April 16, 2024

Rigetti Computing awarded a grant from Innovate UK to develop quantum machine learning techniques for financial data streams

Informative summary

  • Rigetti UK has received a grant from Innovate UK for a project using quantum computing to improve machine learning in finance, in partnership with AWS, Imperial College and Standard Chartered.
  • The consortium aims to combine classical and quantum computing to process complex financial data streams more efficiently, potentially outperforming current methods.
  • The project, which will begin in January 2024 and last 18 months, focuses on the development of scalable quantum-enhanced algorithms for financial data analysis, with open access implementation for broader industrial and academic use.

PRESS RELEASE: Rigetti UK Limited, a wholly owned subsidiary of Rigetti Computing, Inc. (Nasdaq: RGTI) (“Rigetti” or the “Company”), a pioneer in full-stack classical quantum computing, today announced that it has received the award . a grant from Innovate UK as part of the Feasibility Studies in Quantum Computing Applications competition. The consortium aims to use quantum computing to improve current classical machine learning techniques used by financial institutions to analyze complex data streams. Rigetti is joined in this work by Amazon Web Services (AWS), Imperial College London and Standard Chartered.

Financial institutions need to continually interpret complex data streams to extract the information needed to provide accurate credit risk assessment, manage market making services, and predict emissions in the context of green finance, among other things. Classic machine learning techniques used to assist and provide insights into these services have limitations as these data streams are generally complex. Combining quantum computing with classical machine learning methodology could offer more powerful resources for processing these data streams, given the potential for quantum computers to process some types of information more efficiently than classical resources alone .

Leveraging Rigetti’s quantum computer and software, Standard Chartered’s classical datasets and benchmarks, Imperial College London’s expertise in classical machine learning models for data streams, and AWS’s classical high-performance computing resources. , the consortium will seek to address the following research objectives: (1) Further develop quantum signature kernels and quantum-enhanced feature maps, (2) compare the results with classical machine learning methods for streaming data, and (3) build and study Quantum algorithms for computing signatures and signature kernels for long, high-dimensional data streams. efficiently.

The signature, a centerpiece of approximate path theory, provides a top-down description of a stream that filters out extraneous and noisy local information from a stream while retaining essential information. Its algebraic and analytical properties make it a natural universal feature map for streamed data. Members of the Imperial team have made significant efforts to scale signature methods to high-dimensional streams. An elegant solution is provided by signature kernels, which allow you to benefit from the benefits of working with infinite signature features without some of the attendant drawbacks. Adding a quantum element has the potential to improve classical kernel signature methods. These enhanced capabilities could provide a route to demonstrate a commercial application of quantum computing for finance and enable financial institutions to improve their efficiency through reduced costs and increased productivity.

“Developing quantum-enhanced machine learning solutions could enable financial institutions to harness the full power of NISQ-era computing and has the potential to accelerate our work toward a narrow quantum edge, the point at which a quantum computer surpasses to the best classical resources”. said Dr. Subodh Kulkarni, CEO of Rigetti. “Collaboration with leading UK financial institutions, AWS and universities should give us the insights we need to advance the development of quantum applications for the financial sector and many other industries with complex data sets.”

“The future of quantum computing will be built on the combination of quantum and classical computing infrastructure as part of a unified cloud-based environment. “This project is a great example of leveraging classical HPC resources to accelerate innovation in quantum machine learning algorithms, an important step as we move toward quantum advantage,” said Richard Molds, CEO of Amazon Braket. on AWS. “This initiative should not only benefit the financial sector, but could also encourage other industries to benchmark new machine learning models and continue to improve the performance of quantum algorithms.”

“Combining quantum technologies with rough path techniques has the potential to produce more scalable signal processing algorithms for complex financial data streams. Making the implementation open access is crucial to ensure further development of these tools in both academia and industry,” said Dr Cristopher Salvi, Professor of Mathematics and Machine Learning at Imperial College London. “The results of our work together can help strengthen the UK’s efforts in quantum computing research.”

“Quantum computing, like previous and current major technological advances, is poised to deliver broad benefits while causing significant disruptions to established business processes. That is why it is important for companies to prepare for the future by adopting this new technology from an early stage. “Our collaboration with Rigetti, Imperial College London and AWS gives us access to high-performance computing resources and expertise in quantum algorithms that could strengthen our position as an industry leader in a future quantum-ready economy,” said Craig Corte, Director Global Digital. Channels and customer data analysis at Standard Chartered.

The project began on January 1, 2024 and will last 18 months.

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