neuro-quantum-research - SKILL.md Agent Skill

name: neuro-quantum-research description: > Research methodology for the intersection of neuroscience and quantum physics/computing. Use when analyzing papers or research at the boundary of brain science and quantum mechanics, including quantum neural networks, quantum memory models of cognition, quantum-inspired brain simulation, quantum computing for neuroscience, and quantum effects in biological systems. Triggers: quantum neuroscience, quantum brain, quantum neural network, quantum memory, quantum cognition, neuromorphic quantum, quantum machine learning brain.

Neuro-Quantum Research

Overview

Analyze and extract patterns from research at the intersection of neuroscience and quantum physics/computing. This field spans quantum neural networks, quantum-inspired models of consciousness, quantum memory as a model for biological memory, and quantum computing applied to brain simulation.

Key Research Directions

1. Organic-Quantum Brain Hypothesis (q-bio.NC + quant-ph)

3-Layer Quantum Brain Hypothesis extended to engineered organic materials
Magnetic-field-free quantum computing via SVILC qubits in organic materials
Four paths: (P1) flavin-nitroxide radical-pair reservoir, (P2) PTM radical arrays in COFs, (P3) SVILC on κ-(BEDT-TTF) salts, (P4) SSH soliton on trans-polyacetylene
Key insight: If organic materials support quantum computing without external fields, quantum brain effects become experimentally testable

2. Information Geometric Quantum Dynamics (quant-ph + statistical physics)

Classical and quantum dynamics in information geometric spaces (Bernoulli random variables)
Direct connection to Friston's free energy principle (Bayesian brain hypothesis)
Provides rigorous mathematical bridge between quantum formalism and neural prediction

3. Quantum Spectroscopy for Biomolecular Sensing (quant-ph)

Quantum spectroscopy with undetected photons for mid-infrared protein detection
Double-pass quantum interferometer for non-destructive biomolecular analysis
Applications to brain-relevant peptides (NT-proBNP, BSA)

4. Quantum Memory and Scrambling (quant-ph)

Quantum memory protocols (error correction, entanglement distribution)
Scrambling as a model for information processing in neural networks
Classical neural networks as perspectives on quantum memory dynamics
Key metric: entanglement fidelity, erasure error rates

2. Quantum Neural Networks (q-bio.NC + quant-ph)

Stochastic quantum neural network models
Quantum-inspired architectures for brain simulation
Brain-inspired quantum vision models
Superposition and entanglement as computational primitives for cognition

3. Multisensory Learning and Memory Engrams

How sensory modalities recruit neurons across brain regions
Memory engram formation and consolidation
Cross-modal neural recruitment patterns
Connection to quantum-like superposition in memory states

4. Neural Decoding and Brain-Computer Interfaces

Saliency-aware multi-view neural decoding
Functional connectivity-guided band selection
Intrinsic brain network analysis
Object-centric neural representations

Analysis Workflow

Step 1: Paper Classification

Classify papers by primary domain:

quant-ph dominant: Quantum physics paper with neuroscience applications
q-bio.NC dominant: Neuroscience paper using quantum-inspired methods
cs.AI/cs.LG: Machine learning at the intersection
Cross-disciplinary: Equal contribution from both fields

Step 2: Extract Technical Patterns

For each paper, identify:

Core methodology (quantum algorithm, neural architecture, hybrid model)
Mathematical framework (Hilbert space, Hamiltonian dynamics, graph theory)
Experimental validation (simulation, biological data, quantum hardware)
Performance metrics (accuracy, fidelity, computational efficiency)

Step 3: Map to Knowledge Graph

Add entities to kg.db with full metadata
Create relationships based on shared categories, authors, methodologies
Generate vector embeddings for similarity search
Run PageRank to identify influential papers

Step 4: Identify Skill Patterns

Extract reusable methodologies:

Quantum error correction patterns applicable to neural noise
Neural network architectures inspired by quantum principles
Brain simulation techniques using quantum computing
Memory models bridging quantum and biological frameworks

Common Pitfalls

Confusing quantum-inspired classical algorithms with actual quantum computing
Over-interpreting quantum effects in biological systems without experimental evidence
Missing the distinction between quantum neural networks (quantum hardware) and quantum-inspired neural networks (classical hardware with quantum math)
Not checking arxiv rate limits (429 errors) - use 5+ second delays between requests

Resources

Key Categories to Search arXiv

quant-ph + q-bio.NC: Direct neuroscience-quantum intersection
quant-ph + cs.LG: Quantum machine learning
q-bio.NC + cs.AI: AI-brain modeling
cs.NE: Neural and Evolutionary Computing (often includes quantum-inspired methods)

Knowledge Graph Tools

scripts/kg_analysis.py: Vector similarity search, PageRank, community detection
kg.db: SQLite knowledge graph with entities (129+), vectors (BLOB), relationships (1136+)
scripts/kg_tool/target/release/kg_tool: Compiled KG binary — supports: import-paper, generate-embeddings, search, pagerank, communities, stats. Commands vector, embed, import, community, louvain, analyze are NOT supported.

Import Workflow

Search arXiv via proxy (http://127.0.0.1:7890) using HTTPS endpoint: https://export.arxiv.org/api/query
Parse XML response for paper metadata
Filter duplicates against existing kg.db entries (UNIQUE constraint on url column)
Insert into kg_entities with proper schema
Generate vector embeddings for similarity search (store as JSON-encoded bytes in BLOB column)
Create category-based relationships
Run analysis (PageRank, community detection)

Examples

Example: Analyzing a New Paper

User: "分析这篇量子神经网络论文: arxiv 2604.25663"

Agent should:

Fetch paper details from arXiv API
Import into kg.db
Run vector similarity search for related papers
Check PageRank to see if it connects to influential work
Extract any reusable skill patterns