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