Cambridge Quantum (CQ) has developed a new algorithm for solving combinatorial optimization problems that are widespread in business and industry, such as travelling salesman, vehicle routing or job shop scheduling, using near-term quantum computers.
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 increases year over year, optimization algorithms running on even the most powerful classical computers are forced to trade accuracy for speed.
Using the Honeywell System Model H1 quantum computer, the new approach outperformed existing “gold standard” algorithms such as the Quantum Approximate Optimisation Algorithm (QAOA) and the original VQE, reaching a good solution 10 to 100 times faster.
Mattia Fiorentini, head of CQ’s Machine Learning and Quantum Algorithms team, said in a statement: “Our scientists are honing in on a range of workable methods for today’s 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.”