The PGI-2 team of superconducting qubit theory, coordinated by Dr. Ansari https://sites.google.com/site/mansari , is renowned for its expertise in accurately modeling such processors. The group specializes in integrating machine learning techniques with circuit QED theory to identify optimal regimes for hardware control and development. With their exceptional skills in device modeling, the team significantly contributes to the success of all experimental partners in the project. Join us now and be a part of this groundbreaking research journey!
Your Job:
Circuit Quantum Electrodynamics (QED) provides insights into the behavior of superconducting qubit circuits with a few qubits, but scaling up the number of qubits presents significant challenges. As the qubit count increases, modeling accuracy diminishes, often resulting in experimental gate and processor performances that rely on imprecise simulations.
Our objective is to refine the model for greater precision and, using this improved model, to identify optimized parameter regimes through extensive machine learning. We collaborate closely with leading experimental teams across Europe to simulate their devices
This collaboration necessitates prompt scientific communication with experimentalists, developing models based on actual devices, and exploring avenues to enhance performance. The work will involve extensive use of numerical and analytical techniques pioneered by our group. We are seeking an applicant with a robust background and experience in machine learning and quantum computing to contribute meaningfully to this project
Your Profile:
Masters and subsequent Ph.D. degree in theoretical Physics/Electrical Engineering, Mathematics or a related subject
Solid background and experience in machine learning and quantum computing
Familiarity with Machine Learning and circuit QED is highly desirable
Ability to work in a committed and independent manner with flexibility and open-mindedness for new challenges
Interest in working in an interdisciplinary and international team of scientists
Fluency in written and spoken English
We particularly welcome applications from people from a diverse range of backgrounds (e.g., regardless of age, gender, disabilities, sexual orientation/identity, or social, ethnic, and religious background). We strive to offer a diverse and inclusive working environment in which people enjoy equal opportunities and can fulfill their potential.
