# Quantum Computing - Processes Backlog

## Overview

This backlog contains prioritized processes for the Quantum Computing specialization. Each process represents a repeatable workflow that supports quantum algorithm development, hardware integration, error correction, and application deployment.

---

## Process Categories

### Category 1: Quantum Algorithm Development

#### QC-ALGO-001: Quantum Circuit Design and Optimization
**Priority:** P0 - Critical
**Complexity:** High
**Estimated Effort:** 5 days

**Description:**
Design, implement, and optimize quantum circuits for specific computational problems. This process covers circuit construction using quantum gates, depth optimization, gate synthesis, and transpilation to native hardware gate sets.

**Key Activities:**
- Analyze problem requirements and quantum resource needs
- Design circuit architecture and qubit allocation
- Implement using quantum programming frameworks (Qiskit, Cirq, PennyLane)
- Optimize circuit depth and gate count
- Transpile to target hardware native gates
- Validate correctness via simulation

**Outcomes:**
- Optimized quantum circuit implementation
- Resource estimation documentation
- Hardware-ready transpiled circuit
- Performance benchmarks

---

#### QC-ALGO-002: Variational Algorithm Implementation (VQE/QAOA)
**Priority:** P0 - Critical
**Complexity:** High
**Estimated Effort:** 5 days

**Description:**
Implement and tune variational quantum algorithms including VQE (Variational Quantum Eigensolver) and QAOA (Quantum Approximate Optimization Algorithm) for optimization and chemistry applications.

**Key Activities:**
- Define problem Hamiltonian and cost function
- Design parameterized ansatz circuit
- Configure classical optimizer (COBYLA, SPSA, Adam)
- Implement hybrid quantum-classical optimization loop
- Tune hyperparameters and convergence criteria
- Benchmark against classical solutions

**Outcomes:**
- Functioning variational algorithm implementation
- Optimized parameter configurations
- Convergence analysis and results
- Comparison with classical baselines

---

#### QC-ALGO-003: Quantum Algorithm Benchmarking
**Priority:** P1 - High
**Complexity:** Medium
**Estimated Effort:** 3 days

**Description:**
Systematically benchmark quantum algorithms against classical alternatives to quantify speedup, accuracy, and resource requirements across different problem sizes and hardware configurations.

**Key Activities:**
- Define benchmark problem suite and metrics
- Implement classical baseline algorithms
- Execute quantum algorithms on simulators and hardware
- Measure execution time, fidelity, and success probability
- Analyze scaling behavior with problem size
- Document quantum advantage conditions

**Outcomes:**
- Comprehensive benchmark report
- Performance comparison charts
- Quantum advantage analysis
- Hardware suitability recommendations

---

### Category 2: Quantum Error Management

#### QC-ERR-001: Error Mitigation Strategy Implementation
**Priority:** P0 - Critical
**Complexity:** High
**Estimated Effort:** 4 days

**Description:**
Implement and configure error mitigation techniques for NISQ devices including zero-noise extrapolation (ZNE), probabilistic error cancellation (PEC), measurement error mitigation, and dynamical decoupling.

**Key Activities:**
- Characterize hardware noise model
- Select appropriate mitigation techniques
- Implement ZNE with noise scaling methods
- Configure measurement error calibration
- Apply dynamical decoupling sequences
- Validate mitigation effectiveness

**Outcomes:**
- Configured error mitigation pipeline
- Noise characterization report
- Mitigation overhead analysis
- Improved result fidelity metrics

---

#### QC-ERR-002: Quantum Error Correction Code Implementation
**Priority:** P1 - High
**Complexity:** Very High
**Estimated Effort:** 8 days

**Description:**
Implement quantum error correction codes including surface codes, Steane code, and color codes for fault-tolerant quantum computation. Design syndrome extraction circuits and decoders.

**Key Activities:**
- Select appropriate QEC code for application
- Implement logical qubit encoding
- Design syndrome extraction circuits
- Implement classical decoder algorithms
- Simulate error correction performance
- Analyze threshold and overhead requirements

**Outcomes:**
- QEC code implementation
- Decoder implementation
- Threshold analysis documentation
- Resource overhead estimates

---

#### QC-ERR-003: Hardware Noise Characterization
**Priority:** P1 - High
**Complexity:** Medium
**Estimated Effort:** 3 days

**Description:**
Systematically characterize quantum hardware noise including gate errors, readout errors, crosstalk, and decoherence rates using randomized benchmarking and tomography techniques.

**Key Activities:**
- Run randomized benchmarking protocols
- Perform gate set tomography
- Measure T1 and T2 coherence times
- Characterize readout error matrices
- Identify crosstalk patterns
- Build noise model for simulation

**Outcomes:**
- Complete noise characterization report
- Calibrated noise model
- Qubit quality rankings
- Hardware optimization recommendations

---

### Category 3: Quantum Chemistry and Simulation

#### QC-CHEM-001: Molecular Ground State Energy Calculation
**Priority:** P1 - High
**Complexity:** High
**Estimated Effort:** 5 days

**Description:**
Calculate molecular ground state energies using quantum algorithms (VQE, QPE) for chemistry applications including drug discovery and materials design.

**Key Activities:**
- Construct molecular Hamiltonian
- Select qubit encoding (Jordan-Wigner, Bravyi-Kitaev)
- Design chemistry-aware ansatz (UCCSD, ADAPT-VQE)
- Execute VQE optimization
- Compare with classical chemistry methods
- Analyze chemical accuracy

**Outcomes:**
- Ground state energy calculations
- Molecular orbital analysis
- Accuracy comparison with classical methods
- Chemical property predictions

---

#### QC-CHEM-002: Hamiltonian Simulation Implementation
**Priority:** P1 - High
**Complexity:** High
**Estimated Effort:** 4 days

**Description:**
Implement Hamiltonian simulation for quantum many-body systems using Trotter-Suzuki decomposition, quantum signal processing, or qubitization techniques.

**Key Activities:**
- Decompose system Hamiltonian into terms
- Select simulation method (product formulas, QSP)
- Implement time evolution circuits
- Optimize Trotter step count
- Validate against exact solutions
- Analyze simulation error bounds

**Outcomes:**
- Hamiltonian simulation implementation
- Error analysis documentation
- Resource requirement estimates
- Time evolution results

---

### Category 4: Quantum Machine Learning

#### QC-ML-001: Quantum Classifier Implementation
**Priority:** P1 - High
**Complexity:** Medium
**Estimated Effort:** 4 days

**Description:**
Implement quantum machine learning classifiers including variational quantum classifiers, quantum kernel methods, and quantum support vector machines.

**Key Activities:**
- Design data encoding circuit
- Implement parameterized classification ansatz
- Configure training loop with classical optimizer
- Implement quantum kernel computation
- Train and validate on datasets
- Compare with classical ML baselines

**Outcomes:**
- Trained quantum classifier
- Classification accuracy metrics
- Training convergence analysis
- Comparison with classical methods

---

#### QC-ML-002: Quantum Neural Network Training
**Priority:** P2 - Medium
**Complexity:** High
**Estimated Effort:** 5 days

**Description:**
Design and train quantum neural networks (QNNs) for machine learning tasks, addressing challenges like barren plateaus and optimizing training strategies.

**Key Activities:**
- Design QNN architecture
- Implement parameter initialization strategies
- Configure gradient computation (parameter shift, adjoint)
- Implement barren plateau mitigation techniques
- Train on target datasets
- Analyze expressibility and trainability

**Outcomes:**
- Trained quantum neural network
- Gradient landscape analysis
- Training strategy recommendations
- Performance benchmarks

---

### Category 5: Quantum Software Engineering

#### QC-SW-001: Quantum Circuit Testing Framework
**Priority:** P0 - Critical
**Complexity:** Medium
**Estimated Effort:** 4 days

**Description:**
Establish testing frameworks for quantum circuits including unit tests, integration tests, property-based testing, and simulation-based validation.

**Key Activities:**
- Design quantum circuit test architecture
- Implement unit tests for circuit components
- Create integration tests for full algorithms
- Implement property-based tests
- Configure CI/CD pipeline for quantum code
- Establish test coverage metrics

**Outcomes:**
- Quantum testing framework
- Test suite for circuits
- CI/CD integration
- Test coverage report

---

#### QC-SW-002: Quantum-Classical Hybrid System Integration
**Priority:** P1 - High
**Complexity:** High
**Estimated Effort:** 5 days

**Description:**
Design and implement hybrid quantum-classical systems including job submission, result processing, and iterative optimization workflows.

**Key Activities:**
- Design hybrid system architecture
- Implement quantum job submission pipeline
- Build result aggregation and post-processing
- Integrate with classical optimization frameworks
- Implement retry and error handling
- Configure resource management

**Outcomes:**
- Hybrid system implementation
- Job management pipeline
- Integration documentation
- Performance optimization guide

---

#### QC-SW-003: Quantum SDK/Library Development
**Priority:** P2 - Medium
**Complexity:** High
**Estimated Effort:** 6 days

**Description:**
Develop reusable quantum software libraries and SDKs including circuit primitives, algorithm templates, and hardware abstraction layers.

**Key Activities:**
- Design library architecture and APIs
- Implement circuit building blocks
- Create algorithm templates
- Build hardware abstraction layer
- Write documentation and examples
- Implement multi-platform support

**Outcomes:**
- Quantum SDK/library
- API documentation
- Example notebooks
- Platform compatibility matrix

---

### Category 6: Quantum Hardware Integration

#### QC-HW-001: Hardware Backend Configuration
**Priority:** P1 - High
**Complexity:** Medium
**Estimated Effort:** 3 days

**Description:**
Configure and optimize quantum circuits for specific hardware backends including qubit mapping, routing, and native gate transpilation.

**Key Activities:**
- Analyze hardware topology and connectivity
- Configure qubit mapping strategy
- Implement SWAP insertion for routing
- Optimize for native gate set
- Configure noise-aware transpilation
- Validate hardware execution

**Outcomes:**
- Hardware-optimized circuits
- Backend configuration profiles
- Routing optimization report
- Execution success metrics

---

#### QC-HW-002: Multi-Platform Deployment
**Priority:** P2 - Medium
**Complexity:** High
**Estimated Effort:** 5 days

**Description:**
Deploy quantum applications across multiple hardware platforms (IBM, Google, IonQ, etc.) with platform-specific optimizations and unified interfaces.

**Key Activities:**
- Implement platform abstraction layer
- Configure platform-specific transpilation
- Build unified job submission interface
- Implement result normalization
- Compare cross-platform performance
- Document platform differences

**Outcomes:**
- Multi-platform deployment pipeline
- Platform comparison report
- Unified execution interface
- Best practices documentation

---

### Category 7: Quantum Cryptography and Security

#### QC-SEC-001: Post-Quantum Cryptography Assessment
**Priority:** P1 - High
**Complexity:** Medium
**Estimated Effort:** 4 days

**Description:**
Assess and implement post-quantum cryptographic algorithms to ensure security against quantum attacks, including migration planning from vulnerable algorithms.

**Key Activities:**
- Inventory current cryptographic usage
- Assess quantum vulnerability of algorithms
- Evaluate NIST PQC candidates
- Design migration strategy
- Implement PQC algorithms
- Test cryptographic implementations

**Outcomes:**
- Cryptographic vulnerability assessment
- PQC migration plan
- Implementation guidelines
- Security validation report

---

#### QC-SEC-002: Quantum Random Number Generation
**Priority:** P2 - Medium
**Complexity:** Medium
**Estimated Effort:** 3 days

**Description:**
Implement quantum random number generation (QRNG) for cryptographic applications leveraging quantum measurement randomness.

**Key Activities:**
- Design QRNG circuit
- Implement randomness extraction
- Configure statistical testing (NIST suite)
- Validate randomness quality
- Integrate with cryptographic applications
- Document security properties

**Outcomes:**
- QRNG implementation
- Statistical test results
- Integration guidelines
- Security certification documentation

---

### Category 8: Quantum Application Development

#### QC-APP-001: Quantum Optimization Application
**Priority:** P1 - High
**Complexity:** High
**Estimated Effort:** 5 days

**Description:**
Develop quantum optimization applications for combinatorial problems including portfolio optimization, vehicle routing, and scheduling using QAOA or quantum annealing.

**Key Activities:**
- Formulate problem as QUBO/Ising model
- Implement problem encoding
- Design QAOA circuit or annealing schedule
- Configure classical post-processing
- Benchmark against classical solvers
- Package as deployable application

**Outcomes:**
- Optimization application
- Problem formulation library
- Benchmark comparison report
- Deployment documentation

---

#### QC-APP-002: Quantum Finance Application
**Priority:** P2 - Medium
**Complexity:** High
**Estimated Effort:** 5 days

**Description:**
Develop quantum computing applications for financial services including option pricing, risk analysis, and portfolio optimization using quantum Monte Carlo and amplitude estimation.

**Key Activities:**
- Model financial instruments quantumly
- Implement amplitude estimation circuits
- Design quantum Monte Carlo sampling
- Integrate with financial data sources
- Validate against classical pricing models
- Document regulatory compliance

**Outcomes:**
- Quantum finance application
- Pricing model implementation
- Validation report
- Compliance documentation

---

### Category 9: Quantum Research and Documentation

#### QC-DOC-001: Quantum Algorithm Documentation
**Priority:** P1 - High
**Complexity:** Medium
**Estimated Effort:** 3 days

**Description:**
Create comprehensive documentation for quantum algorithms including mathematical foundations, circuit diagrams, complexity analysis, and implementation guides.

**Key Activities:**
- Document algorithm mathematical basis
- Create circuit diagrams and visualizations
- Write complexity analysis
- Develop implementation tutorials
- Create API documentation
- Build example notebooks

**Outcomes:**
- Algorithm documentation
- Tutorial notebooks
- API reference
- Visual circuit guides

---

#### QC-DOC-002: Quantum Resource Estimation
**Priority:** P1 - High
**Complexity:** Medium
**Estimated Effort:** 3 days

**Description:**
Estimate quantum resource requirements including qubit count, circuit depth, gate count, and error correction overhead for target applications.

**Key Activities:**
- Analyze algorithm resource requirements
- Count logical qubits and operations
- Estimate circuit depth
- Calculate error correction overhead
- Project hardware requirements
- Compare with hardware roadmaps

**Outcomes:**
- Resource estimation report
- Hardware requirement projections
- Feasibility analysis
- Timeline recommendations

---

### Category 10: Quantum Education and Training

#### QC-EDU-001: Quantum Computing Training Program
**Priority:** P2 - Medium
**Complexity:** Medium
**Estimated Effort:** 5 days

**Description:**
Develop and deliver quantum computing training programs for developers, scientists, and business stakeholders at various skill levels.

**Key Activities:**
- Design curriculum by skill level
- Create hands-on lab exercises
- Develop presentation materials
- Build assessment framework
- Implement interactive tutorials
- Establish certification criteria

**Outcomes:**
- Training curriculum
- Lab exercises and solutions
- Assessment materials
- Certification program

---

## Summary

| Category | Process Count | Critical (P0) | High (P1) | Medium (P2) |
|----------|---------------|---------------|-----------|-------------|
| Algorithm Development | 3 | 2 | 1 | 0 |
| Error Management | 3 | 1 | 2 | 0 |
| Chemistry and Simulation | 2 | 0 | 2 | 0 |
| Machine Learning | 2 | 0 | 1 | 1 |
| Software Engineering | 3 | 1 | 1 | 1 |
| Hardware Integration | 2 | 0 | 1 | 1 |
| Cryptography and Security | 2 | 0 | 1 | 1 |
| Application Development | 2 | 0 | 1 | 1 |
| Research and Documentation | 2 | 0 | 2 | 0 |
| Education and Training | 1 | 0 | 0 | 1 |
| **Total** | **22** | **4** | **12** | **6** |

## Process Dependencies

```
QC-ALGO-001 (Circuit Design)
    └── QC-ALGO-002 (Variational Algorithms)
    └── QC-SW-001 (Testing Framework)
    └── QC-HW-001 (Hardware Configuration)

QC-ERR-003 (Noise Characterization)
    └── QC-ERR-001 (Error Mitigation)
    └── QC-ERR-002 (Error Correction)

QC-ALGO-002 (Variational Algorithms)
    └── QC-CHEM-001 (Molecular Energy)
    └── QC-ML-001 (Quantum Classifier)
    └── QC-APP-001 (Optimization Application)

QC-SW-002 (Hybrid Integration)
    └── QC-HW-002 (Multi-Platform Deployment)
    └── QC-APP-002 (Finance Application)
```

## Implementation Phases

### Phase 1: Foundation (Weeks 1-4)
- QC-ALGO-001: Quantum Circuit Design and Optimization
- QC-ERR-001: Error Mitigation Strategy Implementation
- QC-SW-001: Quantum Circuit Testing Framework
- QC-ERR-003: Hardware Noise Characterization

### Phase 2: Core Algorithms (Weeks 5-8)
- QC-ALGO-002: Variational Algorithm Implementation
- QC-ALGO-003: Quantum Algorithm Benchmarking
- QC-CHEM-001: Molecular Ground State Energy Calculation
- QC-HW-001: Hardware Backend Configuration

### Phase 3: Applications (Weeks 9-12)
- QC-ML-001: Quantum Classifier Implementation
- QC-APP-001: Quantum Optimization Application
- QC-SEC-001: Post-Quantum Cryptography Assessment
- QC-SW-002: Quantum-Classical Hybrid System Integration

### Phase 4: Advanced Features (Weeks 13-16)
- QC-ERR-002: Quantum Error Correction Code Implementation
- QC-CHEM-002: Hamiltonian Simulation Implementation
- QC-ML-002: Quantum Neural Network Training
- QC-HW-002: Multi-Platform Deployment

### Phase 5: Documentation and Training (Weeks 17-20)
- QC-DOC-001: Quantum Algorithm Documentation
- QC-DOC-002: Quantum Resource Estimation
- QC-EDU-001: Quantum Computing Training Program
- QC-SW-003: Quantum SDK/Library Development
- QC-APP-002: Quantum Finance Application
- QC-SEC-002: Quantum Random Number Generation

---

## Version History

| Version | Date | Author | Changes |
|---------|------|--------|---------|
| 1.0 | 2026-01-23 | Babysitter AI | Initial backlog creation |
