Deep Learning, Complex Data Analysis, Genetic Algorithms, Advanced Mathematics
This paper introduces the Geometrically-grounded Self-Organization Minimum Viable Product (GSO-MVP).
This paper introduces the Geometrically-grounded Self-Organization Minimum Viable Product (GSO-MVP).
Utilizing Large Language Models, Cellular Automaton, and Bayesian Processes to efficiently navigate the search space of possibilities.
Merging the principles of Neural Cellular Automata (NCA) which learn emergent behaviors via gradient descent with Differentiable Logic Gate Networks.
By integrating deep learning with quantum annealing samplers, we automate discovery of highly effective QUBO matrices for specific problem classes.
We deploy the E-DQAS platform to find the ground state of the 1D Transverse Field Ising Model (TFIM), a benchmark for quantum phase transitions.
We use Hermitianization to map a non-Hermitian problem onto a Hamiltonian that is fully Hermitian and thus executable on quantum hardware.
We introduce an advanced application of our (E-DQAS) platform, now re-engineered to treat physical symmetry as a non-negotiable guiding principle.
Symmetry-Preserving Differentiable Quantum Architecture Search automates the discovery of physically valid, hardware-efficient circuits.
Inspired by Differentiable Logic Cellular Automata, E-DQAS reverse-engineers a problem's quantum dynamics into discrete, optimal circuit structures
Introducing a novel computational framework leveraging quaternion algebra as a unified language for both quantum and classical machine learning.
We posit that collective cognitive phenomena, such as financial markets, are guided by latent, pre-existing mathematical patterns.
Our approach is a synthesis of three distinct but complementary mathematical frameworks: Surreal Numbers, Prime Factorization, and verified axioms