzy Computing and Quantum Neural Network
HyFuHAD fuses classical Einstein fuzzy detection from multiple membership functions with quantum fuzzy detection to achieve claimed state-of-the-art performance in unsupervised hyperspectral anomaly detection.
-
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.