quantum-ml-research

name

quantum-ml-research

description

Quantum Machine Learning research assistant. Searches arxiv for quantum ML papers, analyzes patterns from knowledge graph (kg.db), extracts concepts from quantum circuits, neural networks, and finance/medical applications. Use when researching quantum computing applications, quantum algorithms, quantum portfolio optimization, quantum Monte Carlo, quantum neural networks, hybrid quantum-classical medical classification, or analyzing quantum ML literature. Activation: quantum ML research, quantum machine learning, quantum circuit learning, quantum neural network, 量子机器学习, quantum finance research, quantum medical imaging.

Quantum Machine Learning Research

Research assistant for quantum computing applications in machine learning, finance, and medical diagnosis.

Activation Keywords

• quantum ML research
• quantum machine learning
• quantum circuit learning
• quantum neural network
• quantum portfolio optimization
• quantum Monte Carlo
• quantum algorithms
• quantum medical imaging
• hybrid quantum medical
• 量子机器学习
• 量子神经网络

Tools Used

• exec: Run Python scripts for arxiv search, sqlite3 for kg.db
• read: Load skill files, analyze papers
• web_search: Search for quantum ML papers
• sqlite3: Query knowledge graph (kg.db) directly

Critical Pitfalls

• arxiv API requires HTTPS: http://export.arxiv.org/api/query is blocked by security scanner. Always use https://export.arxiv.org/api/query.
• web_extract blocks arxiv URLs: The web_extract tool returns "Blocked: URL targets a private or internal network address" for arxiv. Use curl + arxiv API XML parsing instead.
• kg.db path: /Users/hiyenwong/.openclaw/workspace/kg.db (NOT /Users/hiyenwong/wiki/kg.db)
• No kg_tool subcommands for pagerank/louvain: The kg_tool binary does not have pagerank or louvain subcommands. Implement these in Python using sqlite3 directly.

Workflow

Step 1: Search Literature

IMPORTANT: arxiv API requires HTTPS. HTTP URLs are blocked by security scanner. web_extract also blocks arxiv URLs.

# Correct: HTTPS arxiv API
url = 'https://export.arxiv.org/api/query?search_query=all:quantum+machine+learning&max_results=10&sortBy=submittedDate'

# With proxy (if needed):
# curl -s "https://export.arxiv.org/api/query?id_list=2504.13910,2604.16953" --proxy http://127.0.0.1:7890

Parse the Atom XML response with Python's xml.etree.ElementTree:

import xml.etree.ElementTree as ET
ns = {'atom': 'http://www.w3.org/2005/Atom', 'arxiv': 'http://arxiv.org/schemas/atom'}
root = ET.fromstring(xml_response)
for entry in root.findall('atom:entry', ns):
    title = entry.find('atom:title', ns).text.strip()
    summary = entry.find('atom:summary', ns).text.strip()
    # ... extract authors, categories, published date

Categories to search:

• quant-ph - Quantum Physics
• cs.LG - Machine Learning
• cs.CV - Computer Vision (medical imaging)
• cs.AI - Artificial Intelligence
• cs.ET - Emerging Technologies

Step 2: Import to Knowledge Graph

kg.db schema:

CREATE TABLE kg_entities (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    title TEXT NOT NULL,
    url TEXT UNIQUE NOT NULL,
    content TEXT,
    authors TEXT,
    published_date TEXT,
    category TEXT,
    source TEXT
);
CREATE TABLE kg_vectors (id, entity_id, vector_data BLOB);
CREATE TABLE kg_relations (source INT, target INT, type TEXT, weight REAL);
CREATE TABLE kg_relationships (source_id, target_id, relationship_type, weight);

Import paper:

INSERT OR IGNORE INTO kg_entities (title, url, content, authors, published_date, category, source)
VALUES ('title', 'url', 'abstract_text', 'authors', 'date', 'categories', 'arxiv');

Generate TF-IDF vector:

import json, sqlite3
# Build term frequency from abstract, multiply by IDF from corpus
# Store top-50 features as JSON in vector_data column

Step 3: Analyze Knowledge Graph (Python, not kg_tool)

import sqlite3
conn = sqlite3.connect('/Users/hiyenwong/.openclaw/workspace/kg.db')
cursor = conn.cursor()

# PageRank (implement in Python — no kg_tool subcommand)
cursor.execute("SELECT source, target, weight FROM kg_relations")
edges = cursor.fetchall()
# Build adjacency, iterate damping factor 0.85 for ~20 iterations

# Community detection by category clustering
cursor.execute("SELECT id, category FROM kg_entities WHERE category IS NOT NULL")
# Group papers by shared categories

# Vector similarity
cursor.execute("SELECT entity_id, vector_data FROM kg_vectors")
# Cosine similarity on JSON-encoded feature vectors

Step 4: Extract Patterns

From quantum ML papers, extract:

1. Quantum Circuit Architecture: Gate types, circuit depth, qubit count
2. Learning Paradigm: VQE, QAOA, quantum annealing, hybrid quantum-classical
3. Application Domain: Finance, chemistry, medical diagnosis, optimization
4. Performance Metrics: Accuracy, F1, AUC-ROC, quantum advantage claims
5. Feature Fusion Strategy: How quantum and classical features are combined

Key Research Areas

Quantum Medical Image Classification (see references/quantum-medical.md)

Key pattern: Hybrid quantum-classical architecture for diagnosis

Classical Backbone (ResNet/CNN) → Feature Extractor → Quantum Circuit (4-qubit VQC)
→ Measurement → Classifier

Feature Fusion Strategies:

• SHF (Static): Offline extraction, simple concatenation
• DHF (Dynamic): End-to-end co-adaptation
• TSHF (Temperature-Scaled): Learnable scalar balances gradients ⭐ Best

Key results: TSHF + ResNet + trainable QC → 87.82% acc, 91.77% F1 on BreastMNIST

Privacy-aware federated: Tensor-network (TTN/MPS/MERA) frontend → MPC aggregation → Quantum-Enhanced Processor

Quantum Circuit Learning

Papers focus on:

• Parameterized quantum circuits as neural networks
• Structure optimization for shallow circuits
• Quantum circuit optimization with RL
• Framework-agnostic quantum ML

Quantum Finance

Applications:

• Portfolio optimization (QAOA, quantum annealing)
• Risk analytics (quantum Monte Carlo)
• Derivative pricing
• Option pricing

Knowledge Graph Integration

Schema Notes (CRITICAL — verified 2026-05-06)

• kg.db location: /Users/hiyenwong/.openclaw/workspace/kg.db
• kg_vectors: vector_data is raw float32 bytes (256-dim = 1024 bytes per vector), NOT JSON strings. Load with: np.frombuffer(row[0], dtype=np.float32)
• kg_relationships: Column is relationship_type (NOT relationship). Schema: (id, source_id, target_id, relationship_type, weight, created_at)
• kg_relations: Column is type (NOT rel_type). Schema: (source, target, type, weight)

Query Patterns

# kg.db is at /Users/hiyenwong/.openclaw/workspace/kg.db

# Find quantum finance papers
sqlite3 kg.db "SELECT id, title FROM kg_entities WHERE category LIKE '%quant%' AND title LIKE '%Finance%'"

# Find quantum medical papers
sqlite3 kg.db "SELECT id, title FROM kg_entities WHERE category LIKE '%quant%' AND (title LIKE '%Medical%' OR title LIKE '%Cancer%' OR title LIKE '%Diagnosis%')"

# Count by category
sqlite3 kg.db "SELECT category, COUNT(*) FROM kg_entities GROUP BY category ORDER BY COUNT(*) DESC"

arXiv Search (with fallback)

IMPORTANT: arXiv API (export.arxiv.org) is often unreachable from this environment — it either times out through the sandbox or returns "Rate exceeded" through the proxy. Always use the fallback workflow below.

Fallback Workflow (preferred)

# 1. Search via web_search tool for arxiv papers
# Query: "site:arxiv.org quantum medical healthcare" or similar
# Extract arxiv IDs from results (e.g. 2511.02051, 2603.17790)

# 2. Download HTML for each paper
curl -s -x http://127.0.0.1:7890 --max-time 20 \
  -o /tmp/paper.html "https://arxiv.org/abs/2511.02051"

# 3. Extract metadata via Python regex (NOT piping curl to python)
python3 -c "
import re
with open('/tmp/paper.html') as f: html = f.read()
m = re.search(r'<blockquote[^>]*class=\"abstract[^\"]*\">(.*?)</blockquote>', html, re.DOTALL)
abstract = re.sub(r'<[^>]+>', ' ', m.group(1)).strip() if m else ''
m = re.search(r'<title>(.*?)</title>', html)
title = m.group(1).strip() if m else ''
title = re.sub(r'^\[\d+\.\d+\]\s*', '', title)
print(f'{title}\\n{abstract[:500]}')
"

Direct API (when available)

Use https:// not http:// — the API returns 301 redirect:

url = 'https://export.arxiv.org/api/query?search_query=all:quantum+AND+all:medical&max_results=5'

References

• references/quantum-medical.md — Quantum ML for medical diagnosis
• references/QUANTUM_FINANCE.md — Quantum finance applications
• references/QUANTUM_CIRCUITS.md — Quantum circuit learning patterns

Related Skills

• arxiv-search: General arxiv search
• hybrid-quantum-classical-architecture: System-level hybrid architecture design
• skill-extractor: Extract patterns from papers