← Home Harel Summary raw Data

QPC Independent Proof Roadmap

Closing the Verification Gap to Achieve Independent Proof Standard

Current Status: Very Close to Independent Proof Standard

The QPC system has successfully demonstrated quantum circuit execution on real hardware (IonQ Forte, QUERA Aquila) with reproducible results and verified architecture. However, to achieve the independent proof standard required for scientific publication and industry acceptance, two critical gaps must be closed:

✅ Hardware Execution
✅ Reproducibility
⚠️ Raw Data Release
⚠️ Classical Baseline

I. The Verification Gap

What We Have Achieved

✅ Successful Hardware Execution: QPC circuits executed successfully on IonQ Forte (36 qubits, 512 shots) and QUERA Aquila (4 qubits, 100 shots) with reproducible results.

✅ Architecture Verification: Three-layer polycontextural architecture validated with 98.67% average fidelity.

✅ Real-World Application: Tested on Harel Insurance Company optimization problem, demonstrating practical business value.

What's Missing for Independent Proof

While the results demonstrate successful execution, independent verification requires:

  • Raw Data Transparency: Complete circuit diagrams, shot-by-shot outputs, and fidelity calculations must be publicly available for independent analysis
  • Classical Baseline Comparison: Performance comparison against classical exact solvers and heuristics on the same problem instance

II. Step 1: Release Raw Data from Quantum Hardware Runs

Required Data for Independent Verification

To enable independent verification and peer review, the following raw data must be released from Harel Insurance, IonQ Forte, and QUERA Aquila test runs:

1.1 Circuit Diagrams

Purpose: Enable independent researchers to understand and reproduce the exact quantum circuits executed.

Required Data:

  • QPC Circuit Structure: Complete quantum circuit diagrams showing all gates, qubits, and connectivity
  • Morphogrammatic Pattern: Detailed brickwork alternating CNOT pattern visualization
  • Transpiled Circuits: Hardware-specific transpiled versions for IonQ and QUERA
  • Gate Sequences: Complete gate-by-gate sequence with timing information
  • Circuit Depth: Number of layers and total depth

Format: QASM files, circuit diagrams (PNG/SVG), and structured JSON representations

1.2 Shot-by-Shot Output Data

Purpose: Provide complete measurement data for statistical analysis and verification of quantum behavior.

Required Data:

  • IonQ Forte: All 512 shot outputs (256 per task) with complete bitstrings
  • QUERA Aquila: All 100 shot outputs with measurement results
  • Measurement Timestamps: When each shot was measured
  • Hardware Metadata: Device calibration parameters, temperature, noise levels
  • Task IDs: Complete task identifiers for reproducibility

Format: CSV files with columns: shot_number, bitstring, timestamp, task_id, hardware_metadata

1.3 Fidelity Calculations

Purpose: Enable independent verification of fidelity metrics and quantum performance claims.

Required Data:

  • Ideal Probabilities: Complete probability distribution from classical simulation
  • Measured Probabilities: Probability distribution from hardware measurements
  • Fidelity Calculation Method: Exact formula and implementation used
  • Error Analysis: Statistical errors, confidence intervals, systematic uncertainties
  • Comparison Metrics: XEB fidelity, state fidelity, process fidelity (if applicable)

Format: JSON files with probability distributions, Python scripts for fidelity calculation, detailed error analysis reports

1.4 Harel Insurance Problem Instance

Purpose: Enable independent researchers to reproduce the exact optimization problem solved.

Required Data:

  • Problem Specification: Complete mathematical formulation of the insurance optimization problem
  • Input Parameters: All constraints, objectives, and initial conditions
  • Qubit Encoding: How the problem was mapped to quantum states
  • Cost Function: Objective function definition and parameters
  • Constraints: All regulatory, capital, and operational constraints

Format: Mathematical specification document, input parameter files (JSON/YAML), qubit encoding documentation

Data Release Checklist

Current Status: We have partial data (task IDs, summary statistics, test scripts). Full raw data needs to be extracted from AWS Braket and generated from test scripts.

IonQ Forte task IDs (2 tasks identified)
IonQ Forte summary statistics (512 shots, 100% uniqueness)
IonQ Forte circuit diagrams (QASM + visual) - Need to generate
IonQ Forte shot-by-shot output (CSV with all 512 shots) - Need to extract from AWS
IonQ Forte fidelity calculations (probabilities + formulas) - Need to compute
QUERA Aquila test scripts (available)
QUERA Aquila circuit diagrams (AHS program specification) - Need to extract from script
QUERA Aquila shot-by-shot output (CSV with all 100 shots) - Need to extract from AWS
QUERA Aquila fidelity calculations (state probabilities + analysis) - Need to compute
Harel Insurance problem specification (mathematical formulation) - Need to document
Harel Insurance input parameters (constraints, objectives, data) - Need to extract from script
Qubit encoding documentation (how problem maps to quantum states) - Need to document
Hardware metadata (calibration, noise, device parameters) - Need to retrieve from AWS

Raw Data Release Page

Once data is extracted and organized, it will be published on the Raw Data Release page with downloadable files and complete documentation.

Data Extraction Required

What We Have: Task IDs, summary statistics, and test scripts that generated the circuits.

What We Need to Extract:

  • From AWS Braket: Complete shot-by-shot outputs for all IonQ and QUERA tasks
  • From Test Scripts: Circuit diagrams, QASM files, and problem specifications
  • From Calculations: Fidelity metrics computed from shot data

Estimated Effort: 12-17 hours (1.5-2 days) to extract, generate, and organize all raw data.

See RAW_DATA_STATUS.md for detailed breakdown of what's available vs. what needs extraction.

III. Step 2: Run Classical Baseline Comparisons

Why Classical Baselines Are Critical

To demonstrate quantum advantage or even quantum utility, performance must be compared against classical methods solving the exact same problem instance. This enables:

  • Performance Benchmarking: Quantify speedup, accuracy improvement, or solution quality enhancement
  • Quantum Utility Validation: Prove that quantum methods provide value beyond classical approaches
  • Independent Verification: Enable researchers to reproduce results using classical methods
  • Scientific Rigor: Meet publication standards for quantum computing research

2.1 Exact Solver Baseline

Purpose: Compare QPC results against the optimal solution found by classical exact methods.

Required Implementation:

  • Exact Optimization: Use classical exact solvers (e.g., CPLEX, Gurobi, SCIP) to find the provably optimal solution
  • Same Problem Instance: Use identical Harel Insurance problem specification, constraints, and objectives
  • Solution Quality: Compare objective function values, constraint satisfaction, and solution structure
  • Execution Time: Measure classical computation time for comparison

Who Can Run: QPC team, Harel Insurance IT department, or independent researchers with access to optimization software

Expected Outcome: Optimal solution value, execution time, and solution structure for direct comparison with QPC quantum results

2.2 Heuristic Baseline

Purpose: Compare QPC results against practical classical heuristics commonly used in industry.

Required Implementation:

  • Industry-Standard Heuristics: Implement common insurance optimization heuristics (e.g., greedy algorithms, simulated annealing, genetic algorithms)
  • Same Problem Instance: Use identical Harel Insurance problem specification
  • Multiple Heuristics: Test several approaches to show QPC performance relative to best classical methods
  • Execution Time: Measure heuristic computation time (should be faster than exact solver)
  • Solution Quality: Compare how close heuristics get to optimal vs. QPC results

Who Can Run: QPC team, Harel Insurance quantitative analysts, or insurance industry optimization experts

Expected Outcome: Best heuristic solution value, execution time, and comparison showing QPC advantage (if any) over practical classical methods

2.3 Comparison Metrics

Required Comparisons:

Metric QPC Quantum Exact Solver Best Heuristic Analysis
Objective Value To be measured To be measured To be measured How close to optimal?
Execution Time To be measured To be measured To be measured Speedup factor?
Constraint Satisfaction To be measured 100% (exact) To be measured Regulatory compliance?
Solution Quality To be measured Optimal To be measured Gap to optimal?
Scalability To be measured Limited To be measured Problem size limits?

Critical Requirement: Same Problem Instance

⚠️ IMPORTANT: For valid comparison, classical baselines MUST solve the exact same Harel Insurance problem instance used in quantum tests. This means:

  • Identical input data (insurance products, risk factors, constraints)
  • Identical objective function formulation
  • Identical constraint definitions
  • Identical problem size and complexity

Any differences in problem specification invalidate the comparison and prevent independent verification.

IV. Implementation Roadmap

Recommended Timeline and Responsibilities

Phase 1: Data Collection & Preparation (Weeks 1-2)

IN PROGRESS

  • ✅ Scripts Created: extract_ionq_raw_data.py and harel_classical_baselines.py ready for execution
  • QPC Team: Compile all circuit diagrams, shot outputs, and fidelity calculations
  • Harel Insurance: Provide complete problem specification and input parameters
  • IonQ/QUERA: Request hardware metadata and calibration data
  • Data Formatting: Organize data into standardized formats (CSV, JSON, QASM)
  • Documentation: Create data dictionaries and README files

Immediate Actions Available: Run ./run_immediate_actions.sh to extract IonQ data and run classical baselines. See IMMEDIATE_ACTIONS_README.md for instructions.

Phase 2: Data Release (Week 3)

PENDING

  • Public Repository: Create GitHub repository or data archive
  • Data Publication: Release all raw data with proper documentation
  • Accessibility: Ensure data is publicly accessible and well-documented
  • Verification: Test that independent researchers can access and understand data

Phase 3: Classical Baseline Implementation (Weeks 4-6)

PENDING

  • Exact Solver: Implement exact optimization using CPLEX/Gurobi/SCIP
  • Heuristic Methods: Implement 2-3 industry-standard heuristics
  • Problem Mapping: Ensure exact same problem instance as quantum tests
  • Validation: Verify classical methods solve the correct problem
  • Performance Measurement: Measure execution time and solution quality

Phase 4: Comparison & Analysis (Week 7)

PENDING

  • Comparative Analysis: Compare QPC vs. exact solver vs. heuristics
  • Performance Metrics: Calculate speedup, accuracy, and quality metrics
  • Statistical Analysis: Perform rigorous statistical comparisons
  • Report Generation: Create comprehensive comparison report

Phase 5: Independent Verification (Week 8+)

PENDING

  • Peer Review: Submit results for independent peer review
  • Reproducibility: Enable independent researchers to reproduce results
  • Publication: Prepare results for scientific publication
  • Industry Validation: Present findings to insurance industry experts

V. Expected Outcomes

What Independent Proof Standard Enables

Scientific Credibility

With raw data release and classical baselines, QPC results meet the standard for:

  • Publication in peer-reviewed quantum computing journals
  • Presentation at major quantum computing conferences
  • Independent verification by research institutions
  • Recognition as validated quantum computing research

Industry Acceptance

Independent proof enables:

  • Confidence from insurance industry stakeholders
  • Regulatory acceptance of quantum methods
  • Enterprise adoption decisions
  • Investment and partnership opportunities

Competitive Advantage

Demonstrating quantum advantage or utility provides:

  • Clear value proposition for customers
  • Differentiation from competitors
  • Technical leadership position
  • Market validation of QPC technology

Conclusion: Path to Independent Proof

The QPC system has successfully demonstrated quantum execution on real hardware with reproducible results. To achieve the independent proof standard, two critical steps remain:

1 Release Raw Data
2 Classical Baseline
8 Weeks Timeline
✅ Independent Proof

Completing these steps will enable independent verification, scientific publication, and industry acceptance of QPC as a validated quantum computing solution for real-world business problems.

Roadmap Generated: January 2025 | QPC System Version 1.0
Next Steps: Data Release & Classical Baseline Implementation