Efficient quantum device tuning using machine learning

Fault-tolerant quantum computers require hundreds to millions of physical qubits to be operated with high fidelity. Inevitable hardware imperfections must be tuned away through iterative interplay of characterization, simulation, and parameter refinement, with each data point informing the decision of what to measure next. The technology is only scalable if this task can be efficiently automated. Recent progress in machine learning, currently one of the most rapidly developing fields of computing, makes it possible to automate the entire process. This project will apply these new techniques experimentally, working with leaders in machine learning.

The objective is to achieve automated tuning of semiconductor qubits encoded in gate-defined quantum dots. These qubits are an ideal testbed because the physics is known and the device parameters are conveniently optimized by gate voltages. Nonetheless, tuning a simple device by hand takes days to weeks, which is clearly not scalable.  We expect this machine learning approach to enable the tuning of large quantum circuits.

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