Quantum Physics Exact-Diagonalization Specialist

About OpenTrain

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About AI Training Work

AI training is the human side of building artificial intelligence. Researchers and specialists create difficult examples, assess model outputs, and provide detailed feedback so AI systems can reason more accurately in demanding technical fields.

This project applies advanced physics knowledge to benchmark analysis. Your evaluations and written explanations can help measure and improve how AI systems solve complex quantum physics problems.

• Remote, contractor-based project
• Approximately 10 hours per week
• Expected project window of 8 to 10 weeks
• Prior AI training experience is not required

The Role

OpenTrain is seeking a Condensed Matter and Quantum Information Physics Expert for a research-level physics benchmark. You will work with the PXP model, Rydberg blockade, quantum many-body scars, and constrained quantum dynamics.

Depending on your methods expertise and subfield alignment, contributions may involve solver, auditor, or adjudicator work. The role combines computational physics, mathematical reasoning, numerical analysis, and careful written evaluation.

• Work on benchmark problems involving quantum many-body and condensed matter physics
• Support evaluation and improvement of AI systems
• Provide rigorous technical reasoning and written feedback

What You'll Do

You will develop and analyze exact-diagonalization implementations for complex quantum systems. The work includes using translation and reflection symmetries, constructing block-diagonalized subspaces, and optimizing computational efficiency and numerical fidelity across large Hilbert spaces.

You will also evaluate overlaps with Z2 states, interpret the resulting physical insights, and prepare analytic reports and assessments for physics benchmarks. QuSpin experience is preferred where appropriate, particularly for systems of at least L = 26.

• Develop exact-diagonalization implementations using translation and reflection symmetries
• Use QuSpin where appropriate for systems with sizes of at least L = 26
• Construct and manipulate block-diagonalized Hilbert-space subspaces
• Improve computational efficiency and numerical fidelity
• Analyze overlaps with Z2 or related quantum states
• Interpret numerical results and physical implications
• Produce detailed feedback, analytic reports, and benchmark assessments

Requirements

Applicants should have advanced academic training or equivalent practical experience in condensed matter physics, quantum information, or a closely related discipline. The listing identifies the experience level as entry level, but the technical requirements call for substantial specialist knowledge in the methods and physics described below.

Experience with Python, SymPy, Jupyter, or LaTeX can support the computational and reporting work.

• Exact diagonalization using translation and reflection symmetries
• Hamiltonian block-diagonalization and efficient work with large Hilbert-space subspaces
• Strong understanding of quantum many-body scars, Rydberg blockade, and constrained dynamics
• Experience analyzing overlaps with Z2 or related quantum states
• Ability to interpret numerical results and physical insights
• Advanced academic or equivalent background in condensed matter physics or quantum information
• English language proficiency

Project Details And Compensation

This is a remote, part-time contractor engagement available in the countries specified in the application listing. The expected commitment is less than 20 hours per week, approximately 10 hours per week, across an 8 to 10 week project window.

The role description lists compensation at $80 to $160 per hour, while the structured listing fields show an hourly range of $100 to $170. Review the current compensation details during the application process.

• Employment type: Contractor and part time
• Work arrangement: Remote
• Expected workload: Approximately 10 hours per week
• Project duration: 8 to 10 weeks
• Language: English

Why Build An AI Training Career

AI training and data-labeling work is a fast-growing way to contribute to technology from anywhere with a computer or phone and an internet connection. Specialist projects can let people apply advanced expertise directly to the development and evaluation of state-of-the-art AI systems.

OpenTrain helps contributors discover projects that match their skills, document credible experience, and grow a portfolio across AI training work.

• Apply specialized physics expertise to cutting-edge AI evaluation
• Work remotely with a flexible part-time schedule
• Build experience in an expanding technical field
• Create a profile and apply through OpenTrain