Research Scientist, Quantum AI

Minimum qualifications:

• PhD in Physics, Electrical Engineering, a related engineering discipline, or equivalent practical experience.
• 3 years of experience with Quantum Computing.
• Experience with superconducting qubits.
• One or more scientific publication submission(s) for conferences, journals, or public repositories.
  

Preferred qualifications:

• Experience with Circuit Quantum Electrodynamics (QED), transmon qubits, fluxonium qubits, tunable couplers, or flux/charge noise analysis.
• Experience with collaborative software development.
• Experience collaborating with experimentalists on quantum projects.
• Understanding of two-qubit gates modeling, and qubit readout modeling.

About the job

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

As a Research Scientist, you will play a critical role in modeling and understanding the performance of our superconducting qubit systems and their control electronics. You will be responsible for simulating single and two-qubit gates and readout processes, incorporating realistic environmental noise and limitations from both the quantum device and the control hardware. You will collaborate closely with hardware/electronics design and measurement teams to identify and model error sources that limit overall system performance. Your analysis will directly inform the development of next-generation qubits and control strategies to accelerate our progress toward a large-scale, fault-tolerant quantum computer.

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.

The US base salary range for this full-time position is $141,000-$202,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Simulate single and two-qubit gates and readout for superconducting qubits, incorporating realistic environmental noise (e.g., thermal noise, flux noise, charge noise) and Adiabatic Quantum-Flux-Parametron (AQFP) control hardware constraints (e.g., amplitude and timing resolution, limited bandwidth, pulse distortion, crosstalk).
• Propose and analyze new gate and readout methods compatible with AQFP electronics.
• Collaborate with teams focused on design and measurement to model errors limiting Quantum Processing Unit (QPU) performance and develop improvement strategies.