2026 Internship Project: Hamiltonian Learning
About The Position
Rigetti Computing is building the world’s most powerful computers to solve humanity’s most pressing problems. We believe this technology will fundamentally change the world for the better and will affect nearly every industry over the coming decades. Understanding the bottlenecks and improving the fidelities of operations on quantum chips is critical to achieve this goal. About the internship As an intern, you will work on implementing algorithms to learn the native Hamiltonian and noise governing the qubits on a superconducting quantum chip during parallel gate execution. You will model the effective Hamiltonian and noise on a small multi-qubit subsystem, co-design experiments to validate the model, reconstruct the Hamiltonian and noise from numerical simulations, and validate the model on the chip. You will produce a pre-production research codebase which can be used for continued development to learn the Hamiltonian and noise. This is a rare opportunity for an intern to work on cutting edge research outside academia, with the opportunity to interface with benchmarking, calibration, and device theory teams. Additional points: There is flexibility to work remotely or in the office at Berkeley. Your mentors will be remote. The internship should last 3 months over the summer of 2026, with flexible start and end dates.
Requirements
- Master or PhD candidate: Currently pursuing a Masters or Ph.D. in quantum many-body physics, quantum information, or a related field.
- Strong coding skills: Proven experience using Python for implementing advanced simulations of quantum many-body systems.
- Analytical Rigor and Technical Communication: A highly motivated self-starter with the critical thinking skills to analyze and interpret results, and the communication skills to distill complex findings for cross-functional teams.
Nice To Haves
- Quantum Hardware Experience: Hands-on experience designing and executing experiments on quantum hardware.
- Collaborative Research: Experience working in cross-team environments including theorists and experimentalists to achieve research milestones.
- Coding Experience: Proven experience with python libraries such as numpy, scipy, QuTiP, JAX.
- Scientific Impact: A demonstrated publication record in quantum physics.
Responsibilities
- Implementing algorithms to learn the native Hamiltonian and noise governing the qubits on a superconducting quantum chip during parallel gate execution.
- Model the effective Hamiltonian and noise on a small multi-qubit subsystem
- Co-design experiments to validate the model
- Reconstruct the Hamiltonian and noise from numerical simulations
- Validate the model on the chip
- Produce a pre-production research codebase which can be used for continued development to learn the Hamiltonian and noise.
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What This Job Offers
Career Level
Intern
Education Level
Ph.D. or professional degree
