Parthasarathy H Applications of Quantum Field Theory in Machine Learning 2026 [TorrentX]

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Comprehensive analysis of quantum neural networks through renormalization techniques and supersymmetric field theory applications in computational modeling
Investigation of quantum field dynamics with noise integration, filtering mechanisms, and scattering processes in curved spacetime environments
Study of adaptive beamforming methodologies combined with quantum neural networks for brain modeling and evolving field system applications
Examination of mixed-state dynamics in superstring theory frameworks with emphasis on quantum noisy fields and supersymmetric effects
Analysis of extended Kalman filter integration with quantum neural networks for transmission line control and field estimation optimization
The work explores extended Kalman filter methodologies for transmission line control, field estimation, and symmetry-broken dynamics in signal processing systems for advanced computational modeling applications.
Preface
QNN Using Renormalization of Fields and Supersymmetric Field Theory
Quantum Neural Networks: Scat Applications of Quantum Field Theory to Problems in Machine Learning
QNNs with Noisy Harmonic Oscillators, Strings, and Gravitational Control
Adaptive Beamforming and QNNs for Evolving Brain and Field
Quantum Noisy Fields and Supersymmetric Effects: QNNs with Mixed-State Dynamics in Superstring Theory
Quantum Fields, Signal Theory, and QNNs via Symmetry-Broken Dynamics
Quantum Field Theory with Noise, Filters, Scattering, and Curved Spacetime
QNNs and EKF for Transmission Line Control and Field Estimation