3 papers
quant-ph2026
Quantifying the Hadamard Resilience Law: Discovery of the Coherence Gap in NISQ-Era Classifiers
Wladimir Silva
We report on a fundamental disparity between stochastic noise models and algorithmic performance in NISQ-era classifiers. Utilizing the ibm_kingston processor, we characterize the…
cs.LG2026
VoodooNet: Achieving Analytic Ground States via High-Dimensional Random Projections
Wladimir Silva
We present VoodooNet, a non-iterative neural architecture that replaces the stochastic gradient descent (SGD) paradigm with a closed-form analytic solution via Galactic Expansion.…
quant-ph2026
AQ-Stacker: An Adaptive Quantum Matrix Multiplication Algorithm with Scaling via Parallel Hadamard Stacking
Wladimir Silva
Matrix multiplication (MatMul) is the computational backbone of modern machine learning, yet its classical complexity remains a bottleneck for large scale data processing. We propo…