Realistic machine learning based ultra fast simulator for semiconductor spin qubit devices

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Spin qubits are one of the potential hardware platforms for quantum computing. For semiconducting spin qubits to be operated most devices must be kept at 20 mK. This low temperature operation requires very expensive cryogenic lab equipment to maintain the semiconducting spin qubit devices at operational temperatures.As quantum computing companies ramp up their production during this exciting period of growth, experimental time is a limited resource as the suppliers of cryogenic lab equipment try to keep up with demand.The development of a qubit control software for quantum computing requires access to quantum device hardware to test and improve the performance. A spin qubit machine learning based ultra fast simulator (MLUS) will enable the development of this qubit control with limited access to quantum computing hardware removing a major obstacle for QuantrolOx and other companies. The difficulty is that available quantum device simulators are significantly slower than the measurements that can be performed in this hardware. In order to develop software able to control quantum devices in real time, we require MLUSs. With this goal, our MLUS will be based on novel machine learning approaches.This represents an enormous **market opportunity** to accelerate the QuantrolOx qubit control software that will enable UK quantum computing hardware companies to scale and also for other companies to develop their qubit control algorithms using the MLUS.The proposed project will be a key step in realising automated qubit tuning, optimisation and stabilisation. The implemented automated qubit control software that will enable quantum computers to scale substantially beyond current capabilities.This is a joint project between the leading UK company producing automated control software for quantum technologies, the leading UK laboratory developing AI for quantum technologies, and the UK National Centre for Quantum Computing.