To bring quantum technologies to market takes both technical expertise and commercial acumen. We combine skills with industry partners to maximise the chances of success and impact. This page highlights projects and partnerships initiated or led by CQT Principal Investigators and research staff.
Supported by Singapore’s DSO National Laboratories, CQT Principal Investigators began in 2020 applied research projects: “Research Collaboration On Optical Ground Station For Satellite Quantum Communications” led by Alexander Ling, and “Assessment On Advanced Inertial Sensing Techniques For Navigation And Characterization Of An Atomic Gravimeter Platform” led by Rainer Dumke.
SGInnovate is a Singapore government-backed organisation supporting the local development of deep tech. They have a partnership with CQT to support quantum as one of their focus areas, involving talks, training and support for startups.
CQT Talk - Quantum Machine Learning Journal Club Talk by Thanasilp Supanut, CQT, NUS
Title: Subtleties in the trainability of quantum machine learning models Date/Time: 16-Dec, 02:00PM Venue: Online Via Zoom
Abstract: A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called Quantum Machine Learning (QML), aims to achieve a speedup over traditional machine learning for data analysis. However, its success usually hinges on efficiently training the parameters in quantum neural networks, and the field of QML is still lacking theoretical scaling results for their trainability. Some trainability results have been proven for a closely related field called Variational Quantum Algorithms (VQAs). While both fields involve training a parametrized quantum circuit, there are crucial differences that make the results for one setting not readily applicable to the other. In this work we bridge the two frameworks and show that gradient scaling results for VQAs can also be applied to study the gradient scaling of QML models. Our results indicate that features deemed detrimental for VQA trainability can also lead to issues such as barren plateaus in QML. Consequently, our work has implications for several QML proposals in the literature. In addition, we provide theoretical and numerical evidence that QML models exhibit further trainability issues not present in VQAs, arising from the use of a training dataset. We refer to these as dataset-induced barren plateaus. These results are most relevant when dealing with classical data, as here the choice of embedding scheme (i.e., the map between classical data and quantum states) can greatly affect the gradient scaling.