I'm a physics-background Quantum Computing Developer focused on applied quantum computing: turning quantum computing concepts and algorithms into runnable, testable, reproducible software and experiments.

My current direction combines quantum software, algorithm implementation, simulation, validation, benchmarking, and application-oriented workflows. I work primarily with Python and Qiskit, supported by a broader engineering background in testing, validation, automation, documentation, and reproducible workflow design.

• Qiskit / Python quantum software
• quantum circuit implementation and simulation
• quantum algorithm implementation and reproduction
• result validation and classical-baseline comparison
• testing, debugging, and reproducibility
• benchmark-oriented quantum experiments
• small research, prototype, and proof-of-concept workflows

My working model is:

Problem
  ↓
Classical formulation
  ↓
Quantum mapping
  ↓
Algorithm selection
  ↓
Implementation
  ↓
Simulation / experiment
  ↓
Validation + baseline comparison
  ↓
Benchmark + limitations
  ↓
Reproducible delivery

A small, reproducible Qiskit 2.x workflow for local quantum circuit simulation, validation, testing, and reporting.

Current capabilities include:

• Bell, GHZ, and two-qubit Grover toy workflows
• local simulation with Qiskit Aer
• seeded, shot-based execution
• probability-tolerant result validation
• pytest coverage
• CLI and script entry points
• JSON and Markdown reports
• generated circuit images and public-safe screenshots
• explicit limitations and reproducibility notes

The project is intentionally bounded. It does not claim quantum advantage, production readiness, or hardware performance.

Its purpose is to demonstrate how small quantum experiments can be packaged as inspectable, testable, reproducible engineering workflows.

Repository: qiskit-quantum-workflow-starter

I currently hold 5 IBM Quantum Learning digital badges:

• Basics of Quantum Information
• Fundamentals of Quantum Algorithms
• General Formulation of Quantum Information
• Variational Algorithm Design
• Quantum Business Foundations

I treat these badges as structured learning evidence. My main focus now is converting that foundation into stronger engineering, open-source, benchmarking, and real-project evidence.

My earlier Python engineering work provides the software-engineering foundation for my current quantum computing work, especially in:

• Python project structure
• pytest-based testing
• validation and quality checks
• reproducible workflows
• CLI and local execution
• Docker-based environments
• technical documentation
• reports and handoff-oriented outputs

Selected engineering projects:

• data-quality-etl-starter — Python data validation, ETL, testing, reporting, and reproducible workflows
• fastapi-csv-quality-api — a small FastAPI service with structured validation, pytest coverage, and Docker packaging

These projects are no longer my primary technical direction. They remain public as evidence of broader Python engineering discipline and delivery practices.

My current portfolio is best aligned with bounded quantum software and research-implementation tasks such as:

• Qiskit circuit implementation
• Qiskit notebook or script debugging
• local quantum simulation with Qiskit Aer
• small quantum algorithm implementations from defined methods
• reproducible experiment setup
• result validation and testing
• classical-baseline comparison for small experiments
• technical documentation and experiment reports

I am not positioning myself as a quantum hardware engineer, quantum research scientist, or inventor of new quantum algorithms.

My focus is implementing, validating, engineering, and applying quantum computing methods.

The next stage of this profile is focused on increasing external and applied evidence through:

1. stronger algorithm + benchmark repositories;
2. meaningful open-source quantum contributions;
3. real quantum software, simulation, or research-implementation projects.

Quantum: Qiskit, Qiskit Aer, quantum circuits, quantum algorithms, simulation

Engineering: Python, pytest, Git/GitHub, Docker, reproducible workflows, validation, technical documentation

Scientific foundation: physics, linear algebra, quantum information, scientific computing