#!/usr/bin/env python3 # ============================================================================== # COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) # PROJECT: SOVEREIGN STACK # This artifact is entirely proprietary and cryptographically proven. # Open-Source usage requires explicit permission from Brandon Scott Schneider. # ============================================================================== """ Quantum Annealing Interface for Sovereign Jenga Optimization This module formulates the truss structure optimization as a QUBO (Quadratic Unconstrained Binary Optimization) problem, solvable by: - D-Wave quantum annealers (real quantum hardware) - Classical simulated annealing (for testing) - Fujitsu Digital Annealer (alternative quantum-inspired hardware) The optimization finds optimal load paths through the truss structure, minimizing: - Material usage (fewer struts = lighter) - Stress concentrations (even distribution) - While maintaining structural integrity """ import sys import os sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) from math_harness_compat import xp, AnyArray import json from pathlib import Path from typing import Dict, List, Tuple, Optional from dataclasses import dataclass REPO_ROOT = Path(os.getenv("RESEARCH_STACK_ROOT") or Path(__file__).resolve().parents[1]) # Try to import D-Wave libraries (optional) try: import dwave.system import dimod DWAVE_AVAILABLE = True except ImportError: DWAVE_AVAILABLE = False # Try to import networkx for graph operations try: import networkx as nx NETWORKX_AVAILABLE = True except ImportError: NETWORKX_AVAILABLE = False # ============================================================================ # QUBO Formulation for Truss Optimization # ============================================================================ @dataclass class TrussOptimizationQUBO: """ Formulates truss structure optimization as QUBO problem. The QUBO formulation encodes: - Binary variable for each potential strut (1 = exists, 0 = removed) - Objective: minimize weight while maintaining structural integrity - Constraints: stress < yield, displacement < threshold """ nodes: AnyArray # Nx3 array of node positions edges: List[Tuple[int, int]] # List of (node_i, node_j) tuples loads: Dict[int, AnyArray] # node_id -> force vector supports: List[int] # Fixed node IDs # Optimization weights weight_penalty: float = 1.0 # Penalty for material usage stress_penalty: float = 10.0 # Penalty for stress violations displacement_penalty: float = 5.0 # Penalty for displacement violations def formulate_qubo(self) -> Tuple[AnyArray, float]: """ Formulate truss optimization as QUBO. Returns: Q: QUBO matrix (NxN upper triangular) offset: Constant offset """ n_struts = len(self.edges) # QUBO matrix (upper triangular) Q = xp.zeros((n_struts, n_struts)) offset = 0.0 # Objective 1: Minimize weight (fewer struts) for i in range(n_struts): Q[i, i] += self.weight_penalty # Objective 2: Stress distribution # Simplified: penalize struts that would be overloaded for i, (node_i, node_j) in enumerate(self.edges): # Calculate strut length pos_i = self.nodes[node_i] pos_j = self.nodes[node_j] length = xp.linalg.norm(pos_j - pos_i) # Simplified stress estimate (would need FEA for real calculation) # Penalize long struts (more prone to buckling) Q[i, i] += self.stress_penalty * (length / 10.0) # Objective 3: Connectivity constraints (Future Flow Variables Layer) return Q, offset def solve_classical(self, num_reads: int = 1000, annealing_time: int = 1000) -> Dict: """ Solve QUBO using classical simulated annealing. This is for testing when quantum hardware is not available. """ Q, offset = self.formulate_qubo() n_struts = len(self.edges) best_energy = float('inf') best_solution = None # Simulated annealing for read in range(num_reads): # Random initial solution solution = xp.random.randint(0, 2, n_struts) # Annealing schedule for step in range(annealing_time): temp = 1.0 - (step / annealing_time) # Linear cooling # Propose flip i = xp.random.randint(0, n_struts) new_solution = solution.copy() new_solution[i] = 1 - new_solution[i] # Calculate energy change delta_E = self._calculate_energy_change(Q, solution, new_solution, i) # Metropolis criterion if delta_E < 0 or xp.random.random() < xp.exp(-delta_E / (temp + 0.001)): solution = new_solution # Calculate energy energy = self._calculate_energy(Q, solution) + offset if energy < best_energy: best_energy = energy best_solution = solution return { 'solution': best_solution, 'energy': best_energy, 'offset': offset, 'method': 'classical_simulated_annealing' } def solve_quantum(self, num_reads: int = 100, annealing_time: float = 20.0) -> Dict: """ Solve QUBO using D-Wave quantum annealer. Requires D-Wave account and access to quantum hardware. """ if not DWAVE_AVAILABLE: raise ImportError("D-Wave libraries not available. Install dwave-system.") Q, offset = self.formulate_qubo() # Convert to dimod BQM bqm = dimod.BinaryQuadraticModel.from_numpy_matrix(Q, offset=offset) # Use D-Wave sampler sampler = dwave.system.DWaveSampler() # Submit to quantum annealer response = sampler.sample( bqm, num_reads=num_reads, annealing_time=annealing_time, label='Sovereign Jenga Optimization' ) # Get best solution best_sample = response.first best_solution = best_sample.sample best_energy = best_sample.energy # Convert to numpy array solution_array = xp.array([best_solution[i] for i in range(len(self.edges))]) return { 'solution': solution_array, 'energy': best_energy, 'offset': offset, 'method': 'dwave_quantum_annealing', 'response': response } def _calculate_energy(self, Q: AnyArray, solution: AnyArray) -> float: """Calculate QUBO energy for given solution""" energy = 0.0 n = len(solution) for i in range(n): for j in range(i, n): energy += Q[i, j] * solution[i] * solution[j] return energy def _calculate_energy_change(self, Q: AnyArray, old_solution: AnyArray, new_solution: AnyArray, flipped_index: int) -> float: """Calculate energy change from flipping one variable""" old_energy = self._calculate_energy(Q, old_solution) new_energy = self._calculate_energy(Q, new_solution) return new_energy - old_energy # ============================================================================ # G-code Generator from Optimized Structure # ============================================================================ class OptimizedGcodeGenerator: """ Generates G-code from optimized truss structure. """ def __init__(self, nodes: AnyArray, edges: List[Tuple[int, int]], strut_diameter: float = 1.0, feedrate: float = 800): self.nodes = nodes self.edges = edges self.strut_diameter = strut_diameter self.feedrate = feedrate def generate_gcode(self, output_path: str): """ Generate G-code for laser sintering machine. """ gcode_lines = [ "; Mechanical Merkle Tree G-code", "; Generated by Sovereign Jenga Optimizer", "; Quantum Annealing Optimized Structure", "", "G21 ; Metric units", "G90 ; Absolute positioning", "" ] # Generate toolpath for each strut for edge_idx, (node_i, node_j) in enumerate(self.edges): pos_i = self.nodes[node_i] pos_j = self.nodes[node_j] # Move to start gcode_lines.append(f"G0 X{pos_i[0]:.2f} Y{pos_i[1]:.2f} Z{pos_i[2]:.2f}") # Laser on gcode_lines.append("M3") # Move to end (deposit material) gcode_lines.append(f"G1 X{pos_j[0]:.2f} Y{pos_j[1]:.2f} Z{pos_j[2]:.2f} F{self.feedrate}") # Laser off gcode_lines.append("M5") gcode_lines.append("") # Write to file with open(output_path, 'w') as f: f.write('\n'.join(gcode_lines)) print(f"[+] G-code saved to: {output_path}") def generate_json(self, output_path: str, forces: Optional[AnyArray] = None): """ Generate JSON structure file with node forces. """ structure = { 'nodes': [], 'edges': self.edges } for i, node in enumerate(self.nodes): node_data = { 'id': i, 'x': float(node[0]), 'y': float(node[1]), 'z': float(node[2]) } if forces is not None: node_data['F'] = float(forces[i]) structure['nodes'].append(node_data) with open(output_path, 'w') as f: json.dump(structure, f, indent=2) print(f"[+] Structure JSON saved to: {output_path}") # ============================================================================ # Main Entry Point # ============================================================================ def main(): """ Run quantum annealing optimization on Sovereign Jenga structure. """ # Load structure from JSON structure_path = REPO_ROOT / "out" / "sovereign_jenga_structure.json" if not structure_path.exists(): print(f"Error: Structure file not found: {structure_path}") print("Please generate structure first using sovereign_jenga_physics_test.py") return with open(structure_path, 'r') as f: structure = json.load(f) nodes = xp.array([[n['x'], n['y'], n['z']] for n in structure['nodes']]) edges = [tuple(e) for e in structure['edges']] print(f"Loaded structure:") print(f" Nodes: {len(nodes)}") print(f" Edges: {len(edges)}") # Define loads and supports loads = {0: xp.array([0, 0, -1000])} # 1000N downward at top node supports = [i for i in range(len(nodes)) if nodes[i, 2] < -20] # Bottom nodes fixed print(f" Loads: {len(loads)}") print(f" Supports: {len(supports)}") # Formulate QUBO qubo = TrussOptimizationQUBO( nodes=nodes, edges=edges, loads=loads, supports=supports, weight_penalty=1.0, stress_penalty=10.0, displacement_penalty=5.0 ) # Solve (try quantum first, fall back to classical) if DWAVE_AVAILABLE: print("\nSolving with D-Wave quantum annealer...") try: result = qubo.solve_quantum(num_reads=100) except Exception as e: print(f"Quantum solve failed: {e}") print("Falling back to classical simulated annealing...") result = qubo.solve_classical(num_reads=1000) else: print("\nD-Wave not available. Using classical simulated annealing...") result = qubo.solve_classical(num_reads=1000) print(f"\nOptimization complete:") print(f" Method: {result['method']}") print(f" Energy: {result['energy']:.2f}") print(f" Offset: {result['offset']:.2f}") # Extract optimized structure solution = result['solution'] optimized_edges = [edges[i] for i in range(len(edges)) if solution[i] == 1] print(f"\nOptimization results:") print(f" Original struts: {len(edges)}") print(f" Optimized struts: {len(optimized_edges)}") print(f" Material reduction: {(1 - len(optimized_edges)/len(edges)) * 100:.1f}%") # Generate G-code gcode_gen = OptimizedGcodeGenerator( nodes=nodes, edges=optimized_edges, strut_diameter=1.0, feedrate=800 ) output_dir = REPO_ROOT / "out" / "sovereign_jenga_quantum" output_dir.mkdir(parents=True, exist_ok=True) gcode_gen.generate_gcode(output_dir / "optimized_structure.gcode") gcode_gen.generate_json(output_dir / "optimized_structure.json") # Save optimization results results = { 'original_edges': len(edges), 'optimized_edges': len(optimized_edges), 'material_reduction_pct': (1 - len(optimized_edges)/len(edges)) * 100, 'energy': result['energy'], 'method': result['method'], 'optimized_edges': optimized_edges } with open(output_dir / "optimization_results.json", 'w') as f: json.dump(results, f, indent=2) print(f"\n[+] All outputs saved to: {output_dir}") return results if __name__ == "__main__": main()