import numpy as np
import hashlib
from qiskit import QuantumCircuit, Aer, execute, transpile
from qiskit.providers.aer import AerSimulator
from qiskit.quantum_info import Statevector, random_statevector
from qiskit.circuit.library import QFT, IQFT
from collections import defaultdict
import time
import json
class QuantumLedger:
def __init__(self, num_qubits=9, nodes=5): # 9 qubits for Shor error correction
self.num_qubits = num_qubits
self.nodes = nodes # Simulate planetary nodes (e.g., continents)
self.backend = AerSimulator() # Noisy simulator for realism
self.ledger = defaultdict(dict) # Distributed ledger: node -> {resource: amount}
self.merkle_tree = {} # Classical Merkle tree for hashing
self.consensus_log = [] # Log of consensus rounds
def quantum_hash(self, data):
"""Quantum hashing using QFT for secure, entanglement-based hash."""
qc = QuantumCircuit(self.num_qubits // 3, self.num_qubits // 3) # Simplified
qc.h(0)
for i in range(1, qc.num_qubits):
qc.cx(0, i)
qc.append(QFT(qc.num_qubits), range(qc.num_qubits))
# Encode data into amplitudes
state = Statevector.from_label('0' * qc.num_qubits)
for i, bit in enumerate(data[:qc.num_qubits]):
if bit == '1':
state = state.evolve(qc)
qc.measure_all()
job = execute(qc, self.backend, shots=1)
result = job.result().get_counts()
return list(result.keys())[0] # Quantum hash as string
def build_merkle_tree(self, data_list):
"""Classical Merkle tree for data integrity."""
if not data_list:
return None
if len(data_list) == 1:
return hashlib.sha256(data_list[0].encode()).hexdigest()
mid = len(data_list) // 2
left = self.build_merkle_tree(data_list[:mid])
right = self.build_merkle_tree(data_list[mid:])
return hashlib.sha256((left + right).encode()).hexdigest()
def apply_error_correction(self, qc):
"""Apply Shor's 9-qubit error correction code."""
# Simplified Shor code: Encode logical qubit into 9 physical qubits
qc.cx(0, 3)
qc.cx(0, 6)
qc.h([0, 3, 6])
qc.cx(0, 1)
qc.cx(3, 4)
qc.cx(6, 7)
qc.cx(0, 2)
qc.cx(3, 5)
qc.cx(6, 8)
return qc
def quantum_teleportation(self, data_state):
"""Simulate quantum teleportation for FTL data transfer."""
qc = QuantumCircuit(3, 3)
# Entangle qubits 1 and 2
qc.h(1)
qc.cx(1, 2)
# Prepare data on qubit 0 (simulate data encoding)
if data_state == '1':
qc.x(0)
# Teleport: Measure entangled pair
qc.cx(0, 1)
qc.h(0)
qc.measure([0, 1], [0, 1])
# Apply corrections based on measurement (simplified)
qc.x(2).c_if(0, 1)
qc.z(2).c_if(1, 1)
return qc
def sync_inventory(self, global_data, node_id):
"""Sync inventory for a single node using quantum teleportation."""
qc = self.quantum_teleportation(str(global_data)) # Teleport data state
qc = self.apply_error_correction(qc) # Add error correction
job = execute(qc, self.backend, shots=1024, noise_model=None) # Add noise for realism
result = job.result()
counts = result.get_counts()
# Decode synced data (approximate FTL sync)
synced_amount = sum(int(k, 2) for k in counts.keys()) / 1024 * max(global_data.values())
self.ledger[node_id] = {k: v * np.random.uniform(0.95, 1.05) for k, v in global_data.items()} # Self-correct oscillation
return self.ledger[node_id]
def multi_node_sync(self, nodes_data):
"""Distributed sync across planetary nodes with consensus."""
synced_ledgers = {}
for node, data in nodes_data.items():
synced_ledgers[node] = self.sync_inventory(data, node)
# Quantum consensus: Simulate Byzantine agreement with majority vote
consensus_votes = {}
for resource in set(k for d in nodes_data.values() for k in d.keys()):
votes = [synced_ledgers[node].get(resource, 0) for node in synced_ledgers]
consensus_votes[resource] = np.median(votes) # Median for fault tolerance
self.consensus_log.append({"round": len(self.consensus_log) + 1, "votes": consensus_votes, "timestamp": time.time()})
# Update Merkle tree
data_hashes = [self.quantum_hash(json.dumps(d)) for d in synced_ledgers.values()]
self.merkle_tree = self.build_merkle_tree(data_hashes)
return synced_ledgers, consensus_votes
def validate_ledger(self, node_id):
"""Validate a node's ledger against Merkle root."""
node_hash = self.quantum_hash(json.dumps(self.ledger[node_id]))
return node_hash in self.merkle_tree # Simplified check
def integrate_with_ai_iot(self, ai_optimizer, iot_simulator):
"""Real-time integration: Pull from AI and IoT for dynamic sync."""
iot_data = iot_simulator.simulate_tracking()
regions = [[d['water_level'], d['energy_usage'], random.uniform(0,1)] for d in iot_data.values()]
allocations = ai_optimizer.optimize_allocation(regions)
# Simulate planetary data from IoT
planetary_data = {f"region_{i}": {"water": allocations[i], "energy": iot_data[f"sensor_{i}"]['energy_usage']} for i in range(len(allocations))}
synced, consensus = self.multi_node_sync(planetary_data)
return synced, consensus
# Example Usage (Runnable Standalone)
if __name__ == "__main__":
from ai_optimizer import ResourceOptimizer # Import from same folder
from iot_simulator import IoTSimulator
ledger = QuantumLedger()
optimizer = ResourceOptimizer()
simulator = IoTSimulator()
# Train AI briefly
optimizer.train_on_real_data(simulator.simulate_tracking())
# Run full integration
synced_ledgers, consensus = ledger.integrate_with_ai_iot(optimizer, simulator)
print("Synced Planetary Ledgers:", json.dumps(synced_ledgers, indent=2))
print("Consensus Allocations:", consensus)
print("Merkle Root:", ledger.merkle_tree)
print("Validation for node 'region_0':", ledger.validate_ledger("region_0"))
