#!/usr/bin/env python3 """ Swarm Query: Qutrit vs Classical Efficiency for Gossip_DAG_QR_Go_Tile_Flipping Query the swarm system to determine whether qutrits (quantum 3-level systems) or classical encoding would be more efficient for the Gossip_DAG_QR_Go_Tile_Flipping protocol (MATH_MODEL_MAP 0.4.10). """ import json import uuid from pathlib import Path from datetime import datetime def generate_qutrit_vs_classical_request(): """Generate swarm request for qutrit vs classical efficiency comparison.""" request = { "request_id": f"swarm_qutrit_vs_classical_{uuid.uuid4().hex[:12]}", "timestamp": datetime.now().isoformat(), "query_type": "efficiency_comparison", "scope": "gossip_dag_qr_go_tile_flipping", "priority": "P0_CRITICAL", "description": "Ask the swarm to compare qutrit vs classical encoding efficiency for Gossip_DAG_QR_Go_Tile_Flipping protocol", "context": { "insight": "QR code modules act as Go tiles that flip based on gossip messages", "formalism_id": "0.4.10 Gossip_DAG_QR_Go_Tile_Flipping", "current_implementation": "Classical encoding with 4 tile states", "qutrit_option": "Quantum 3-level systems (|0⟩, |1⟩, |W⟩)", "qutrit_infrastructure": "Sovereign Signal Tier with 20 THz Rabi frequency" }, "implementation_comparison": { "classical_implementation": { "tile_states": 4, "states": ["empty", "black", "captured", "ko"], "encoding": "Classical enumeration", "superposition": "Not available", "hardware": "Standard CPU/GPU", "files": [ "0-Core-Formalism/lean/Semantics/Semantics/GossipFlipMessage.lean", "0-Core-Formalism/lean/Semantics/Semantics/TileStateMachine.lean", "0-Core-Formalism/lean/Semantics/Semantics/QRGridState.lean" ] }, "qutrit_options": { "option_1_single_qutrit": { "tile_states": 3, "states": ["|0⟩ (Ground)", "|1⟩ (Excited)", "|W⟩ (Tunnel)"], "mapping": "Reduce from 4 to 3 states (remove one or combine)", "superposition": "Available (3-level superposition)", "hardware": "Sovereign Signal Tier (20 THz Rabi frequency)", "limitation": "Only 3 states, need to reduce from 4" }, "option_2_two_qutrits": { "tile_states": 9, "states": "3² = 9 combinations", "mapping": "Map 4 tile states to 2 qutrits (6 unused states)", "superposition": "Available (9-level superposition)", "hardware": "Sovereign Signal Tier (20 THz Rabi frequency)", "limitation": "Over-provisioned (9 states for 4 needed)" } } }, "efficiency_criteria": { "computational_efficiency": { "description": "Compare computational efficiency of tile state transitions", "questions": [ "How many operations per tile flip for classical vs qutrit?", "What is the latency of state transitions?", "How does superposition affect computation?", "What is the throughput for parallel tile flips?" ] }, "state_space_capacity": { "description": "Compare state space capacity and scalability", "questions": [ "How many tile states can be encoded per resource unit?", "How does scaling affect performance?", "What is the memory footprint per tile?", "How does grid size affect performance?" ] }, "superposition_benefits": { "description": "Evaluate benefits of quantum superposition", "questions": [ "Can superposition enable simultaneous tile flips?", "Does superposition accelerate Go rule evaluation?", "Can superposition enable parallel gossip message processing?", "What is the quantum advantage for this use case?" ] }, "hardware_requirements": { "description": "Compare hardware requirements and feasibility", "questions": [ "What hardware is required for classical implementation?", "What hardware is required for qutrit implementation?", "Is Sovereign Signal Tier infrastructure available?", "What are the power consumption differences?" ] }, "integration_complexity": { "description": "Evaluate integration with existing Research Stack", "questions": [ "How complex is integrating classical implementation?", "How complex is integrating qutrit implementation?", "Does qutrit integration require new hardware?", "What are the maintenance implications?" ] }, "error_correction": { "description": "Compare error correction and fault tolerance", "questions": [ "How does classical error correction work?", "How does qutrit error correction work?", "What is the error rate difference?", "How does fault tolerance compare?" ] } }, "existing_infrastructure": { "qutrit_spec": "shared-data/data/germane/research/qutrit_state_spec.md", "qutrit_states": ["|0⟩ (Ground)", "|1⟩ (Excited)", "|W⟩ (Tunnel)"], "rabi_frequency": "20 THz", "synchronization": "Atmospheric fracking array", "mechanical_anchor": "Mechanical Merkle Tree", "topological_invariant": "DAG lattice" }, "expected_deliverables": { "efficiency_comparison": "Detailed efficiency comparison table", "recommendation": "Clear recommendation (classical vs qutrit)", "state_mapping": "Proposed state mapping for recommended approach", "implementation_plan": "Step-by-step implementation plan for recommendation", "performance_metrics": "Expected performance metrics", "hardware_requirements": "Hardware requirements for recommendation", "integration_roadmap": "Integration roadmap with existing infrastructure" }, "decision_factors": { "computational_speed": "Weight: 0.3", "state_capacity": "Weight: 0.2", "superposition_advantage": "Weight: 0.2", "hardware_availability": "Weight: 0.15", "integration_complexity": "Weight: 0.1", "error_correction": "Weight: 0.05" }, "swarm_response_format": { "efficiency_summary": "High-level efficiency comparison", "detailed_analysis": "Detailed analysis per criterion", "quantum_advantage_assessment": "Assessment of quantum advantage", "recommendation": "Clear recommendation with justification", "state_mapping_proposal": "Proposed state mapping for recommendation", "implementation_roadmap": "Step-by-step implementation plan", "performance_estimates": "Estimated performance metrics", "concerns_or_caveats": "Any concerns or limitations identified" } } return request def save_request(request, output_path): """Save swarm request to file.""" Path(output_path).parent.mkdir(parents=True, exist_ok=True) with open(output_path, 'w') as f: json.dump(request, f, indent=2) return output_path def main(): """Generate and save qutrit vs classical efficiency comparison request.""" print("=" * 70) print("Swarm Query: Qutrit vs Classical Efficiency Comparison") print("=" * 70) # Generate request request = generate_qutrit_vs_classical_request() # Save request output_path = "shared-data/data/swarm_requests/swarm_qutrit_vs_classical.json" saved_path = save_request(request, output_path) print(f"\nRequest generated and saved to: {saved_path}") print(f"Request ID: {request['request_id']}") print(f"Priority: {request['priority']}") print(f"Formalism ID: {request['context']['formalism_id']}") print("\nContext:") print(f" Insight: {request['context']['insight']}") print(f" Current Implementation: {request['context']['current_implementation']}") print(f" Qutrit Option: {request['context']['qutrit_option']}") print("\n" + "=" * 70) print("Implementation Comparison") print("=" * 70) print("\nClassical Implementation:") for key, value in request['implementation_comparison']['classical_implementation'].items(): print(f" {key}: {value}") print("\nQutrit Option 1 (Single Qutrit):") for key, value in request['implementation_comparison']['qutrit_options']['option_1_single_qutrit'].items(): print(f" {key}: {value}") print("\nQutrit Option 2 (Two Qutrits):") for key, value in request['implementation_comparison']['qutrit_options']['option_2_two_qutrits'].items(): print(f" {key}: {value}") print("\n" + "=" * 70) print("Efficiency Criteria") print("=" * 70) for criterion, info in request['efficiency_criteria'].items(): print(f" {criterion}: {len(info['questions'])} questions") print("\n" + "=" * 70) print("Existing Qutrit Infrastructure") print("=" * 70) for key, value in request['existing_infrastructure'].items(): print(f" {key}: {value}") print("\n" + "=" * 70) print("Decision Factors (Weights)") print("=" * 70) for factor, weight in request['decision_factors'].items(): print(f" {factor}: {weight}") print("\n" + "=" * 70) print("Expected Deliverables") print("=" * 70) for deliverable in request['expected_deliverables'].keys(): print(f" - {deliverable}") print("\n✅ Swarm query generation completed successfully") print("\nThis query asks the swarm to compare:") print(" - Classical implementation (4 tile states)") print(" - Single qutrit (3 states, need reduction)") print(" - Two qutrits (9 states, over-provisioned)") print("\nEvaluation criteria:") print(" - Computational efficiency") print(" - State space capacity") print(" - Superposition benefits") print(" - Hardware requirements") print(" - Integration complexity") print(" - Error correction") if __name__ == "__main__": main()