Software Engineer and Software Team Lead

Bosonic Quantum Devices Ltd., Calgary, AB

• Build quantum-software infrastructure for distributed quantum computing, including SDK components and classical simulators designed around modular quantum architectures, in Python and Rust.
• Implemented a hypergraph-partitioning compiler pass that partitions logical circuits for distributed execution, reducing executable operations by up to 81% and remote operations by up to 90% versus baseline partitioners across 200+ OpenQASM benchmark circuits (QFT, quantum volume, QuGAN, adders, multipliers, Bernstein–Vazirani, W-state).
• Led a team of 3 software interns delivering company software projects; set unit-testing standards, CI/CD pipelines, and pull-request code review practices to keep code modular, tested, and maintainable.
• Translate architecture, research, and product requirements into extensible APIs, software tools, and implementation plans with technical and non-technical collaborators.
• Lead community engagement: workshop design and delivery, technical education, and intern conversion initiatives that strengthen the company's talent and developer pipeline.

Proprietary work; technical details and source code are not publicly available.

Business Analyst Intern

Bosonic Quantum Devices Ltd., Calgary, AB

• Supported a deep-tech quantum startup across technical strategy, market research, software initiatives, investor communications, and organizational execution.
• Created investor-facing technical and business materials that supported more than $1M in investment raised.
• Produced competitor analyses and market breakdowns that informed positioning, strategic priorities, and evidence-based decisions.
• Converted technical workshops into internship opportunities by connecting education and community activity with hiring needs.
• Explained complex quantum computing concepts to technical, business, and investor audiences through written materials, presentations, and research synthesis.

Adaptive Quantum Circuit Simulator

ML-guided simulator selection

• Implemented and optimized four classical quantum circuit simulators (statevector, p-block, MPS, stabilizer) from their published research papers, each suited to a different class of circuits.
• Built a decision-tree machine learning model that predicts the best simulator from circuit features and dispatches simulation automatically; benchmarked against Qiskit Aer across circuit families, achieving a median runtime improvement of roughly 88%.

Interactive Quantum Computing Tutorial Series

Distributed quantum computing

• Authored interactive Jupyter notebooks, styled with a marimo extension, covering the full quantum computing pipeline, including simple models of how monolithic and distributed architectures scale toward useful qubit counts.
• Wrote a notebook on hypergraph partitioning, translating a research-level algorithm into plain-language explanations; presented the workshops live to a class of master's students, which converted to three internships.

IBM Flood Risk Quantum Challenge

• Developed a hybrid quantum–classical machine learning workflow to estimate flash flood probability, and investigated quantum-kernel-based feature selection.

Mphasis & COPA Airlines Quantum Computing Challenge

• Developed a quantum-optimization approach for rescheduling cancelled flights and reassigning passengers using D-Wave quantum annealing.
• Built an interactive application with React.js, FastAPI, and Uvicorn to visualize flight metrics, rescheduling outcomes, and comparative analyses for decision makers.
• Authored a technical white paper documenting the methodology, implementation, system design, and future development considerations.