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