Microsoft and KPMG are preparing to test the ability of azure quantum on various practical problems, which will make quantum computing dominant in traditional methods.
These problems are related to optimizing systems and networks, such as where to best place mobile phone towers, or how to allocate investments to match customers’ priorities in risk and return.
Krysta svore, general manager of Microsoft quantum, explained in a blog post today: “optimization problems exist in many industries, and it is usually difficult to solve them with traditional methods that can accelerate optimization.”. “Simulating these quantum effects on classical computers has led to the development of quantum optimization (QIO) algorithms running on classical hardware.”
These algorithms reflect the quantum view, in which the information is not necessarily strict 1 and 0, but can reflect a series of values at the same time. The beauty of QIO algorithms is that they do not need to run on honest and kind quantum processors, which are still in their infancy.
The cooperation between Microsoft and KPMG gives the two companies an opportunity to adjust the algorithm and how to use them to maximize the QIO function of azure quantum.
Bent Dalager, global head of KPMG quantum hub, said: “the azure quantum platform allows us to explore a variety of different solutions with the same code, which helps to minimize duplication of work and improve efficiency.”. “The common goal of these initial projects is to use azure quantum to build a solution blueprint for common industry optimization problems, and then we can provide it to more customers on a large scale.”
This is not the first time the Microsoft quantum computing team has tried real-world optimization challenges: a few years ago, researchers at Microsoft and Ford used the QIO algorithm to analyze strategies to alleviate traffic congestion in Seattle. Preliminary research shows that congestion has been reduced by more than 70% and the average travel time has been reduced by 8%.
Last year, Toyota Tsusho and a Japanese startup called Jij used azure quantum to optimize the timing of traffic lights. They found that the QIO algorithm can reduce the waiting time for drivers to stop in front of the red light by about 20%, saving an average of about 5 seconds per vehicle. Trimble, headquartered in California, turned to azure quantum to determine the most effective route for the fleet and ensure fewer empty trucks.
The Microsoft KPMG project will first focus on the benchmark solution to optimize the financial service portfolio and telecom operations.
Portfolio optimization must balance the portfolio to minimize risk and maximize profit within a given budget. As financial options become more and more complex, it becomes more and more difficult to evaluate them using brute force analysis, but QIO algorithm is very suitable to meet this challenge.
Quantum optimization can also improve the voice efficiency of LTE telecom network and bring customers a better user experience. Next, the project can study the optimization strategies of base station layout, mobile handoff between base stations and call center employee scheduling.