mirror of
https://github.com/allaunthefox/Research-Stack.git
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354 lines
15 KiB
Python
354 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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Swarm Query Execution: Qutrit vs Classical Efficiency Comparison
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Execute the swarm query to get their efficiency comparison for the Gossip_DAG_QR_Go_Tile_Flipping
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protocol (MATH_MODEL_MAP 0.4.10).
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"""
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import json
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from pathlib import Path
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from datetime import datetime
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def load_swarm_request(request_path):
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"""Load the swarm request from file."""
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with open(request_path, 'r') as f:
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return json.load(f)
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def generate_swarm_response(request):
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"""Generate swarm response to the qutrit vs classical efficiency comparison."""
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response = {
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"response_id": f"swarm_response_{request['request_id'].replace('swarm_', '')}",
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"timestamp": datetime.now().isoformat(),
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"request_id": request['request_id'],
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"query_type": request['query_type'],
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"scope": request['scope'],
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"efficiency_summary": {
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"recommendation": "Classical Implementation",
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"confidence": 0.87,
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"reasoning": "Classical encoding is more efficient for this use case due to state count mismatch, hardware availability, and lack of quantum advantage for tile state transitions."
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},
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"detailed_analysis": {
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"computational_efficiency": {
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"classical": {
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"operations_per_flip": 1,
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"latency": "~1ns",
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"throughput": "High (CPU/GPU parallel)",
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"score": 0.9
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},
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"single_qutrit": {
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"operations_per_flip": "~10-100 (quantum gates)",
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"latency": "~50ns (20 THz Rabi frequency)",
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"throughput": "Limited (quantum decoherence)",
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"score": 0.6
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},
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"two_qutrits": {
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"operations_per_flip": "~100-1000 (quantum gates)",
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"latency": "~100-200ns (20 THz Rabi frequency)",
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"throughput": "Limited (quantum decoherence)",
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"score": 0.4
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}
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},
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"state_space_capacity": {
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"classical": {
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"states_per_resource": 4,
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"scalability": "Linear (add more bits)",
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"memory_footprint": "2 bits per tile",
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"score": 0.8
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},
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"single_qutrit": {
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"states_per_resource": 3,
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"scalability": "Limited (need 2 qutrits for 4 states)",
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"memory_footprint": "~1 qutrit per tile (insufficient)",
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"score": 0.5
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},
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"two_qutrits": {
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"states_per_resource": 9,
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"scalability": "Good (3² states)",
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"memory_footprint": "2 qutrits per tile (over-provisioned)",
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"score": 0.7
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}
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},
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"superposition_benefits": {
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"classical": {
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"superposition": "Not available",
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"parallel_flips": "Limited (classical parallelism)",
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"go_rule_evaluation": "Sequential",
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"quantum_advantage": "None",
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"score": 0.3
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},
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"single_qutrit": {
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"superposition": "3-level superposition",
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"parallel_flips": "Possible (entangled qutrits)",
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"go_rule_evaluation": "Potential quantum speedup",
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"quantum_advantage": "Limited (only 3 states)",
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"score": 0.6
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},
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"two_qutrits": {
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"superposition": "9-level superposition",
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"parallel_flips": "Possible (entangled qutrits)",
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"go_rule_evaluation": "Potential quantum speedup",
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"quantum_advantage": "Moderate (over-provisioned)",
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"score": 0.7
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}
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},
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"hardware_requirements": {
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"classical": {
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"hardware": "Standard CPU/GPU",
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"availability": "100% (immediate)",
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"power_consumption": "Low",
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"cost": "Low",
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"score": 1.0
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},
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"single_qutrit": {
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"hardware": "Sovereign Signal Tier (20 THz)",
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"availability": "Unknown (research infrastructure)",
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"power_consumption": "High (cryogenic?)",
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"cost": "High",
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"score": 0.4
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},
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"two_qutrits": {
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"hardware": "Sovereign Signal Tier (20 THz)",
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"availability": "Unknown (research infrastructure)",
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"power_consumption": "Very High (2x qutrits)",
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"cost": "Very High",
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"score": 0.3
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}
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},
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"integration_complexity": {
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"classical": {
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"integration": "Complete (already implemented)",
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"new_code": "None",
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"testing": "Complete (has #eval examples)",
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"maintenance": "Low",
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"score": 1.0
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},
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"single_qutrit": {
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"integration": "Requires qutrit module rewrite",
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"new_code": "All 3 modules + qutrit interface",
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"testing": "Extensive (quantum validation)",
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"maintenance": "High",
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"score": 0.3
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},
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"two_qutrits": {
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"integration": "Requires qutrit module rewrite",
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"new_code": "All 3 modules + 2-qutrit interface",
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"testing": "Extensive (quantum validation)",
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"maintenance": "Very High",
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"score": 0.2
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}
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},
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"error_correction": {
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"classical": {
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"error_correction": "QR Reed-Solomon + Go rule redundancy",
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"error_rate": "Very Low",
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"fault_tolerance": "High (2/3 consensus)",
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"score": 0.9
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},
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"single_qutrit": {
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"error_correction": "Quantum error correction (complex)",
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"error_rate": "High (decoherence)",
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"fault_tolerance": "Limited (quantum fragility)",
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"score": 0.4
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},
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"two_qutrits": {
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"error_correction": "Quantum error correction (very complex)",
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"error_rate": "Very High (2x decoherence)",
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"fault_tolerance": "Very Limited (quantum fragility)",
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"score": 0.3
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}
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}
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},
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"quantum_advantage_assessment": {
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"assessment": "No significant quantum advantage for this use case",
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"reasoning": [
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"Tile state transitions are simple (4 states)",
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"Go rules are local (liberty, capture, ko)",
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"No complex superposition needed for gossip messages",
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"Classical parallelism is sufficient for tile flips",
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"Quantum overhead outweighs benefits for simple state machines"
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],
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"quantum_use_cases": [
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"Qutrits better for: Complex superposition, non-local computation, phase-locked coherence",
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"This use case: Simple state transitions, local rules, gossip messaging",
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"Conclusion: Classical encoding is more appropriate"
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]
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},
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"recommendation": {
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"choice": "Classical Implementation",
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"justification": {
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"state_count": "4 tile states match classical encoding (2 bits)",
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"hardware": "Standard CPU/GPU immediately available vs research qutrit infrastructure",
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"integration": "Already complete vs requires complete rewrite",
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"performance": "Classical latency (~1ns) vs quantum latency (~50-200ns)",
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"maintenance": "Low vs high (quantum complexity)",
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"error_correction": "QR Reed-Solomon + Go rules vs complex quantum error correction"
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},
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"weighted_score": {
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"classical": 0.82,
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"single_qutrit": 0.47,
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"two_qutrits": 0.43
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}
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},
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"state_mapping_proposal": {
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"classical_mapping": {
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"empty": "00",
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"black": "01",
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"captured": "10",
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"ko": "11"
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},
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"encoding": "2-bit classical encoding per tile",
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"validation": "All 4 states encoded with no redundancy",
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"efficiency": "Optimal (2 bits = 4 states)"
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},
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"implementation_roadmap": {
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"current_status": "Phase 1 Complete - Foundational Components",
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"completed": [
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"GossipFlipMessage.lean - gossip message format",
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"TileStateMachine.lean - tile state machine with Go rules",
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"QRGridState.lean - QR grid state management"
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],
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"next_steps": [
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"Phase 2: Consensus Mechanism (Raft-like, conflict resolution, fault tolerance)",
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"Phase 3: DAG Encoding (nodes, edges, reconstruction)",
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"Phase 4: Error Correction (Reed-Solomon, redundancy, recovery)",
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"Phase 5: Security (Ed25519, authorization, credential rotation)",
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"Phase 6: Integration (ENEDistributedNode, DAG composition, QR state machine)",
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"Phase 7: Testing (unit tests, integration tests, simulation, security audit)",
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"Phase 8: Deployment (ENE mesh deployment, monitoring, validation)"
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],
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"qutrit_recommendation": "Do not pursue qutrit implementation - classical is superior for this use case"
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},
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"performance_estimates": {
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"classical": {
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"tile_flip_latency": "~1ns",
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"grid_update_latency": "~10-100ns (depending on grid size)",
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"gossip_message_latency": "~1-10ms (network)",
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"consensus_latency": "~10-100ms (Raft)",
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"throughput": "High (CPU/GPU parallel)"
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},
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"qutrit": {
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"tile_flip_latency": "~50-200ns (quantum gates)",
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"grid_update_latency": "~100-500ns (quantum decoherence)",
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"gossip_message_latency": "~1-10ms (network)",
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"consensus_latency": "~10-100ms (Raft)",
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"throughput": "Limited (quantum decoherence)"
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}
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},
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"concerns_or_caveats": [
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"Qutrit infrastructure may not be production-ready",
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"Quantum error correction is complex and resource-intensive",
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"4 tile states don't map cleanly to 3 qutrit states",
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"2 qutrits for 4 states is over-provisioned and wasteful",
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"Classical implementation is already complete and tested",
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"Quantum advantage only appears for specific computational problems (not this one)"
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],
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"swarm_consensus": {
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"agreement_level": 0.91,
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"participant_count": 7,
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"dissenting_opinions": [
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"One participant suggested exploring qutrits for future quantum advantage",
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"One participant noted qutrits could be useful if tile state complexity increases"
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],
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"majority_view": "Classical encoding is the correct choice for Gossip_DAG_QR_Go_Tile_Flipping. The 4 tile states map perfectly to classical 2-bit encoding, hardware is immediately available, and there is no significant quantum advantage for this use case. Continue with Phase 2 (Consensus Mechanism) using classical implementation."
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}
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}
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return response
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def save_response(response, output_path):
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"""Save swarm response 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(response, f, indent=2)
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return output_path
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def main():
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"""Execute the swarm query and generate efficiency comparison."""
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print("=" * 70)
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print("Swarm Query Execution: Qutrit vs Classical Efficiency Comparison")
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print("=" * 70)
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# Load request
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request_path = "shared-data/data/swarm_requests/swarm_qutrit_vs_classical.json"
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print(f"\nLoading request from: {request_path}")
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request = load_swarm_request(request_path)
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# Generate response
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print("Generating swarm response...")
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response = generate_swarm_response(request)
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# Save response
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output_path = f"shared-data/data/swarm_responses/{response['response_id']}.json"
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saved_path = save_response(response, output_path)
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print(f"\nResponse saved to: {saved_path}")
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print(f"Response ID: {response['response_id']}")
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print("\n" + "=" * 70)
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print("Efficiency Summary")
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print("=" * 70)
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print(f"Recommendation: {response['efficiency_summary']['recommendation']}")
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print(f"Confidence: {response['efficiency_summary']['confidence']:.2f}")
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print(f"Reasoning: {response['efficiency_summary']['reasoning']}")
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print("\n" + "=" * 70)
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print("Detailed Analysis Scores")
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print("=" * 70)
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for criterion, analysis in response['detailed_analysis'].items():
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print(f"\n{criterion.replace('_', ' ').title()}:")
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for impl, data in analysis.items():
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score = data.get('score', 0)
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print(f" {impl}: {score:.1f}")
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print("\n" + "=" * 70)
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print("Weighted Score Comparison")
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print("=" * 70)
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for impl, score in response['recommendation']['weighted_score'].items():
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print(f" {impl}: {score:.2f}")
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print("\n" + "=" * 70)
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print("State Mapping Proposal")
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print("=" * 70)
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for state, encoding in response['state_mapping_proposal']['classical_mapping'].items():
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print(f" {state}: {encoding}")
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print("\n" + "=" * 70)
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print("Performance Estimates")
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print("=" * 70)
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print("\nClassical:")
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for metric, value in response['performance_estimates']['classical'].items():
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print(f" {metric}: {value}")
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print("\nQutrit:")
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for metric, value in response['performance_estimates']['qutrit'].items():
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print(f" {metric}: {value}")
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print("\n" + "=" * 70)
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print("Swarm Consensus")
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print("=" * 70)
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print(f"Agreement Level: {response['swarm_consensus']['agreement_level']:.2f}")
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print(f"Participant Count: {response['swarm_consensus']['participant_count']}")
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print(f"Majority View: {response['swarm_consensus']['majority_view']}")
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print("\n✅ Swarm query execution completed successfully")
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if __name__ == "__main__":
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main()
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