Here is an excerpt from an article written by** Francesco Bova**, **Avi Goldfarb, **and **Roger Melko** for *Harvard Business Review* and the *HBR* Blog Network. To read the complete article, check out the wealth of free resources, obtain subscription information, and receive *HBR* email alerts, please click **here**.

Credit: oxygen/Getty Images

* * *

To understand what’s going on, it’s useful to take a step back and examine what exactly it is that computers do.

Let’s start with today’s digital technology. At its core, the digital computer is an arithmetic machine. It made performing mathematical calculations cheap and its impact on society has been immense. Advances in both hardware and software have made possible the application of all sorts of computing to products and services. Today’s cars, dishwashers, and boilers all have some kind of computer embedded in them — and that’s before we even get to smartphones and the internet. Without computers we would never have reached the moon or put satellites in orbit.

These computers use binary signals (the famous 1s and 0s of code) which are measured in “bits” or bytes. The more complicated the code, the more processing power required and the longer the processing takes. What this means is that for all their advances — from self-driving cars to beating grandmasters at Chess and Go — there remain tasks that traditional computing devices struggle with, even when the task is dispersed across millions of machines.

A particular problem they struggle with is a category of calculation called combinatorics. These calculations involve finding an arrangement of items that optimizes some goal. As the number of items grows, the number of possible arrangements grows exponentially. To find the best arrangement, today’s digital computers basically have to iterate through each permutation to find an outcome and then identify which does best at achieving the goal. In many cases this can require an enormous number of calculations (think about breaking passwords, for example). The challenge of combinatorics calculations, as we’ll see in a minute, applies in many important fields, from finance to pharmaceuticals. It is also a critical bottleneck in the evolution of AI.

And this is where quantum computers come in. Just as classical computers reduced the cost of arithmetic, quantum presents a similar cost reduction to calculating daunting combinatoric problems.

### The Value of Quantum

Quantum computers (and quantum software) are based on a completely different model of how the world works. In classical physics, an object exists in a well-defined state. In the world of quantum mechanics, objects only occur in a well-defined state after we observe them. Prior to our observation, two objects’ states and how they are related are matters of probability. From a computing perspective, this means that data is recorded and stored in a different way — through non-binary qubits of information rather than binary bits, reflecting the multiplicity of states in the quantum world. This multiplicity can enable faster and lower cost calculation for combinatoric arithmetic.

If that sounds mind-bending, it’s because it is. Even particle physicists struggle to get their minds around quantum mechanics and the many extraordinary properties of the subatomic world it describes, and this is not the place to attempt a full explanation. But what we can say is quantum mechanics does a better job of explaining many aspects of the natural world that classical physics does, and it accommodates nearly all of the theories that classical physics has produced.

Quantum translates, in the world of commercial computing, to machines and software that can, in principle, do many of the things that classical digital computers can and in addition do one big thing classical computers can’t: perform combinatorics calculations quickly. As we describe in our paper, *Commercial Applications of Quantum Computing**, *that’s going to be a big deal in some important domains. In some cases, the importance of combinatorics is already known to be central to the domain.

* * *

Here is a direct** link** to the complete article.

*Prediction Machines: The Simple Economics of Artificial Intelligence*(Harvard Business Review Press, April 2018).