Cambridge Quantum Algorithm solves optimization problems much faster and outperforms existing quantum methods

Cambridge Quantum Algorithm solves optimization problems much faster and outperforms existing quantum methods-01

In a development likely to set a new industry standard, Cambridge Quantum ( CQ ) scientists have developed a new algorithm to solve combinatorial optimization problems that are widely used in business and industry are, such as B. Traveling Salesman, vehicle routing or job shop scheduling, using quantum computers of the near future.

Math puzzles like this underlie a variety of real-world optimization challenges, such as: B. the design of manufacturing processes, the filling of delivery vans or the routing of passenger aircraft. As the level of automation in modern global companies increases from year to year, optimization algorithms that run on even the most powerful classic computers are forced to trade accuracy for speed.

In this  publication,  which appeared on the pre-print repository arXiv, the CQ scientists present the Filtering Variational Quantum Eigenolver (F-VQE) to make combinatorial optimization more efficient. Using the Honeywell System Model H1 quantum computer, the new approach outperformed existing “gold standard” algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and the original VQE, achieving a good solution 10 to 100 times faster.

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The study was carried out by a CQ research team consisting of Dr. Michael Lubasch , Dr. David Amaro , Dr. Carlo Modica , Dr. Matthias Rosenkranz and Dr. Marcello Benedetti wrote. The scientists are part of the CQ machine learning and quantum algorithms team led by Dr. Mattia Fiorentini .

F-VQE uses a method published in this  study  by CQ in September 2020 , which showed how a quantum circuit can be broken down into smaller circuits and operated with fewer qubits without losing the quantum advantage. As a result, a 23 qubit problem was solved by using only up to 6 hardware qubits at a time. The CQ scientists also showed that the new approach is very well suited for use with Noisy Intermediate Scale Quantum Era (NISQ) machines. These advances increase the size of the optimization problems that are within the reach of today’s NISQ calculators.

“Our scientists are working on a number of practicable methods for today’s quantum computers. We want companies and governments to achieve a quantum advantage faster in general tasks, and our experience from working with large industrial partners enables a deep understanding of the needs of today’s practitioners, “says Fiorentini.” F-VQE has clear advantages over previous quantum algorithms: It finds good solutions faster and uses quantum hardware much more efficiently. F-VQE could have a transformative effect and help to solve previously unsolvable problems in the entire economy and industry. ”

Ilyas Khan , CEO of CQ , said, “Our team of scientists are relentlessly focused on bridging the gap between the real limits of classical computation and the quantum advantage that will be available in the NISQ era. They are setting new standards in quantum computing, and their research will inspire further advances rapidly. ”

Tony Uttley , President of Honeywell Quantum Solutions, said, “This project illustrates the exciting advances that are being made in quantum computing. By developing algorithms that do more with fewer qubits and running them on the best possible hardware, we are making a significant advance in solving real-world problems earlier than expected. ”

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