GitHub - unbconductor/psi.emergence: psi.emergence contains the master source code for the NB (No Boundary Gate) Quantum-Inspired Neural Network framework, utilizing continuous phase memory to demonstrate emergent intelligence, autonomous noise mitigation, and perfect last-iterate convergence. · GitHub
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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).<br>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.
Core Mechanics
The G-Metric (The Invariant Compass)<br>The system evaluates its own probability mass using a custom state-dispersion invariant known as the G-metric:G = (N * ΣP_i² - 1) / (N - 1)<br>Rather than forcing the network to choose a specific basis state, the G-metric acts as an internal thermodynamic reading.<br>It measures how localized or how uniform the current state vector has become, allowing the system to self-correct without external micro-management. The target equilibrium for this system is tuned to 0.5189 in order to achieve convergence.
The Entropic Driver (Active Noise Mitigation)<br>Located within pathway_entropic_driver,<br>this mechanism is the engine of the QNN's emergent intelligence.<br>When environmental turbulence is introduced, algorithmic guardrails trigger autonomous interventions:<br>Preventing Rigidity: If the system becomes overly localized (G > G_{high}), the driver dynamically forces probability mass back toward a uniform distribution.<br>Preventing Pure Chaos: If the system falls into pure noise (G<br>Phase Preservation & Implicit Quantum Memory<br>Traditional classical neural networks simply update probabilities. This architecture, however, explicitly preserves the "hidden" variable of quantum memory: the complex phase. This phase preservation serves as intrinsic "opponent-action feedback," resulting in smooth, exponential decay during optimization and perfect last-iterate convergence.
The Nature of the Memory (A Complex Array)<br>The core state of the system is stored in self.psi_orchestra, initialized as a NumPy array of complex numbers. For an 11-qubit system, this array contains 2^{11} or 2,048 distinct elements, each corresponding to a specific basis state (e.g., |00000000000)). Each complex number holds two vital pieces of information:
Amplitude (Magnitude): Dictates the probability of measuring that specific state.
Phase (Angle): Dictates how this state will constructively or destructively interfere with other states in the future.
The Physics of Compounding (Markovian Evolution)<br>The simulated quantum system evolves via a Markov chain, expressed mathematically as:<br>$|\psi_{t+1}\rangle = U_t|\psi_t\rangle$Here, $|\psi_t\rangle$ is the psi_orchestra at the current step, and $U_t$ is the U_NB_master_conductor operation. The state at step t is the mathematical product of all previous transformations. It is an "implicit" memory because the history is fully integrated into the present form.
Code-Level Implementation<br>When the entropic driver...