hybrid-quantum-classical-architecture

name

hybrid-quantum-classical-architecture

description

Design and optimization of hybrid quantum-classical computing system architectures. Includes dataflow frameworks, fault-tolerant design, resource efficiency optimization, automated architecture search, and quantum ML application patterns. Use when: (1) designing quantum computing systems, (2) optimizing hybrid quantum-classical architectures, (3) implementing fault-tolerant quantum systems, (4) searching for optimal quantum architectures, (5) building hybrid quantum ML pipelines for medical/finance applications, or (6) working with quantum system engineering concepts.

Hybrid Quantum-Classical Architecture

Design and optimize hybrid quantum-classical computing system architectures with focus on dataflow, fault tolerance, resource efficiency, automated architecture search, and quantum ML application patterns.

Core Concepts

1. Hybrid Architecture Types

• Dataflow Frameworks: Quantum-classical data pipelines (e.g., Tierkreis)
• Fault-Tolerant Systems: Error correction with resource constraints (e.g., LSQCA)
• Modular Hybrid Systems: Spin-optical, photon-matter combinations
• Automated Search: ML-driven architecture optimization
• Quantum ML Pipelines: Hybrid quantum-classical models for diagnosis/classification

2. Key Design Patterns

Pattern A: Dataflow-Centric

Classical Preprocessing → Quantum Execution → Classical Postprocessing
                      ↓
                  Error Correction Layer

Pattern B: Resource-Efficient FTQC

Load/Store Architecture → Logical Operations → Memory Management
                      ↓
                  Qubit Connectivity Optimization

Pattern C: Modular Hybrid

Module 1 (Spin) + Module 2 (Optical) → Entangling Interface → Scalable Architecture

Pattern D: Quantum ML Feature Fusion

Classical Backbone (ResNet/CNN) → Feature Extract → Quantum Circuit (VQC) → Measurement → Classifier

• SHF: Static Hybrid Fusion — offline extraction, simple concatenation
• DHF: Dynamic Hybrid Fusion — end-to-end co-adaptation
• TSHF: Temperature-Scaled Hybrid Fusion — learnable scalar for gradient balance (best: 87.82% acc on BreastMNIST)
• Constraint: Qubit count must match latent dimension for stable training

Pattern E: Tensor-Network Quantum Processing (NISQ-constrained systems)

High-D Input → Tensor-Network Compression (TTN/MPS/MERA) → Compact Latent → Small-Qubit QC → Readout

• TTN+Quantum-Enhanced-Processor is the most balanced combination
• Serves dual role: enables small-qubit processing AND reduces communication overhead in federated settings

Tools

Knowledge Graph Integration

Use sqlite3 directly on kg.db (NOT kg_tool subcommands — they don't exist for pagerank/louvain):

# Query papers
sqlite3 kg.db "SELECT id, title FROM kg_entities WHERE category LIKE '%quant%'"

# Check relations
sqlite3 kg.db "SELECT source, target, type, weight FROM kg_relations LIMIT 10"

kg.db path: /Users/hiyenwong/.openclaw/workspace/kg.db

ArXiv Search

Search for latest quantum architecture papers (HTTPS required — HTTP blocked by security scanner):

curl -s "https://export.arxiv.org/api/query?search_query=all:quantum+architecture&max_results=5" --proxy http://127.0.0.1:7890

Workflow

Step 1: Identify Architecture Requirements

Analyze the problem to determine:

• Compute Model: Quantum-only vs hybrid
• Fault Tolerance Level: Required error rates
• Resource Constraints: Qubit count, connectivity
• Performance Goals: Speedup targets, accuracy requirements

Step 2: Survey Existing Architectures

Search knowledge graph for similar approaches, design patterns, and key papers.

Step 3: Design Architecture

Choose appropriate pattern based on constraints:

• Dataflow for cloud/hybrid execution
• LSQCA for resource-constrained FTQC
• Modular for heterogeneous systems
• Pattern D (feature fusion) for quantum ML classification tasks
• Pattern E (tensor-network compression) for NISQ-constrained or federated settings

Step 4: Evaluate Architecture

Metrics: resource efficiency (qubits per logical op), error rates, scalability, flexibility.

Step 5: Iterate and Optimize

Use automated search: QAS, unsupervised representation learning, RL optimization.

References

• Dataflow: See references/tierkreis.md for Tierkreis framework
• Fault Tolerance: See references/lsqca.md for LSQCA architecture
• Modular Systems: See references/spin-optical.md for hybrid systems
• Automated Search: See references/qas.md for architecture search methods
• Quantum ML: See references/quantum-ml-patterns.md for hybrid ML architectures

Examples

Example 1: Design Cloud Quantum Platform

User: "Design a cloud-accessible quantum computing platform"

Workflow:

1. Requirements: Cloud access, multiple users, job scheduling
2. Survey: Tierkreis (dataflow), Tianyan (cloud platform)
3. Design: Dataflow architecture with classical job scheduler
4. Evaluate: User concurrency, job throughput, error rates
5. Iterate: Optimize scheduling with QAS

Example 2: Hybrid Quantum ML for Medical Diagnosis

User: "Build a hybrid quantum-classical model for breast cancer classification"

Workflow:

1. Requirements: Medical image input, clinical-grade accuracy
2. Choose Pattern D (feature fusion) with TSHF strategy
3. Design: ResNet backbone → 4-qubit VQC → TSHF fusion → classifier
4. Evaluate: Accuracy, F1, AUC-ROC on BreastMNIST
5. If NISQ-constrained: Add Pattern E (TTN compression) before quantum circuit

Related Skills

• arxiv-search: Find quantum architecture papers
• quantum-ml-research: Quantum ML research and paper analysis
• skill-extractor: Extract design patterns from papers

Notes

• arxiv API requires HTTPS (HTTP blocked by security scanner)
• web_extract blocks arxiv URLs — use curl + XML parsing instead
• kg.db path: /Users/hiyenwong/.openclaw/workspace/kg.db
• No kg_tool subcommands for pagerank/louvain — implement in Python with sqlite3