A New Belief System

Random Matrix Theory Applied to Deep Belief Signaling Networks

See references for quantum computing, graph signal processing, and belief propagation:📖

A Formal Belief System

Python and Qiskit Implementation

# Run inference on information shared between random populations of...
belief_prop = bp.random(population, environments, neural_architectures: neural_ode, gan, cnn, rnn; depth: multi, ...)

# Analyze intersection of neural architectures and environments(graph signal processing)
GSP.engine(analysis(union for belief_prop), algo_seq: [forward, backward, forward])

GSP.engine can be further optimized through quantum topological search:

# Initialization
import matplotlib.pyplot as plt
import numpy as np

# Importing Qiskit
from qiskit import IBMQ, Aer, QuantumCircuit, ClassicalRegister, QuantumRegister, execute
from qiskit.providers.ibmq import least_busy
from qiskit.quantum_info import Statevector

# Import basic plot tools
from qiskit.visualization import plot_histogram

# Initialize quantum components
n = 2 # qubits
grover_circuit = QuantumCircuit(n)
grover_circuit = initialize_s(grover_circuit, [0,1])
grover_circuit.draw()

def initialize_s(qc, qubits):
    """Apply a H-gate to 'qubits' in qc"""
    for q in qubits:
        qc.h(q)
    return qc

. . . . . And so on

See references for quantum computing, graph signal processing, and belief propagation: 📖