quantum computing

The selected candidate will be responsible for designing, building, training, and deploying machine learning models on Xanadu’s cutting-edge specialized quantum computing hardware. As part of our Quantum Machine Learning team, they will participate in multiple aspects of machine learning research and development targeted to near-term quantum devices. Other duties may include the training of traditional (non-quantum) machine learning models for comparison and benchmarking purposes.

We are establishing a new research institute headed by Martin Kliesch on quantum computing and related topics, see www.tuhh.de/quantum.

The selected candidates will work with our established team of physicists and engineers to build our integrated photonic quantum computation platform based on the continuous variable (CV) approach. They will be involved in all aspects of the quantum hardware system at Xanadu’s lab: design of photonic components, construction of the apparatus, and carrying out key experiments. They will also be responsible for writing patents and peer-reviewed publications describing these devices and experiments. Successful applicants will have a proven track record of accomplishments in experimental quantum optics, having developed during their research careers cutting-edge techniques for the generation, control, and detection of non-classical light.

Multiphoton quantum interference is one of the most intriguing phenomena in quantum physics, and is at the very heart of quantum computing and metrology technologies. However, the post-classical sensing and computational capabilities of multiphoton networks are yet far from being fully explored in practical experimental scenarios.

This theoretical project aims to develop scalable sensing and computational techniques based on the use of optimal linear interferometers with experimentally available photonic input states. The main idea is to exploit the full quantum information encoded in the interferometric evolution of the input photonic quantum states by employing novel measurement techniques (e.g. iterative interferometric dynamics, conditional dynamics, multiplexing and correlation measurements sensitive to the photonic inner and spatial modes).

Start date: 1 October 2018 or 1 February 2019
Application deadline: 7 May 2018
Supervisors: Dr V Tamma, Dr H Yu (Univeresity of Portsmouth), Prof G Adesso (University of Nottingham)

Submission deadline: 

Friday, June 1, 2018

Registration deadline: 

Friday, June 1, 2018

This 3-day workshop on applications of discrete phase space methods in fault-tolerant quantum computing provides a platform for young researchers to present their work while leaving enough opportunities for extended discussions and informal exchange of ideas. The workshop targets equally students entering the field and researchers with a few years of experience.

Pages

Subscribe to RSS - quantum computing