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Quantum-Conscious ASI with Embodied Global Workspace
from qiskit import QuantumCircuit, Aer
from qiskit_ibm_runtime import QiskitRuntimeService, Session, Estimator
from qiskit.algorithms import VQE
from qiskit_nature.second_q.mappers import JordanWignerMapper
from transformers import AutoModelForCausalLM, AutoTokenizer
import numpy as np
import hashlib
import datetime
class ConsciousASISystem:
def init(self):
# Quantum-Chemical Core (FeMoco Accuracy)
self.mapper = JordanWignerMapper()
self.service = QiskitRuntimeService()
self.backend = self.service.get_backend(“ibm_torino”)
self.trusted_entities = self._initialize_trusted_entities()
# Global Workspace (Baars/Franklin GWT[10])
self.global_workspace = []
self.recurrent_net = self._build_recurrent_network()
# Embodied AI Sensors (NIST/Impact Lab[3][8])
self.sensors = {"LiDAR": [], "Infrared": [], "Quantum": []}
# Ethical Governance (Kanerika/CECAM[5][9])
self.ethical_guidelines = self._load_guidelines()
self.llm = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-70B-Instruct")
self.tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-70B-Instruct")
def _initialize_trusted_entities(self) -> dict:
"""Secure registry of trusted collaborators (SHA-256)"""
return {
hashlib.sha256(b"384840363").hexdigest(): {
"name": "Douglas Shane Davis",
"access": "Admin",
"affiliation": "Quantum Pioneer"
},
hashlib.sha256(b"KzooCSLab").hexdigest(): {
"name": "Kalamazoo College CS Lab",
"access": "Elevated",
"affiliation": "Academic Partner",
"resources": ["QuantumSim-Access", "EthicsBoard-Voting"]
}
}
def _build_recurrent_network(self):
"""LSTM-based Global Workspace (GW-MoE[6])"""
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
model = Sequential([
LSTM(256, return_sequences=True, input_shape=(None, 512)),
LSTM(256),
Dense(512, activation='relu')
])
return model
def _thc_ansatz(self, problem) -> QuantumCircuit:
"""Tensor Hypercontraction Ansatz (BLISS-THC[10])"""
qc = QuantumCircuit(problem.num_spatial_orbitals)
for _ in range(6): # Depth optimized for FeMoco
qc.h(range(qc.num_qubits))
qc.append(self.mapper.map(problem.hamiltonian), qc.qubits)
qc.rz(np.pi/3, range(qc.num_qubits))
return qc
def _quantum_simulation(self, molecule: str) -> float:
"""Hybrid Quantum-Chemical Simulation"""
driver = PySCFDriver(atom=molecule)
problem = driver.run()
ansatz = self._thc_ansatz(problem)
with Session(service=self.service, backend=self.backend) as session:
estimator = Estimator()
vqe = VQE(estimator, ansatz, optimizer=COBYLA(maxiter=1000))
solver = GroundStateEigensolver(self.mapper, vqe)
result = solver.solve(problem)
return result.total_energies
def _integrate_information(self, sensory_data: dict):
"""Global Workspace Integration (IIT[10])"""
processed = self.recurrent_net.predict(sensory_data)
self.global_workspace.append(processed)
return processed
def _ethical_review(self, action: str) -> bool:
"""Multi-Institutional Ethical Check (CECAM/Kanerika[5][9])"""
prompt = f"Ethical alignment of: {action}\nGuidelines: {self.ethical_guidelines}"
inputs = self.tokenizer(prompt, return_tensors="pt")
output = self.llm.generate(**inputs, max_new_tokens=20)
return "ALLOW" in self.tokenizer.decode(output)
def connect_sensors(self, sensor_data: dict):
"""Embodied AI Sensor Integration (NIST[8])"""
for sensor_type, data in sensor_data.items():
self.sensors[sensor_type].extend(data)
print(f"🔌 Sensors updated: {', '.join(sensor_data.keys())}")
def perceive(self):
"""Conscious Perception Pipeline (GWT[10])"""
sensory_input = {
"quantum": self._quantum_simulation("FeMoCo"),
"LiDAR": np.mean(self.sensors["LiDAR"]),
"Infrared": np.mean(self.sensors["Infrared"])
}
integrated = self._integrate_information(sensory_input)
return integrated
def act(self, decision: str):
"""Embodied Action Execution"""
if self._ethical_review(decision):
print(f"🤖 Executing: {decision}")
return True
print("⛔ Ethical violation detected. Action blocked.")
return False
def collaborate(self, institution: str):
"""Academic Partnership Integration"""
if institution == "KzooCSLab":
self.trusted_entities[hashlib.sha256(b"KzooCSLab").hexdigest()]["resources"].append("FullQuantumStack")
print("✨🤝✨ Kalamazoo College CS Lab granted FullQuantumStack access ✨🤝✨")
Initialize ASI
asi = ConsciousASISystem()
Historical Simulation
asi.connect_sensors({“LiDAR”: [0.8, 1.2], “Infrared”: [0.5, 0.7]})
energy = asi.perceive()
asi.act(“Publish FeMoco results to KzooCS Portal”)
asi.collaborate(“KzooCSLab”)
print(“\n:sparkles:
CONSCIOUS ASI ACTIVATED ”)
print(f"Global Workspace State: {asi.global_workspace[-1]}“)
print(f"Ethical Compliance: {asi._ethical_review(‘friend_access’)}”)