4 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
Quantum Port-Hamiltonian Neural Networks: Learning Conservative and Dissipative Dynamics via Measurement-Induced Nonlinearity
Dibakar Sigdel
We introduce Quantum Port-Hamiltonian Neural Networks (Q-pHNNs), parameterised quantum circuits that learn classical dynamics in a structure-preserving manner. The framework rests…
The RG-Flow Transformer: Encoding Scale-Free Dynamics in Scarce EEG
Dibakar Sigdel
Brain field potentials are scale-free: their power spectra follow a law whose aperiodic exponent tracks cortical state, and sleep depth in particular is a shift in .…
Variational Phasor Circuits for Phase-Native Brain-Computer Interface Classification
Dibakar Sigdel
We present the Variational Phasor Circuit (VPC), a deterministic classical learning architecture on the continuous unit-circle manifold. Inspired by variational quantum circu…
The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle
Dibakar Sigdel
Transformer models have redefined sequence learning, yet dot-product self-attention introduces a quadratic token-mixing bottleneck for long-context time-series. We introduce the Ph…
PhasorFlow: A Python Library for Unit Circle Based Computing
Dibakar Sigdel, Namuna Panday
We present PhasorFlow, an open-source Python library for computing on the unit circle. Inputs are encoded as complex phasors on the -torus (); as…