Mastering operational efficiency: How to offer today real solutions to tomorrow’s quantum problems

Choosing between option A or B in our daily lives can be often complicated, especially when the decision is taken within a context of uncertainty; what I am going to eat for breakfast to maximize my energy in a tough working day. It seems it is going to rain: should I take a car or a bus, which is the best way to reach my daughter’s school in the rush hour?  This kind of day-by-day questions could seem simple to answer but if we try to solve them using math we can end up with a problem of 10^xx combinations which a normal computer could take hours, even centuries to solve. Moreover, if we jump from individual choices to operational problems that companies have to face to improve their businesses, then the combinations of possible solutions can grow exponentially.

In the world of Big Data and AI often the complexity comes from the amount of data we need to process or how much data we need to train an algorithm. But, if we think in combinatorial problems like the above, then it is a matter of number of calculations, and so, the technologies needed to solve them must be different. Quantum computing seems to be a feasible answer but we will need to wait some years until quantum computers can face real problems.

In order to close this gap, we are going to be talking about digital annealing as a real solution for solving real optimization problems today. To do so, we will go through some real examples in Financial Services, Manufacturing or Logistics showing the challenges to be faced and the achieved business outcomes.