Current semiconductor-based quantum computing approaches rely upon achieving control of nanocircuits at the single-electron level and using them as quantum bits (qubits). Establishing a stable configuration of spins in quantum dot (QD) devices is accomplished by a combination of electrostatic confinement, bandgap engineering, and dynamical control via nearby electrical gates. However, with an increasing number of QD qubits, the relevant parameter space grows exponentially, making heuristic control unfeasible. In semiconductor quantum computing, devices now have tens of individual electrostatic and dynamical gate voltages that must be carefully set to isolate the system to the single-electron regime and to realize good qubit performance. Large-scale quantum processors hinge on fully autonomous tuning processes that can be parallelized for practical applications.
Over the past decade, there has been a number of advances aimed at automation of the various aspect of tuning QD devices, from testing device functionality and bootstrapping to setting the device topology to charge tuning. While the initial attempts relied on the appealingly intuitive and relatively easy to implement conventional algorithms involving a combination of techniques from regression analysis, pattern matching, and quantum control theory, more recently researchers began to take advantage of the tools provided by the field of artificial intelligence. I will discuss how our recently proposed ray-based classification framework (RBC) can be combined with an “action-based” algorithm to tune a bootstrapped QD device into a specific few-electron charge configuration, paving a path towards fully automated QD quits initialization.
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