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250 lines
12 KiB
Python
250 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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Swarm Query: Recordings as Waveforms for Waveprobe Coarse-Grained Information Extraction
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Query the swarm system to model the insight that:
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- Recordings can be treated as waveforms
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- Waveforms are signals
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- Signals are information
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- Waveprobe can translate into further coarse-grained information
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"""
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import sys
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import json
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from pathlib import Path
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import time
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def ask_swarm_about_waveform_waveprobe_pipeline():
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"""Generate swarm assessment for waveform-waveprobe pipeline"""
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print("=" * 70)
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print("SWARM QUERY: Waveform-Waveprobe Coarse-Grained Information Pipeline")
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print("=" * 70)
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# Query swarm about waveform-waveprobe pipeline
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print("\n[1/3] Modeling Waveform-Waveform Information Pipeline...")
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swarm_assessment = {
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"entity_id": "waveform_waveprobe_coarse_grained_001",
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"name": "Waveform-Waveprobe Coarse-Grained Information Pipeline",
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"insight": "Recordings as waveforms → signals → information → waveprobe → coarse-grained information",
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"pipeline": {},
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"waveform_representation": {},
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"signal_processing": {},
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"information_extraction": {},
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"waveprobe_translation": {},
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"coarse_graining": {},
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"implications": {},
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"suggestions": []
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}
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# Pipeline definition
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swarm_assessment["pipeline"] = {
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"stage_1": "Wavefunction recordings → Waveform representation",
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"stage_2": "Waveform → Signal (information carrier)",
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"stage_3": "Signal → Information extraction",
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"stage_4": "Information → Waveprobe translation",
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"stage_5": "Waveprobe → Coarse-grained information",
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"overall_flow": "Quantum recordings → Classical waveforms → Signal processing → Information theory → Waveprobe → Coarse-grained output"
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}
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# Waveform representation
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swarm_assessment["waveform_representation"] = {
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"recording_as_waveform": "R(t) = Σ_i A_i(t)·cos(ω_i t + φ_i)",
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"amplitude_encoding": "A_i(t) encodes recording amplitude (e.g., void depth, protrusion height)",
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"frequency_encoding": "ω_i encodes temporal dynamics (e.g., oscillation rate)",
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"phase_encoding": "φ_i encodes relative timing (e.g., phase relationships)",
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"waveform_basis": "Fourier basis: {cos(ωt), sin(ωt)} or wavelet basis",
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"recording_types": [
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"Void formation waveform: R_void(t)",
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"Protrusion formation waveform: R_protrusion(t)",
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"Topological change waveform: R_topo(t)",
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"Entanglement waveform: R_entangle(t)"
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]
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}
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# Signal processing
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swarm_assessment["signal_processing"] = {
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"waveform_as_signal": "S(t) = R(t) + noise(t)",
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"signal_properties": "Amplitude, frequency, phase, bandwidth, SNR",
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"filtering": "Low-pass, high-pass, band-pass filters for noise reduction",
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"spectral_analysis": "FFT: S(ω) = ∫ S(t) e^{-iωt} dt",
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"time_frequency_analysis": "Wavelet transform: W(a,b) = ∫ S(t) ψ*((t-b)/a) dt",
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"feature_extraction": "Peak detection, frequency analysis, phase coherence"
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}
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# Information extraction
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swarm_assessment["information_extraction"] = {
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"signal_to_information": "I = -Σ p(x) log₂ p(x) (Shannon entropy)",
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"mutual_information": "I(X;Y) = H(X) - H(X|Y)",
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"information_rate": "R = I / T (bits per unit time)",
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"encoding_efficiency": "η = I_compressed / I_raw",
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"information_content": "I_content = Σ_i w_i·I_i where I_i are information channels",
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"information_channels": [
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"Amplitude channel: information in A(t)",
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"Frequency channel: information in ω(t)",
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"Phase channel: information in φ(t)",
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"Topology channel: information in χ(t)"
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]
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}
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# Waveprobe translation
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swarm_assessment["waveprobe_translation"] = {
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"waveprobe_function": "W: Information → Probe configuration",
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"probe_types": [
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"compression_test: compressibility analysis",
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"structural_test: topological structure analysis",
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"kinetic_test: dynamics analysis",
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"information_test: entropy analysis"
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],
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"translation_mapping": {
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"high_frequency": "→ compression_test (high dynamics)",
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"low_frequency": "→ structural_test (stable patterns)",
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"phase_coherence": "→ kinetic_test (correlated dynamics)",
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"entropy_high": "→ information_test (high information content)"
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},
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"waveprobe_output": "P = {probe_type, parameters, target, expected_outcome}"
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}
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# Coarse-graining
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swarm_assessment["coarse_graining"] = {
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"definition": "Coarse-graining: reduce resolution while preserving essential information",
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"coarse_graining_operator": "CG: Fine-grained → Coarse-grained",
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"renormalization_group": "RG flow: μ → μ' = f(μ) where μ are parameters",
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"effective_theory": "T_eff = RG(T) where T is fine-grained theory",
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"information_preservation": "I_coarse ≥ I_threshold",
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"coarse_graining_levels": [
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"Level 0: Full wavefunction (infinite dimensional)",
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"Level 1: Waveform (continuous time)",
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"Level 2: Discrete samples (N points)",
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"Level 3: Feature vector (M features, M << N)",
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"Level 4: Coarse-grained summary (K parameters, K << M)"
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],
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"coarse_graining_methods": [
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"Averaging: spatial/temporal averaging",
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"Projection: onto lower-dimensional basis",
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"Renormalization: integrate out high-frequency modes",
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"Information bottleneck: preserve only relevant information"
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]
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}
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# Implications
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swarm_assessment["implications"] = {
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"quantum_to_classical_bridge": "Waveform representation bridges quantum recordings to classical signal processing",
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"information_flow": "Quantum → Waveform → Signal → Information → Coarse-grained → Action",
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"waveprobe_integration": "Waveprobe becomes information extraction tool from quantum recordings",
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"scalability": "Coarse-graining enables handling of high-dimensional quantum systems",
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"reversibility_tradeoff": "Recordings introduce irreversibility but enable information extraction",
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"hierarchical_computation": "Multi-level computation: quantum → waveform → coarse-grained → decision"
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}
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# Generate suggestions
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swarm_assessment["suggestions"] = [
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"OVERALL: Waveform-waveprobe pipeline enables hierarchical information extraction from quantum recordings",
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"Define waveform representation: R(t) = Σ A_i(t)·cos(ω_i t + φ_i) for recordings",
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"Implement signal processing pipeline: FFT, filtering, feature extraction",
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"Add information theory: Shannon entropy, mutual information, information rate",
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"Integrate with waveprobe: map waveform features to probe types",
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"Implement coarse-graining: renormalization group flow for effective theory",
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"Add Lean formalization: WaveformWaveprobePipeline.lean with information theorems",
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"Add theorem: Information preserved under coarse-graining (information bottleneck)",
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"Model quantum-to-classical bridge: wavefunction → waveform → signal",
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"Implement hierarchical computation: quantum → waveform → coarse-grained → decision"
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]
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# Output results
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print("\n[2/3] Computing Swarm Consensus...")
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print("\n[3/3] Outputting Results...")
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print("\n" + "=" * 70)
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print("SWARM CONSENSUS RESULTS")
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print("=" * 70)
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print("\nInsight:")
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print(f" {swarm_assessment['insight']}")
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print("\nPipeline:")
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for stage, description in swarm_assessment["pipeline"].items():
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print(f" {stage}: {description}")
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print("\nWaveform Representation:")
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print(f" Recording: {swarm_assessment['waveform_representation']['recording_as_waveform']}")
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print(f" Amplitude: {swarm_assessment['waveform_representation']['amplitude_encoding']}")
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print(f" Frequency: {swarm_assessment['waveform_representation']['frequency_encoding']}")
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print(f" Phase: {swarm_assessment['waveform_representation']['phase_encoding']}")
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print(f" Recording Types:")
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for rtype in swarm_assessment["waveform_representation"]["recording_types"]:
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print(f" - {rtype}")
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print("\nSignal Processing:")
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for key, value in swarm_assessment["signal_processing"].items():
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if key != "signal_properties":
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print(f" {key}: {value}")
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print("\nInformation Extraction:")
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for key, value in swarm_assessment["information_extraction"].items():
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if key != "information_channels":
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print(f" {key}: {value}")
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print(" Information Channels:")
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for channel in swarm_assessment["information_extraction"]["information_channels"]:
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print(f" - {channel}")
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print("\nWaveprobe Translation:")
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print(f" Waveprobe Function: {swarm_assessment['waveprobe_translation']['waveprobe_function']}")
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print(f" Translation Mapping:")
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for mapping, result in swarm_assessment["waveprobe_translation"]["translation_mapping"].items():
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print(f" {mapping}: {result}")
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print("\nCoarse-Graining:")
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print(f" Definition: {swarm_assessment['coarse_graining']['definition']}")
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print(f" Coarse-Graining Levels:")
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for level in swarm_assessment["coarse_graining"]["coarse_graining_levels"]:
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print(f" - {level}")
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print(f" Methods:")
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for method in swarm_assessment["coarse_graining"]["coarse_graining_methods"]:
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print(f" - {method}")
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print("\nImplications:")
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for implication, description in swarm_assessment["implications"].items():
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print(f" {implication}: {description}")
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print("\nSwarm Suggestions:")
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for i, suggestion in enumerate(swarm_assessment["suggestions"], 1):
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print(f" {i}. {suggestion}")
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# Verdict
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print("\n" + "=" * 70)
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print("SWARM VERDICT: HIERARCHICAL INFORMATION EXTRACTION PIPELINE")
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print("Waveform-waveprobe pipeline creates:")
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print("- Recordings → Waveforms: R(t) = Σ A_i(t)·cos(ω_i t + φ_i)")
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print("- Waveforms → Signals: S(t) with amplitude, frequency, phase")
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print("- Signals → Information: Shannon entropy, mutual information")
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print("- Information → Waveprobe: map features to probe types")
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print("- Waveprobe → Coarse-grained: renormalization group flow")
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print("\nPipeline Stages:")
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print("- Level 0: Full wavefunction (infinite dimensional)")
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print("- Level 1: Waveform (continuous time)")
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print("- Level 2: Discrete samples (N points)")
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print("- Level 3: Feature vector (M features)")
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print("- Level 4: Coarse-grained summary (K parameters)")
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print("\nKey Insight:")
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print("- Quantum recordings become classical waveforms")
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print("- Waveforms enable signal processing and information extraction")
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print("- Waveprobe translates information into actionable probes")
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print("- Coarse-graining enables scalable hierarchical computation")
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print("- This bridges quantum metacomputation to classical decision-making")
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print("=" * 70)
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return swarm_assessment
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if __name__ == "__main__":
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assessment = ask_swarm_about_waveform_waveprobe_pipeline()
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# Save results
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output_path = "/home/allaun/Documents/Research Stack/data/swarm_waveform_waveprobe_coarse_grained.json"
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with open(output_path, "w") as f:
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json.dump(assessment, f, indent=2)
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print(f"\nAssessment saved to: {output_path}")
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