{"id":26876,"date":"2021-08-14T06:00:00","date_gmt":"2021-08-14T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=26876"},"modified":"2021-08-11T15:51:16","modified_gmt":"2021-08-11T22:51:16","slug":"cambridge-quantum-algorithm-solves-optimization-problems-significantly-faster-outperforming-existing-quantum-methods","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2021\/08\/14\/cambridge-quantum-algorithm-solves-optimization-problems-significantly-faster-outperforming-existing-quantum-methods\/","title":{"rendered":"Cambridge Quantum Algorithm Solves Optimization Problems Significantly Faster, Outperforming Existing Quantum Methods"},"content":{"rendered":"\n

A novel combinatorial optimization algorithm sets a new quantum computing standard at the heart of the modern economy<\/em><\/p>\n\n\n\n

In a development that is likely to\u00a0set a new industry standard, scientists at Cambridge Quantum (CQ<\/a>) have developed a new algorithm for solving combinatorial optimization problems that are widespread in business and industry, such as traveling salesman, vehicle routing or job shop scheduling, using near-term quantum computers.<\/p>\n\n\n\n

Mathematical conundrums like these lie at the heart of a vast range of real-world optimization challenges such as designing manufacturing processes, filling delivery trucks or routing passenger jets. As the level of automation in modern global businesses\u00a0increases year over year, optimization algorithms running on even the most powerful classical computers are forced to trade accuracy for speed.<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

In this\u00a0paper<\/a>\u00a0published on the pre-print repository arXiv, CQ scientists introduce the Filtering Variational Quantum Eigensolver (F-VQE) to make combinatorial optimisation more efficient. Using the Honeywell System Model H1 quantum computer, the new approach outperformed existing \u201cgold standard\u201d algorithms such as the Quantum Approximate Optimisation Algorithm (QAOA) and the original VQE, reaching a good solution 10 to 100 times faster. The paper has been authored by CQ\u2019s research team comprising Michael Lubasch, Ph.D., David Amaro, Ph.D., Carlo Modica, Ph.D., Matthias Rosenkranz, Ph.D., and Marcello Benedetti, Ph.D. The scientists are part of CQ\u2019s Machine Learning and Quantum Algorithms team headed by Dr. Mattia Fiorentini.<\/p>\n\n\n\n

F-VQE leverages a method published in this\u00a0paper<\/a>\u00a0by CQ in September 2020, which demonstrated how a quantum circuit can be decomposed into smaller circuits and run using fewer qubits without losing quantum advantage. As a result, a 23-qubit problem was solved by using only up to 6 hardware qubits at time. CQ\u2019s scientists also demonstrated that the new approach is highly adaptable for use with noisy intermediate-scale quantum (NISQ) era machines. These advancements increase the scale of the optimization problems that are within reach of today\u2019s NISQ computers.<\/p>\n\n\n\n

\u201cOur scientists are honing in on a range of workable methods for today\u2019s quantum computers. We want enterprises and governments to achieve quantum advantage for general purpose tasks more quickly, and our experience of working with large industrial partners facilitates a deep understanding of the needs of practitioners today.\u201d said Fiorentini. \u201cF-VQE has distinct advantages over previous quantum algorithms: it finds good candidate solutions faster and uses quantum hardware much more efficiently. F-VQE could have a transformative impact, helping to solve previously intractable problems across business and industry.\u201d
<\/p><\/blockquote>\n\n\n\n

\u201cOur team of scientists is relentlessly focused on closing the gap between the real-world limits of classical computation and the quantum advantage that will be available in the NISQ era,” said Ilyas Khan, CEO of CQ. “They are establishing new standards in quantum computing and their research will inspire rapid further progress.\u201d <\/p><\/blockquote>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

Join us on Twitter: @InsideBigData1 \u2013 https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

In a development that is likely to set a new industry standard, scientists at Cambridge Quantum (CQ) have developed a new algorithm for solving combinatorial optimization problems that are widespread in business and industry, such as traveling salesman, vehicle routing or job shop scheduling, using near-term quantum computers.<\/p>\n","protected":false},"author":10513,"featured_media":26878,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[115,180,67,268,56,1],"tags":[982,634,1011,96],"acf":[],"yoast_head":"\nCambridge Quantum Algorithm Solves Optimization Problems Significantly Faster, Outperforming Existing Quantum Methods - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2021\/08\/14\/cambridge-quantum-algorithm-solves-optimization-problems-significantly-faster-outperforming-existing-quantum-methods\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cambridge Quantum Algorithm Solves Optimization Problems Significantly Faster, Outperforming Existing Quantum Methods - 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