About
Building software at the intersection of quantum mechanics and engineering
David Coldeira
Scientific Software Engineer specializing in quantum computing and physics-informed software design.
I build production-quality tools for complex physical systems—combining deep physics knowledge with rigorous software engineering to create solutions that actually work.
What I Do
Quantum Computing Research
Building educational tools and frameworks that make quantum mechanics accessible while maintaining physical rigor.
Current Projects: - Quantum Process Language (QPL): Relations-first quantum programming where entanglement is first-class - Quantum Advisor: Honest evaluation tool for quantum computational advantage - Photon Duality Simulator: Novel theoretical framework for quantum interference
Scientific Software Engineering
Production systems for complex scientific domains, applying rigorous engineering principles to real-world physics problems.
Day Job - Laboratory Software Engineering: - Lead developer for GQMLab LIMS at Geoquip Marine - Processing offshore seabed instrument data (GDS, JVtech) - ML pipelines predicting soil mechanics from field measurements - Full-stack: Python, Flutter, Docker, PostgreSQL
Night Job - Quantum Computing Research: - QPL: Relations-first quantum programming language - Quantum Advisor: Honest evaluation of quantum advantage - Educational tools with production-quality standards
Cross-Domain Insight: Both domains require physics-informed design, rigorous correctness, and honest evaluation over hype. The same principles that make geotechnical predictions reliable apply to quantum software.
Philosophy
Honest Evaluation Over Hype
Most quantum computing content is hype. I provide critical, evidence-based analysis of when quantum computing actually helps (and when it doesn’t).
If something doesn’t work, I say so. If classical methods are better, I recommend them. Honesty matters more than excitement.
Physics-Informed Design
Domain knowledge drives architecture. Understanding quantum mechanics shapes how QPL handles entanglement. Understanding soil mechanics influences geotechnical data pipelines.
The best software for physical systems comes from understanding both the physics and the engineering.
Production-Ready Research Code
Research code shouldn’t be throwaway. Build with: - Comprehensive testing (QPL: 100% test pass rate) - Clear documentation - Maintainability from day one - Real-world applicability
Rigorous Correctness
Get the math right. Quantum mechanics is unforgiving: - Test assumptions - Verify against known physics - Fix bugs thoroughly (see: QPL cross-basis measurement bug fix)
Background
Education: Physics BSc → Self-taught software engineering → Production systems
Technical Domains: - Quantum Computing: Python, NumPy, Qiskit, quantum state simulation, tensor networks - Scientific Software: Python (FastAPI, Pandas, Scikit-learn), PostgreSQL, Docker, Kubernetes - Full-Stack: Flutter, modern web frameworks, REST APIs, ML pipelines
What This Blog Offers
Deep Technical Analysis
Expert-level quantum computing and scientific software insights. Not tutorials—assumes you know programming fundamentals and focuses on the “why” behind architectural decisions.
Honest Quantum Evaluation
Critical assessment of quantum computing claims. When does quantum actually help? When are classical methods better? Evidence-based answers with peer-reviewed citations.
Physics-Informed Design Patterns
How domain knowledge shapes software architecture. Patterns that work across domains—from quantum computing to geotechnical engineering.
Real-World Experience
Lessons from building production scientific software. What actually works? What doesn’t? What matters?
Current Focus
Stage 1: n-Qubit Relations (Completed)
QPL now supports arbitrary n-qubit entanglement: - GHZ states up to 5 qubits tested - General tensor product operations - Proper measurement and state collapse - 100% test pass rate
Stage 2: Process Algebra (Next)
Building quantum type system and process composition for QPL.
Quantum Advisor Expansion
Growing the knowledge base with more quantum algorithms and problem classes.
Connect
Open to discussions about: - Quantum computing (honest evaluation, not hype) - Scientific software engineering - Physics-informed software design - Collaboration on educational tools or research
Contact: - Email: dcoldeira@gmail.com - GitHub: @dcoldeira - LinkedIn: dcoldeira
Content Philosophy
Rigorous over flashy. Every claim backed by evidence. Honest about limitations. Clear about what works and what doesn’t.
Expert-level, not beginner. Assumes programming fundamentals and physics basics. Focuses on depth, not breadth.
Cross-domain insights. Principles that apply whether you’re building geotechnical software or quantum computing tools.
Production mindset. Research code should be production-quality. Tools should be usable. Claims should be testable.
“The best software for physical systems comes from understanding both the physics and the engineering.”