Research Scientist, Gate Measurement, Quantum AI

The Gates team is executing a multipronged research program to reach fidelities required for long-lived logical qubits. As a Gates Research Scientist, you will work on identifying noise processes through precise error benchmarking and use these results to identify new gate design and architecture parameters. You will implement improved error-metrology circuits and cross-validate them with physical models from the theory team. You will also be responsible for conducting detailed architecture analysis and simulations, and defining hardware requirements of new gate designs for forward-looking architectures.

As a Research Scientist, you will collaborate with other research scientists, hardware engineers, and software engineers to make high-performance quantum computing a reality.

The full potential of quantum computing will be unlocked with a large-scale computer capable of complex, error-corrected computations. Google Quantum AI's mission is to build this computer and unlock solutions to classically intractable problems. Our roadmap is focused on advancing the capabilities of quantum computing and enabling meaningful applications.

Responsibilities

• Characterize and identify limiting error mechanisms in quantum logic gates.
• Collaborate with theory team to build and verify physics models for noise and error processes in quantum logic gates.
• Perform detailed architecture modeling, analyze design parameters and build hardware system specifications for forward-look architectures.
  
• Develop software infrastructure and tools for wider team use.

Minimum qualifications:

• PhD in Physics, Applied Physics, or Electrical Engineering, a related technical field, or equivalent practical experience.
• Experience collecting and analyzing data in an experimental setting.
• Experience with programming and statistical analysis of datasets.
• Experience with techniques to characterize and calibrate hardware performance.

Preferred qualifications:

• Experience in analysis and modeling of multi-qubit quantum platforms.
• Experience collecting and analyzing data in an experimental setting.
• Experience with techniques to characterize and calibrate hardware performance.
• Proficiency with programming and statistical analysis of large datasets.