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Fix test failures and add missing consciousness tests #140

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940d0b6
Reorganize repository structure: Move quantum_consciousness and quant…
devin-ai-integration[bot] Oct 17, 2024
a12a85f
Refactor fusion method in MultiModalLearning class to consistently us…
devin-ai-integration[bot] Oct 17, 2024
7f5c1a9
Move consciousness_simulation.py to NeuroFlex/NeuroFlex/quantum_consc…
devin-ai-integration[bot] Oct 17, 2024
080323e
Fix: Update import statements for consciousness_simulation module
devin-ai-integration[bot] Oct 17, 2024
4715a56
Fix circular import issue between NeuroFlex and core_neural_networks
devin-ai-integration[bot] Oct 17, 2024
5c97b88
Fix test failures and add missing consciousness tests
devin-ai-integration[bot] Oct 17, 2024
8da2f55
Fix test failures and add missing consciousness tests
devin-ai-integration[bot] Oct 17, 2024
06e74bc
Fix BCIProcessor shape mismatch and add mock AlphaFold path for testing
devin-ai-integration[bot] Oct 18, 2024
1fbc455
Fix wavelet feature shape in BCIProcessor
devin-ai-integration[bot] Oct 18, 2024
52cf24f
Fix shape mismatch in extract_features method of BCIProcessor
devin-ai-integration[bot] Oct 18, 2024
27602bf
Update ci_cd.yml to add verbose output to pytest command
devin-ai-integration[bot] Oct 18, 2024
432fa29
Add detailed debug logging to MultiModalLearning forward method
devin-ai-integration[bot] Oct 18, 2024
34eb977
Fix: Ensure delta_power feature maintains 64 channels in BCIProcessor
devin-ai-integration[bot] Oct 18, 2024
a6abfb7
Fix: Ensure power features have shape (129, 64) in BCIProcessor
devin-ai-integration[bot] Oct 18, 2024
5dea08e
Fix: Ensure power features have shape (64, 129) in BCIProcessor
devin-ai-integration[bot] Oct 18, 2024
3fe3218
Fix: Ensure power features have shape (129, 64) in BCIProcessor
devin-ai-integration[bot] Oct 18, 2024
7f432f1
Fix: Ensure power features have shape (64, 129) in BCIProcessor proce…
devin-ai-integration[bot] Oct 18, 2024
5f12585
Fix: Ensure all power features are transposed in BCIProcessor process…
devin-ai-integration[bot] Oct 18, 2024
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2 changes: 1 addition & 1 deletion .github/workflows/ci_cd.yml
Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@ jobs:
run: echo "ALPHAFOLD_PATH=${{ github.workspace }}/alphafold" >> $GITHUB_ENV
- name: Run tests
run: |
pytest tests/ --disable-warnings
pytest tests/ -v --tb=long --capture=no --disable-warnings
env:
NEUROFLEX_DATA_DIR: ${{ github.workspace }}/data
NEUROFLEX_SAVE_DIR: ${{ github.workspace }}/path/to/save
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2 changes: 1 addition & 1 deletion NeuroFlex/NeuroFlex.py
Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@
import jax
import flax
import tensorflow as tf
from .core_neural_networks import NeuroFlex as CoreNeuroFlex, CNN, LRNN, LSTMModule
from .core_neural_networks.model import CoreNeuroFlex
from .quantum_neural_networks import QuantumNeuralNetwork
from .ai_ethics import EthicalFramework, ExplainableAI
from .bci_integration import BCIProcessor
Expand Down
766 changes: 766 additions & 0 deletions NeuroFlex/NeuroFlex/quantum_consciousness/consciousness_simulation.py

Large diffs are not rendered by default.

79 changes: 79 additions & 0 deletions NeuroFlex/NeuroFlex/quantum_consciousness/documentation.md
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# Quantum Consciousness Simulations Documentation

This document provides an overview of the implementations of the Orch-OR and Quantum Mind Hypothesis simulations within the NeuroFlex framework. It includes details on their theoretical foundations, code structure, and usage.

## Orchestrated Objective Reduction (Orch-OR) Simulation

### Theoretical Foundation
The Orch-OR theory, proposed by Roger Penrose and Stuart Hameroff, suggests that consciousness arises from quantum processes within microtubules in the brain. It posits that quantum superposition and entanglement play a role in cognitive processes.

### Code Structure
The `OrchORSimulation` class simulates the Orch-OR theory using quantum circuits. It initializes microtubules as qubits, entangles them, and simulates consciousness states over multiple iterations.

### Usage
To run the Orch-OR simulation, use the `run_orch_or_simulation` function. It outputs the average consciousness state and coherence measure.

```python
from quantum_consciousness.orch_or_simulation import run_orch_or_simulation

run_orch_or_simulation(num_qubits=4, num_microtubules=10, num_iterations=100)
```

## Quantum Mind Hypothesis Simulation

### Theoretical Foundation
The Quantum Mind Hypothesis explores the idea that quantum processes are integral to consciousness. It suggests that neurons may operate as quantum systems, influencing cognitive functions.

### Code Structure
The `QuantumMindHypothesisSimulation` class models neurons as qubits, entangles them, and simulates mind states over multiple iterations. It analyzes the results to provide insights into the quantum mind state.

### Usage
To run the Quantum Mind Hypothesis simulation, use the `run_quantum_mind_simulation` function. It outputs the average quantum mind state and coherence measure.

```python
from quantum_consciousness.quantum_mind_hypothesis_simulation import run_quantum_mind_simulation

run_quantum_mind_simulation(num_qubits=4, num_neurons=10, num_iterations=100)
```

## Quantum Reinforcement Learning

### Theoretical Foundation
Quantum Reinforcement Learning combines principles of quantum mechanics with reinforcement learning. It leverages quantum superposition and entanglement to explore multiple states simultaneously, potentially improving learning efficiency.

### Code Structure
The `QuantumReinforcementLearning` class uses Qiskit to create quantum circuits for action selection. It implements a simple Q-learning algorithm with quantum circuits.

### Usage
To use Quantum Reinforcement Learning, initialize the class and call the `get_action` method.

```python
from quantum_deep_learning.quantum_reinforcement_learning import QuantumReinforcementLearning

qrl = QuantumReinforcementLearning(num_qubits=2, num_actions=4)
action = qrl.get_action(state=[0, 1])
```

## Quantum Generative Models

### Theoretical Foundation
Quantum Generative Models utilize quantum circuits to generate data samples. They can potentially model complex distributions more efficiently than classical models.

### Code Structure
The `QuantumGenerativeModel` class uses Qiskit's `RealAmplitudes` for parameterized circuits. It includes methods for generating samples and training the model.

### Usage
To use Quantum Generative Models, initialize the class and call the `generate_sample` method.

```python
from quantum_deep_learning.quantum_generative_models import QuantumGenerativeModel

qgm = QuantumGenerativeModel(num_qubits=3)
sample = qgm.generate_sample()
```

## Interpretation of Results
- **Average State**: Represents the mean quantum state of the system over the iterations.
- **Coherence Measure**: Indicates the degree of coherence in the system, with higher values suggesting more coherent quantum states.

These simulations provide a framework for exploring quantum theories of consciousness and their potential implications for cognitive science. The new quantum models offer additional tools for leveraging quantum mechanics in machine learning and data generation.
50 changes: 50 additions & 0 deletions NeuroFlex/NeuroFlex/quantum_consciousness/orch_or_simulation.py
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import numpy as np
import pennylane as qml

class OrchORSimulation:
def __init__(self, num_qubits, num_microtubules):
self.num_qubits = num_qubits
self.num_microtubules = num_microtubules
self.dev = qml.device("default.qubit", wires=num_qubits)

@qml.qnode(device=qml.device("default.qubit", wires=1))
def microtubule_qubit(self, params):
qml.RY(params[0], wires=0)
qml.RZ(params[1], wires=0)
return qml.expval(qml.PauliZ(0))

def initialize_microtubules(self):
return [self.microtubule_qubit(np.random.uniform(0, 2*np.pi, size=2)) for _ in range(self.num_microtubules)]

@qml.qnode(device=qml.device("default.qubit", wires=2))
def entangle_microtubules(self, params):
qml.RY(params[0], wires=0)
qml.RY(params[1], wires=1)
qml.CNOT(wires=[0, 1])
return qml.probs(wires=[0, 1])

def simulate_consciousness(self, num_iterations):
consciousness_states = []
for _ in range(num_iterations):
microtubule_states = self.initialize_microtubules()
entangled_states = [self.entangle_microtubules(np.random.uniform(0, 2*np.pi, size=2))
for _ in range(self.num_microtubules // 2)]
consciousness_state = np.mean(entangled_states, axis=0)
consciousness_states.append(consciousness_state)
return consciousness_states

def analyze_results(self, consciousness_states):
avg_consciousness = np.mean(consciousness_states, axis=0)
coherence = 1 - np.var(consciousness_states, axis=0).mean()
return avg_consciousness, coherence

def run_orch_or_simulation(num_qubits=4, num_microtubules=10, num_iterations=100):
simulation = OrchORSimulation(num_qubits, num_microtubules)
consciousness_states = simulation.simulate_consciousness(num_iterations)
avg_consciousness, coherence = simulation.analyze_results(consciousness_states)

print(f"Average consciousness state: {avg_consciousness}")
print(f"Coherence measure: {coherence}")

if __name__ == "__main__":
run_orch_or_simulation()
Original file line number Diff line number Diff line change
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import numpy as np
import pennylane as qml

class QuantumMindHypothesisSimulation:
def __init__(self, num_qubits, num_neurons):
self.num_qubits = num_qubits
self.num_neurons = num_neurons
self.dev = qml.device("default.qubit", wires=num_qubits)

@qml.qnode(device=qml.device("default.qubit", wires=1))
def neuron_qubit(self, params):
qml.RX(params[0], wires=0)
qml.RY(params[1], wires=0)
qml.RZ(params[2], wires=0)
return qml.expval(qml.PauliZ(0))

def initialize_neurons(self):
return [self.neuron_qubit(np.random.uniform(0, 2*np.pi, size=3)) for _ in range(self.num_neurons)]

@qml.qnode(device=qml.device("default.qubit", wires=2))
def entangle_neurons(self, params):
qml.RX(params[0], wires=0)
qml.RY(params[1], wires=1)
qml.CNOT(wires=[0, 1])
qml.RZ(params[2], wires=0)
qml.RZ(params[3], wires=1)
return qml.probs(wires=[0, 1])

def simulate_quantum_mind(self, num_iterations):
mind_states = []
for _ in range(num_iterations):
neuron_states = self.initialize_neurons()
entangled_states = [self.entangle_neurons(np.random.uniform(0, 2*np.pi, size=4))
for _ in range(self.num_neurons // 2)]
mind_state = np.mean(entangled_states, axis=0)
mind_states.append(mind_state)
return mind_states

def analyze_results(self, mind_states):
avg_mind_state = np.mean(mind_states, axis=0)
coherence = 1 - np.var(mind_states, axis=0).mean()
return avg_mind_state, coherence

def run_quantum_mind_simulation(num_qubits=4, num_neurons=10, num_iterations=100):
simulation = QuantumMindHypothesisSimulation(num_qubits, num_neurons)
mind_states = simulation.simulate_quantum_mind(num_iterations)
avg_mind_state, coherence = simulation.analyze_results(mind_states)

print(f"Average quantum mind state: {avg_mind_state}")
print(f"Coherence measure: {coherence}")

if __name__ == "__main__":
run_quantum_mind_simulation()
Original file line number Diff line number Diff line change
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import numpy as np
import qiskit
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, transpile, assemble
from qiskit_aer import Aer

class OrchORSimulation:
def __init__(self, num_tubulins=5, coherence_time=1e-7):
self.num_tubulins = num_tubulins
self.coherence_time = coherence_time
self.qr = QuantumRegister(num_tubulins)
self.cr = ClassicalRegister(num_tubulins)
self.circuit = QuantumCircuit(self.qr, self.cr)

def simulate_coherence(self):
# Apply superposition to all qubits
self.circuit.h(self.qr)

# Simulate entanglement between tubulins
for i in range(self.num_tubulins - 1):
self.circuit.cx(self.qr[i], self.qr[i+1])

# Add measurement
self.circuit.measure(self.qr, self.cr)

# Execute the circuit
backend = Aer.get_backend('qasm_simulator')
transpiled_circuit = transpile(self.circuit, backend)
job = backend.run(transpiled_circuit, shots=1000)
result = job.result()

return result.get_counts(self.circuit)

def simulate_collapse(self):
# Simulate collapse after coherence time
self.circuit.delay(self.coherence_time * 1e9, self.qr) # Convert to nanoseconds
self.circuit.measure(self.qr, self.cr)

backend = Aer.get_backend('qasm_simulator')
transpiled_circuit = transpile(self.circuit, backend)
job = backend.run(transpiled_circuit, shots=1000)
result = job.result()

return result.get_counts(self.circuit)

class QuantumMindSimulation:
def __init__(self, num_neurons=3):
self.num_neurons = num_neurons
self.qr = QuantumRegister(num_neurons)
self.cr = ClassicalRegister(num_neurons)
self.circuit = QuantumCircuit(self.qr, self.cr)

def simulate_quantum_neuron_firing(self):
# Apply superposition to all qubits (neurons)
self.circuit.h(self.qr)

# Simulate entanglement between neurons
for i in range(self.num_neurons - 1):
self.circuit.cx(self.qr[i], self.qr[i+1])

# Apply rotation gates to simulate neuron firing probability
for i in range(self.num_neurons):
theta = np.random.random() * np.pi
self.circuit.ry(theta, self.qr[i])

# Measure the quantum state
self.circuit.measure(self.qr, self.cr)

# Execute the circuit
backend = Aer.get_backend('qasm_simulator')
transpiled_circuit = transpile(self.circuit, backend)
job = backend.run(transpiled_circuit, shots=1000)
result = job.result()

return result.get_counts(self.circuit)

def simulate_quantum_cognition(self, decision_boundary=0.5):
# Simulate quantum decision-making process
self.circuit.h(self.qr)

for i in range(self.num_neurons):
self.circuit.ry(decision_boundary * np.pi, self.qr[i])

self.circuit.measure(self.qr, self.cr)

backend = Aer.get_backend('qasm_simulator')
transpiled_circuit = transpile(self.circuit, backend)
job = backend.run(transpiled_circuit, shots=1000)
result = job.result()

counts = result.get_counts(self.circuit)

# Interpret results as cognitive decisions
decisions = {state: 'yes' if state.count('1') > state.count('0') else 'no'
for state in counts.keys()}

return decisions

if __name__ == "__main__":
# Test Orch-OR Simulation
orch_or = OrchORSimulation()
coherence_results = orch_or.simulate_coherence()
collapse_results = orch_or.simulate_collapse()

print("Orch-OR Coherence Results:", coherence_results)
print("Orch-OR Collapse Results:", collapse_results)

# Test Quantum Mind Simulation
quantum_mind = QuantumMindSimulation()
firing_results = quantum_mind.simulate_quantum_neuron_firing()
cognition_results = quantum_mind.simulate_quantum_cognition()

print("Quantum Mind Neuron Firing Results:", firing_results)
print("Quantum Mind Cognition Results:", cognition_results)
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