Bram Evert
Lead Research Scientist, Quantum Advantage at Rigetti Computing
Lisbon, Portugal
Summary
🤩
Rockstar🎓
Top SchoolBram Evert is a physicist-turned-quantum data scientist with 11 years of experience building and benchmarking algorithms on experimental quantum hardware, currently leading Quantum Advantage research at Rigetti from Lisbon. He blends deep qubit-physics expertise with strong software engineering—contributing to core open-source tooling like rigetti/pyquil—to implement error mitigation, calibration and compilation pipelines that turn noisy devices into useful experimental platforms. His work spans hands-on processor calibration at D-Wave, machine-learning-driven signal analysis for wearables, and development of reproducible Python libraries for benchmarking and mitigation. Bram is particularly skilled at closing the loop between hardware feedback and algorithm design, using experiments both as demonstrations of quantum utility and as quantitative guides for hardware improvement.
11 years of coding experience
6 years of employment as a software developer
Bachelor of Engineering (B.Eng.), Engineering Physics, Bachelor of Engineering (B.Eng.), Engineering Physics at McMaster UniversityMaster of Science (M.Sc.), Physics, Master of Science (M.Sc.), Physics at McGill University
Github Skills (3)
quantum-computing10
numpy10
python10
Programming languages (6)
Github contributions (5)
rigetti/pyquil
Oct 2021 - Feb 2022
A Python library for quantum programming using Quil.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 reviews, 6 commits, 11 PRs in 4 months
Contributions summary:Bram primarily focused on modifying the `pyquil` library, specifically related to quantum waveform generation and simulation. Their contributions involved fixing bugs in the `quiltwaveforms.py` file, updating the application of phase in waveforms, and correcting the padding of waveforms. Additionally, the user updated the `matrices.py` file by moving native gates to the top of the QUANTUM_GATES dictionary. These changes indicate a focus on refining the core functionality and accuracy of quantum computing operations.
python-librarypythonquantum-computingquantumquantum-programming
bramathon/pyquil
Nov 2021 - Sep 2023
A Python library for quantum programming using Quil.
Contributions:48 pushes, 6 branches in 1 year 10 months
python-librarypythonquantum-computingquantumquantum-programming
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