Qiskit Cirq Quantum Error

Qiskit Cirq Quantum Error

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

This skill covers the development of software for quantum computers, focusing on quantum algorithms, quantum error correction, and hybrid quantum-classical applications. It includes using quantum SDKs (Qiskit, Cirq, PennyLane), quantum circuit design, and optimization for NISQ (Noisy Intermediate-Scale Quantum) devices.

Why This Matters

• Quantum Advantage: Solve problems intractable for classical computers
• Future-Proofing: Prepare for quantum computing era
• Scientific Discovery: Enable new computational approaches for research

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Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • Problem specification (optimization, simulation, etc.)
  • Quantum circuit definitions
  • Hardware constraints (qubit count, connectivity, noise)
  • Algorithm parameters and hyperparameters
• Entry Conditions:
  • Quantum SDK installed (Qiskit, Cirq, PennyLane)
  • Access to quantum hardware or simulator
  • Understanding of quantum computing fundamentals
  • Python 3.9+ with required libraries
• Outputs:
  • Quantum circuits and algorithms
  • Measurement results and statistics
  • Optimized parameters (for variational algorithms)
  • Performance benchmarks and analysis
• Artifacts Required (Deliverables):
  • Quantum circuit code (Qiskit/​Cirq)
  • Algorithm implementations
  • Error correction schemes
  • Benchmark results and analysis
• Acceptance Evidence:
  • Quantum circuit runs successfully on hardware/​simulator
  • Results match theoretical expectations (within noise)
  • Error correction reduces error rate
  • Performance benchmarks completed
• Success Criteria:
  • Circuit depth optimized for target hardware
  • Error rate reduced by QEC (target: 10x reduction)
  • Algorithm achieves expected speedup (for suitable problems)
  • Results reproducible across multiple runs

Skill Composition

• Depends on: high-performance-computing-hpc
• Compatible with: post-quantum-cryptography, neuromorphic-computing
• Conflicts with: None
• Related Skills: model-registry-versioning

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Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions / Constraints / Non-goals

• Assumptions:
  • Development environment is properly configured
  • Required dependencies are available
  • Team has basic understanding of domain
• Constraints:
  • Must follow existing codebase conventions
  • Time and resource limitations
  • Compatibility requirements
• Non-goals:
  • This skill does not cover edge cases outside scope
  • Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:
  • Python 3.8+
  • Node.js 16+
  • Modern browsers (Chrome, Firefox, Safari, Edge)
• Required AI Tools:
  • Code editor (VS Code recommended)
  • Testing framework appropriate for language
  • Version control (Git)
• Dependencies:
  • Language-specific package manager
  • Build tools
  • Testing libraries
• Environment Setup:
  • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/​Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/​pagination for large data
• Resource Cleanup: Close DB connections/​file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/​Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/​Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/​clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/​Typecheck passed
• Logging/​Metrics/​Trace implemented
• Security checks passed
• Documentation/​Changelog updated
• Accessibility/​Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries
• ⚠️ Watch out for: Common symptoms and quick fixes
• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure