The Quantum Hardware Team at Quantum Motion develops and operates silicon based quantum processors built with standard CMOS (complementary metal-oxide-semiconductor) technology. This approach offers advantages for scaling such as high qubit density, long qubit coherence lifetimes and the ability to leverage the advanced nanofabrication methods of the semiconductor industry.
Quantum Motion has built a long and reliable track record in silicon qubit technology. The team has demonstrated a four-qubit silicon processor with gate fidelities above 99% and has set new standards in high-quality qubit readout. The group has also driven automated routines forward, including characterisation of 1024 quantum dots in less than 5 minutes. These advances open up many exciting research opportunities for spin-qubits based on silicon MOS devices, fabricated using the same processes used routinely across the IC industry today.
The project will be carried out in our facilities at the CIC nanoGUNE in San Sebastian under the supervision of Prof. Fernando Gonzalez-Zalba and Quantum Motion industrial supervisor Dr. Virginia Ciriano-Tejel. Moreover, it includes the option, if beneficial to the project and your interests, to spend periods working at the Quantum Motion headquarters in London.
Project Focus
This project tackles a real bottleneck in quantum computing. As the number of qubits grows, tuning, calibration and stable operation of qubits become increasingly difficult to manage. The aim of this PhD is to develop reliable automation routines that support these processes and allow systems of many transistors at low temperature to operate as functional quantum processors.
This PhD track combines experimental work with machine learning. You will work at the front line of quantum engineering and develop skills that reach well beyond this field. Your contributions will feed directly into the development and deployment of next-generation silicon-based quantum processors.
You will gain hands-on experience and deep expertise in:
Perform dynamical operations on spin qubits.
Analyse and interpret experimental data, contributing to scientific publications, patents, and presentations.
Applying machine learning methods for automation, including reinforcement learning, Bayesian approaches and practical data analysis
Working with RF and DC electronics and high frequency laboratory equipment
Running experiments at millikelvin temperatures in dilution fridges