quantum-computing

name	quantum-computing
description	Quantum computing concepts and NeuralBlitz quantum neuron implementation
license	MIT
compatibility	opencode
metadata	{"audience":"developers","category":"domain-specific"}

What I do

• Implement quantum-inspired neural networks
• Model quantum spiking neurons
• Handle quantum state evolution
• Manage superposition and entanglement
• Implement quantum tunneling behavior
• Handle coherence and decoherence
• Work with multi-reality networks
• Optimize quantum circuit simulation

When to use me

When working with the NeuralBlitz quantum computing components or implementing quantum-inspired algorithms.

Quantum State Representation

import numpy as np
from typing import Optional, Tuple
from scipy.linalg import expm


class QuantumState:
    """
    Quantum state vector in Hilbert space.
    
    The state is represented as a normalized complex vector.
    For n qubits, the state space has dimension 2^n.
    """
    
    def __init__(self, num_qubits: int) -> None:
        """
        Initialize quantum state to |0⟩^n.
        
        Args:
            num_qubits: Number of qubits (must be positive integer)
        """
        self.num_qubits = num_qubits
        self.dim = 2 ** num_qubits
        self.state = np.zeros(self.dim, dtype=complex)
        self.state[0] = 1.0  # |0⟩ state
    
    @property
    def data(self) -> np.ndarray:
        """Return the state vector (read-only view)."""
        return self.state.copy()
    
    def normalize(self) -> None:
        """Normalize state vector to unit length."""
        norm = np.linalg.norm(self.state)
        if norm > 0:
            self.state /= norm
    
    def measure(self) -> int:
        """
        Measure state in computational basis.
        
        Returns:
            Measured basis state index with probability = |<x|ψ⟩|²
        """
        probs = np.abs(self.state) ** 2
        probs /= probs.sum()
        return np.random.choice(len(self.state), p=probabilities)
    
    def fidelity(self, other: 'QuantumState') -> float:
        """
        Calculate fidelity between two quantum states.
        
        Fidelity = |⟨ψ|φ⟩|² for pure states.
        
        Args:
            other: Another quantum state to compare
            
        Returns:
            Fidelity value between 0 and 1
        """
        overlap = np.vdot(self.state, other.state)
        return np.abs(overlap) ** 2

Quantum Gates

class QuantumGate:
    """Quantum gate operations on state vectors."""
    
    @staticmethod
    def hadamard(num_qubits: int, target: int) -> np.ndarray:
        """
        Create Hadamard gate for superposition.
        
        H|0⟩ = (|0⟩ + |1⟩)/√2
        H|1⟩ = (|0⟩ - |1⟩)/√2
        """
        H = np.array([[1, 1], [1, -1]]) / np.sqrt(2)
        return QuantumGate.expand_gate(H, num_qubits, target)
    
    @staticmethod
    def pauli_x(num_qubits: int, target: int) -> np.ndarray:
        """Pauli-X (NOT) gate: |0⟩→|1⟩, |1⟩→|0⟩"""
        X = np.array([[0, 1], [1, 0]])
        return QuantumGate.expand_gate(X, num_qubits, target)
    
    @staticmethod
    def pauli_z(num_qubits: int, target: int) -> np.ndarray:
        """Pauli-Z gate: |1⟩→-|1⟩, |0⟩→|0⟩"""
        Z = np.array([[1, 0], [0, -1]])
        return QuantumGate.expand_gate(Z, num_qubits, target)
    
    @staticmethod
    def cnot(num_qubits: int, control: int, target: ) -> np.ndarray:
        
        dim =  ** num_qubits
        matrix = np.eye(dim, dtype=)
        
         i  (dim):
             (i >> control) &    (i >> target) & :
                j = i | ( << target)
                k = i & ~( << target)
                matrix[j, i] = 
                matrix[k, i] = 
        
         matrix
    

     () -> np.ndarray:
        
        RZ = np.array([
            [np.exp(- * angle / ), ],
            [, np.exp( * angle / )]
        ])
         QuantumGate.expand_gate(RZ, num_qubits, target)
    

     () -> np.ndarray:
        
        dim =  ** num_qubits
        
         target <   target >= num_qubits:
             ValueError()
        
        result = np.eye(, dtype=)
        
         i  (num_qubits):
             i == target:
                result = np.kron(result, gate)
            :
                result = np.kron(result, np.eye(, dtype=))
        
         result

Quantum Spiking Neuron

class QuantumSpikingNeuron:
    """
    Quantum-inspired spiking neuron model.
    
    Combines quantum mechanics with spiking neural network principles:
    - Quantum superposition for input integration
    - Quantum tunneling for spike generation
    - Coherence time limiting neural activity
    """
    
    def __init__(
        self,
        num_qubits: int = 4,
        coherence_time: float = 100.0,
        tunneling: float = 0.1,
        threshold: float = 0.5,
    ) -> None:
        self.num_qubits = num_qubits
        self.coherence_time = coherence_time
        self.tunneling = tunneling
        self.threshold = threshold
        self.state = QuantumState(num_qubits)
        self.weights = np.random.randn(num_qubits) * 0.1
        self.spike_history: list[float] = []
        self.last_update: float = 0.0
    
    def evolve(
        self,
        inputs: np.ndarray,
        dt: float,
        hamiltonian: Optional[np.ndarray] = None
    ) -> Tuple[np.ndarray, bool]:
        """
        Evolve quantum state and potentially spike.
        
        Args:
            inputs: Input currents from connected neurons
            dt: Time step in milliseconds
            hamiltonian: Optional custom Hamiltonian matrix
            
        Returns:
            Tuple of (output_state, did_spike)
        """
         (inputs) != .num_qubits:
             ValueError()
        
        .last_update = dt
        
        
         hamiltonian  :
            hamiltonian = np.diag(-.weights * inputs)
        
        
        evolution = expm(- * hamiltonian * dt)
        
        
        .state.state = evolution @ .state.state
        
        
        spike_probability = ._calculate_spike_probability()
        should_spike = np.random.random() < spike_probability
        
         should_spike:
            ._emit_spike()
             .state.state, 
        
         .state.state, 
    
     () -> :
        
        excited_prob = np.(np.(.state.state[ ** (.num_qubits - ):]) ** )
        coherence_factor = np.exp(-.last_update / .coherence_time)
        
         excited_prob * .tunneling * coherence_factor
    
     () -> :
        
        .spike_history.append(.last_update)
        
        
        .state = QuantumState(.num_qubits)
        
        
        ._refractory_timer =   
    
     () -> :
        
         np.(
            np.arange( ** .num_qubits) * 
            np.(.state.state) ** 
        )

Multi-Reality Network

class MultiRealityNetwork:
    """
    Network that spans multiple parallel realities.
    
    Each reality maintains its own quantum state,
    and entanglement allows correlation between realities.
    """
    
    def __init__(
        self,
        num_realities: int = 4,
        neurons_per_reality: int = 20,
        entanglement_strength: float = 0.1,
    ) -> None:
        self.num_realities = num_realities
        self.neurons_per_reality = neurons_per_reality
        self.entanglement_strength = entanglement_strength
        
        self.neurons: list[list[QuantumSpikingNeuron]] = []
        self.entanglement_matrix: np.ndarray
        
        self._initialize_network()
    
    def _initialize_network(self) -> None:
        """Initialize neurons in each reality."""
        for r in range(self.num_realities):
            reality_neurons = [
                QuantumSpikingNeuron(
                    num_qubits=4,
                    coherence_time=100.0,
                    tunneling=0.1,
                )
                for _ in range(self.neurons_per_reality)
            ]
            self.neurons.append(reality_neurons)
        
        self.entanglement_matrix = np.eye(.num_realities)
    
     () -> [[]]:
        
        spike_matrix = []
        
         r  (.num_realities):
            reality_spikes = []
            
             neuron  .neurons[r]:
                inputs = ._get_inputs(r, neuron)
                _, spiked = neuron.evolve(inputs, dt)
                reality_spikes.append(spiked)
            
            spike_matrix.append(reality_spikes)
        
        ._apply_entanglement(spike_matrix)
        
         spike_matrix
    
     () -> np.ndarray:
        
        inputs = np.zeros(target_neuron.num_qubits)
        
         i, neuron  (.neurons[reality]):
             neuron   target_neuron:
                inputs[i] = neuron.get_membrane_potential()
        
         inputs
    
     () -> :
        
        spike_array = np.array(spike_matrix)
        
         i  (.num_realities):
             j  (i + , .num_realities):
                correlation = .entanglement_matrix[i, j]
                
                 correlation > :
                    ._entangle_realities(
                        spike_array[i],
                        spike_array[j],
                        correlation
                    )
    
     () -> :
        
        agreement = np.(spikes_a == spikes_b) / (spikes_a)
        
         agreement > :
            
            adjustment = strength * ( - agreement)
         agreement < :
            
            adjustment = -strength * ( - agreement)
        :
            adjustment = 
        
        .entanglement_matrix = np.clip(
            .entanglement_matrix + adjustment * ,
            , 
        )