Achieving last-iterate convergence in a QNN via an autonomous Gmetric driver
The article discusses the development of the NB Quantum-Inspired Neural Network (QNN) framework, which aims to achieve last-iterate convergence through innovative mechanisms. It highlights the use of a G-metric for self-correction and an entropic driver for active noise mitigation. This architecture preserves quantum memory and allows the system to navigate complex environments effectively.
- ▪The NB Quantum-Inspired Neural Network framework demonstrates emergent intelligence and autonomous noise mitigation.
- ▪The G-metric serves as an internal thermodynamic reading to evaluate the system's probability mass.
- ▪The entropic driver intervenes to maintain balance between localization and chaos in the system.
Opening excerpt (first ~120 words) tap to expand
psi.emergence: NB (No Boundary Gate) Quantum Inspired Neural Network psi.emergence contains the master source code for the NB (No Boundary Gate) Quantum-Inspired Neural Network framework built to demonstrate emergent intelligence, autonomous noise mitigation, and perfect last-iterate convergence. Unlike traditional neural architectures that rely on rigid parameter updates and hard-elimination, this system computes through the constructive and destructive interference of probability waves across 2,048 basis states (11 qubits). It naturally navigates high-dimensional, chaotic environments by bridging discrete parameter updates with continuous phase memory—mechanically mirroring the preservation of flow found in the Navier-Stokes equations.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.