Cambridge Quantum has announced the discovery of a new algorithm that will accelerate quantum Monte Carlo integration. This new algorithm will shorten the time and will offer an advantage to the finance industry in its quantum computing integration efforts.
Monte Carlo integration is a technique of numerical integration by using random numbers. This technique is particularly useful when we work with higher dimensional integrals. This process is used in drug development, financial risk analysis, supply chain logistics, and in many other different business and scientific applications. This process asks for huge computing resources to complete with today’s technology, so a different and more scalable solution needed to be developed.
“This new algorithm is a historic advance which expands quantum Monte Carlo integration and will have applications both during and beyond the NISQ era. We are now capable of achieving what was previously only a theoretical quantum speedup. That’s something that none of the existing quantum Monte Carlo integration (QMCI) algorithms can do without substantial overhead that renders current methods unusable.”
Steven Herbert, Senior Research Scientist at CQC
CQC Senior Research Scientist Steven Herbert has released a paper on how they overcame the issue by creating the new algorithm. The paper was published on arXiv, and you can fully read it here.
“This is an impressive breakthrough by the scientists at CQC that will be of tremendous value to the financial sector as well as many other industries and is just the latest in a continuing streak of innovations that confirm our world leading position in quantum computing.”
Ilyas Khan, CEO of Cambridge Quantum Computing
About CQC
QCQ was founded in 2014 and and is gained the reputation in the industry and has gathered a lot of support by many different companies and institutions, CQC is a world leader in quantum software and quantum algorithms, allowing clients to achieve the most out of the rapidly evolving quantum computing hardware. They have offices in the UK, USA, and Japan. If you want to learn more about QCQ please visit their websiteand theirLinkedIn. You can also access the tket Python module onGitHub.