QuantumXCT learns parameterized quantum circuits to model interaction-induced unitary transformations between non-interacting and interacting cellular state distributions from transcriptomic profiles.
In: Gomez, S., Hennart, J.-P
4 Pith papers cite this work, alongside 329 external citations. Polarity classification is still indexing.
4
Pith papers citing it
329
external citations · OpenAlex
citation-role summary
method 1
citation-polarity summary
roles
method 1polarities
use method 1representative citing papers
A new optimization-based calibration method allows accurate spatially varying power-law attenuation modeling in ultrasound wave simulations with mean errors below 3%.
A quantum-inspired ARIMA pipeline using swap-test correlations and variational circuits is proposed, but its validation is confounded by order selection and a missing same-order classical baseline.
Systematic benchmarks on NACA0012, RAE2822, and ONERA M6 cases show derivative-free optimizers competitive with adjoint-based methods and stronger in higher dimensions.
citing papers explorer
-
QuantumXCT: Learning Interaction-Induced State Transformation in Cell-Cell Communication via Quantum Entanglement and Generative Modeling
QuantumXCT learns parameterized quantum circuits to model interaction-induced unitary transformations between non-interacting and interacting cellular state distributions from transcriptomic profiles.
-
Spatially heterogeneous power-law attenuation with multiple relaxation mechanisms for ultrasound modeling
A new optimization-based calibration method allows accurate spatially varying power-law attenuation modeling in ultrasound wave simulations with mean errors below 3%.
-
QARIMA: A Quantum Approach To Classical Time Series Analysis
A quantum-inspired ARIMA pipeline using swap-test correlations and variational circuits is proposed, but its validation is confounded by order selection and a missing same-order classical baseline.
-
Derivative-free optimization is competitive for aerodynamic design optimization in moderate dimensions
Systematic benchmarks on NACA0012, RAE2822, and ONERA M6 cases show derivative-free optimizers competitive with adjoint-based methods and stronger in higher dimensions.