MetaMorphQ defines five physics-derived invariants for VQE circuits that enable oracle-free testing with zero false positives and Youden's J of 0.57 on 500 benchmarks versus 0.02 for convergence testing.
Quantum circuit learning
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5representative citing papers
QLAM extends state-space models with quantum superposition in the hidden state for linear-time long-sequence modeling and reports consistent gains over RNN and transformer baselines on sequential image tasks.
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.
Quantum reservoir computing with distributed architectures reduces time-series forecasting errors by up to 78.8% MAE and 72.3% RMSE in NISQ simulations compared to classical methods.
QYOLO replaces deep YOLO backbone C2f modules with a shared sinusoidal quantum-inspired mixer, reducing parameters by 20% and GFLOPs by 12% with under 0.5 pp mAP drop on VisDrone2019.
citing papers explorer
-
MetaMorphQ: Physics-Based Metamorphic Testing of Variational Quantum Circuits
MetaMorphQ defines five physics-derived invariants for VQE circuits that enable oracle-free testing with zero false positives and Youden's J of 0.57 on 500 benchmarks versus 0.02 for convergence testing.
-
QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling
QLAM extends state-space models with quantum superposition in the hidden state for linear-time long-sequence modeling and reports consistent gains over RNN and transformer baselines on sequential image tasks.
-
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.
-
Scalable Quantum Reservoir Computing over Distributed Quantum Architectures
Quantum reservoir computing with distributed architectures reduces time-series forecasting errors by up to 78.8% MAE and 72.3% RMSE in NISQ simulations compared to classical methods.
-
QYOLO: Lightweight Object Detection via Quantum Inspired Shared Channel Mixing
QYOLO replaces deep YOLO backbone C2f modules with a shared sinusoidal quantum-inspired mixer, reducing parameters by 20% and GFLOPs by 12% with under 0.5 pp mAP drop on VisDrone2019.