Here's a batch of fresh news and announcements from across Imperial.
From a new way to tackle agricultural pests, to a grant awarded to develop quantum machine learning techniques for finance, here is some quick-read news from across Imperial.
Medfly modifications
Researchers have created the first gene drive for the Mediterranean fruit fly (medfly), a global agricultural pest affecting food production. The team was led by Dr Nikolai Windbichler and Dr Angela Meccariello at Imperial's Department of Life Sciences, and included researchers from the University of East Anglia and the Hebrew University of Jerusalem.
Gene drives are genetic modifications that preferentially spread throughout a species, and which are designed to reduce the population. No gene drives have been released in the wild yet, but versions in malaria-carrying mosquitos have been shown to be highly effective in the lab.
This success prompted the researchers to look at other pest species that could be susceptible to similar interventions. The team were able to target the process of sex determination in medflies, creating a gene drive that transforms genetic females into fertile but harmless XX males. The proof-of-concept demonstrates how gene drives can be applied to insect pests in the same group as medflies.
Dr Meccariello said: "Our result demonstrate the untapped potential for gene drives to tackle agricultural pests in an environmentally friendly and economical way."
Read the full paper in Nature Communications.
Quantum for finance
, Professor Jack Jacquier and Dr Christopher Salvi, from the Department of Mathematics at Imperial, are part of an Innovate UK grant to develop quantum machine learning techniques for financial data streams. Together with Rigetti Computing and Standard Chartered, they will develop techniques designed to enable financial institutions to process, interpret, and make decisions with complex data streams more effectively.
Financial institutions need to continuously interpret complex data streams to extract information necessary for providing accurate credit risk evaluation, managing market-making services, and predicting emissions in the context of green finance, among other things. Using ideas from rough paths combining quantum computing with classical machine learning methodology for sequential data could offer more powerful resources for processing financial data streams, given the potential for quantum computers to process some types of information more efficiently than with classical resources alone.
Dr Salvi said: "Making the software implementation open access is crucial to ensure further development of these tools both in academia and industry. The outcomes of our joint work can help strengthen the UK's efforts in quantum computing research."
