Scalable and Automated Tuning of Spin-based Quantum Computer Architectures

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Scalable and Automated Tuning of Spin-based Quantum Computer ArchitecturesBrief description of the context of the research including potential impact: - Quantum computing is an emergent technology that has the potential to solve classically intractable problems using a new paradigm of computation. The basis for such computation uses the quantum states of a system to encode information, unlike the binary states of modern-day transistors (bits). Of the many candidate platforms for realising these quantum bits (qubits), spins confined in semiconductor structures are attractive for their long coherence times as well as small size and ease of integration with control electronics, giving them an edge in terms of scalability. The challenge lies in moving away from proof-of-concept devices (containing just a handful of qubits) to microchips that contain dozens of such qubits and beyond. This is currently hindered by device variability and noise associated with the host material, which, in the first case, makes device tuning very difficult, and in the second case, can lead to a reduction in the fidelity of quantum operations, especially if these noise sources have spatio-temporal correlations. If clever algorithms and protocols can be devised to, firstly, tune multiple devices efficiently, and secondly, compensate for noise in real-time, this would mark an enormous step forward in the scalability of spin qubit architectures. Aims and objectives:- The aim of this project is to explore machine learning methods capable of tuning and stabilizing spin qubits over large length-scales. In doing so, the hope is to inspire the development of spin qubit architectures that are most resilient to variability and noise. Until now, spin qubit architectures have been pioneered with a physics mindset, i.e. how spin qubits can most easily interact with one another and how they can be most easily addressed using magnetic and electric fields. The novelty of this project approach lies in the software and hardware co-design, which has previously not been considered at the device design stage. This will be an interdisciplinary effort, drawing on both experimental physics and software methodologies, such as accelerated Bayesian optimization methods and time series analysis - a new effort within spin-based quantum computing. Some milestones for this project would include the characterization of material disorder at the wafer scale, expansion of tuning algorithms beyond a handful of qubits, and new methods for compensating noise, as would be evidenced by an enhancement in the fidelity of quantum operations. Some material platforms that could be investigated include Ge/SiGe heterostructures, Si-MOS, and Si Fin-FET devices, with the goal to isolate the most promising candidate. EPSRC alignment:- This project falls within the EPSRC Quantum Technologies research area.Any companies or collaborators involved:- An existing collaboration with Katsaros Group at the Institute of Science and Technology Austria (ISTA) will be extended, from whom we will receive quantum devices on which our algorithms and experiments will be tested. In addition, devices could be sourced from the Quantum Coherence Laboratory at the University of Basel or the Quantum Technologies division at the IBM Research Laboratory in Zurich or Scappucci Lab at QuTech. Moreover, to characterize quantum devices on a wafer scale, we will reach out to the teams at IBM or at CEA-Leti in Grenoble to use their state-of-the-art cryo-prober system. Lastly, we will continue collaborations with groups at the University of Oxford from departments of Engineering Science as well as Statistics, who provide invaluable algorithmic input.