This guide covers advanced features and configurations of the Quantum Agent Manager (QAM) system, intended for users who need to customize and extend the framework's capabilities.
Hierarchical QUBOs enable solving large-scale optimization problems by breaking them into interconnected levels. This approach is particularly effective for complex agent systems with multiple organizational layers.
from qam.hierarchical_qubo import HierarchicalQUBO, QUBOLevel
import numpy as np
# Create multi-level QUBO system
hqubo = HierarchicalQUBO()
# Define first level for department-level scheduling
level1_matrix = np.array([
[2, -1, 0],
[-1, 3, -1],
[0, -1, 2]
])
level1 = hqubo.add_level(
qubo_matrix=level1_matrix,
variables=["dept1", "dept2", "dept3"],
constraints={"dept1": 0.5} # Ensure dept1 is partially utilized
)
# Define second level for team-level scheduling
level2_matrix = np.array([
[1, 0.5],
[0.5, 2]
])
level2 = hqubo.add_level(
qubo_matrix=level2_matrix,
variables=["team1", "team2"]
)
# Connect levels with specified interaction strength
hqubo.add_connection(level1, level2, weight=0.7)
# Customize optimization parameters
hqubo.optimization_parameters.update({
'inter_level_weight': 0.5,
'constraint_weight': 10.0,
'convergence_threshold': 1e-6
})
# Solve hierarchical system
results = hqubo.optimize()# Custom convergence criteria
class CustomConvergence:
def check(self, current_state, previous_state):
return np.abs(current_state - previous_state).max() < 1e-5
hqubo.optimization_parameters['convergence_checker'] = CustomConvergence()
# Custom constraint handling
class CustomConstraintHandler:
def apply(self, matrix, constraints):
# Implement custom constraint logic
return modified_matrix
hqubo.optimization_parameters['constraint_handler'] = CustomConstraintHandler()Create specialized solvers for specific optimization requirements or hardware platforms.
class CustomQuantumSolver:
def __init__(self, backend_type="simulator"):
self.backend = backend_type
self.optimization_history = []
def optimize(self, qubo_matrix):
"""Custom quantum-inspired optimization implementation"""
n = qubo_matrix.shape[0]
# Initialize with quantum-inspired state
state = self._prepare_quantum_state(n)
# Implement optimization logic
for step in range(self.max_steps):
# Apply quantum operations
new_state = self._apply_quantum_operations(state, qubo_matrix)
# Measure and update
energy = self._calculate_energy(new_state, qubo_matrix)
self.optimization_history.append(energy)
if self._check_convergence(energy):
break
state = new_state
return self._get_final_solution(state)
def _prepare_quantum_state(self, n):
"""Initialize quantum-inspired state"""
return np.random.randn(2**n) + 1j * np.random.randn(2**n)
def _apply_quantum_operations(self, state, qubo):
"""Apply quantum-inspired operations"""
# Implement quantum-inspired transformations
return evolved_state
def _calculate_energy(self, state, qubo):
"""Calculate energy expectation value"""
return float(np.real(state.conj() @ qubo @ state))# Create and configure custom solver
custom_solver = CustomQuantumSolver(backend_type="quantum_inspired")
custom_solver.max_steps = 1000
# Integrate with hierarchical QUBO
hqubo.optimization_parameters['custom_solver'] = custom_solver
# Run optimization with custom solver
results = hqubo.optimize()
# Analyze optimization performance
optimization_history = custom_solver.optimization_history
print(f"Convergence achieved in {len(optimization_history)} steps")Implement sophisticated quantum state evolution for complex decision-making scenarios.
from qam.quantum_reasoning import QuantumReasoningState, DecisionPath
import numpy as np
class EnhancedQuantumState(QuantumReasoningState):
def __init__(self):
super().__init__()
self.decoherence_rate = 0.01
def evolve_with_decoherence(self, hamiltonian, time_steps):
"""Evolution with environmental effects"""
for t in range(time_steps):
# Coherent evolution
self.evolve(hamiltonian)
# Apply decoherence
self._apply_decoherence()
def _apply_decoherence(self):
"""Simulate environmental decoherence"""
for path in self.amplitudes:
self.amplitudes[path] *= (1 - self.decoherence_rate)
def add_superposition_path(self, paths, weights):
"""Create superposition of decision paths"""
total_weight = sum(weights)
normalized_weights = [w/total_weight for w in weights]
for path, weight in zip(paths, normalized_weights):
self.add_decision_path(path, np.sqrt(weight))class CustomMeasurement:
def __init__(self, basis_states):
self.basis_states = basis_states
def measure(self, quantum_state):
"""Implement custom measurement protocol"""
probabilities = {}
for basis_state in self.basis_states:
prob = 0
for path, amplitude in quantum_state.amplitudes.items():
if self._matches_basis(path, basis_state):
prob += abs(amplitude)**2
probabilities[basis_state] = prob
return self._select_outcome(probabilities)
def _matches_basis(self, path, basis_state):
"""Check if path matches basis state"""
# Implement basis matching logic
return True
def _select_outcome(self, probabilities):
"""Select measurement outcome"""
# Implement selection logic
return max(probabilities.items(), key=lambda x: x[1])[0]