D-Wave customers have, so far, pursued more than 200 early quantum applications on the D‑Wave quantum computer across a range of disciplines.
Check out our recent video showing what our customers have to say regarding applications they are developing on the D-Wave system:
Some examples are provided below; for a more extensive list, see the D-Wave website.
Imagine you are building a house, and have a list of things you want to have in your house, but you can’t afford everything on your list because you are constrained by a budget. What you really want to work out is the combination of items which gives you the best value for your money.
This is an example of a optimization problem, where you are trying to find the best combination of things given some constraints. While problems with only a few choices are easy, as the number of choices grows, they quickly get very hard to solve optimally. With just 270 on/off switches, there are more possible combinations than atoms in the universe!
These types of optimization problems exist in many different domains, such as systems design, mission planning, airline scheduling, financial analysis, web search, and cancer radiotherapy. They are some of the most complex problems in the world, with potentially enormous benefits to businesses, people and science if optimal solutions can be readily computed.
Example: Volkswagen Group: Optimizing taxi travel time through Beijing
Volkswagen was the first car manufacturer to use a quantum computer to calculate traffic flows. Their scientists did a research project for traffic flow optimization. Using data from 10,000 taxis in Bejing, they programmed an algorithm to optimize the travel time of taxis in the city.
See more examples of optimization applications on the D-Wave website.
When you look at a photograph it is very easy for you to pick out the different objects in the image: trees, mountains, cats, etc. This task is almost effortless for humans, but is in fact a hugely difficult task for computers to achieve. This is because programmers don’t know how to define the essence of a ‘tree’ in computer code.
Machine learning is the most successful approach to solving this problem, by which programmers write algorithms that automatically learn to recognize the ‘essences’ of objects by detecting recurring patterns in huge amounts of data. Because of the amount of data involved in this process, and the immense number of potential combinations of data elements, this is a very computationally-expensive optimization problem. As with other optimization problems, these can be mapped to the native capability of the D-Wave QPU.
Machine learning relies heavily on sampling from complex probability distributions. NASA scientists successfully the D-Wave system on image data sets in a generative unsupervised learning setting, one of the most difficult paradigms in machine learning.
See more examples of machine learning on the D-Wave website.
In 1981, Nobel Prize–winning physicist Richard Feynman delivered his seminal lecture “Simulating Physics with Computers”. His idea was that unlike a classical computer which could only approximate a simulation of physics, a quantum computer could simulate it exactly – as quantum physics. In a paper published in 1982 he said, “I therefore believe it's true that with a suitable class of quantum machines you could imitate any quantum system, including the physical world.”
Today quantum materials simulation is being actively pursued by scientists around the world, and some see it as the first “killer application” for quantum computers.
Example: Los Alamos National Lab: Graph partitioning for quantum molecular dynamics simulation
Motivated by the use of graph-based methods for quantum molecular dynamics simulations, LANL's work explores graph partitioning/clustering methods on D-Wave systems. Results are shown to equal or out-perform current “state of the art” methods.
See more examples of materials simulation on the D-Wave website.