Two Applications of Quantum Computing

As researchers are discovering new applications for quantum computing, currently we know of several useful applications.

One of those is search and optimization. In this application, quantum computing can be used where there are many options or entries where we need to find a pattern or specific optimal value. For instance, a Data Engineer may need to use Grover’s Algorithm to find a piece of information from a certain user. Other use cases include energy distribution, climate modeling, and portfolio optimization. With these diverse use cases, quantum computing sounds like it solves almost any problem. However, if you are familiar with parallel computing, it may sound like quantum computing isn’t necessary.

Parallel computing is known to use many processors to simultaneously execute multiple, smaller calculations by breaking down a larger, complex problem. Still, parallel computing only solves one-to-one problems, where each input results in one output. Quantum computing can solve many-to-one problems, where many inputs result in one output. To do this, the quantum mechanical properties of superposition and interference are in play to process multiple inputs simultaneously while also reaching the correct or optimal solutions after processing the inputs.

Another application is using quantum systems to simulate the properties in nature with quantum mechanics. Since the resources needed to simulate nature scale exponentially, quantum computers can be used to use the same set of rules that nature does. Researchers can apply this to finding a protein’s structure, discover the next novel battery materials, or create powerful solar cells.

These two applications are very hot in research right now because fault-tolerant quantum computers are needed to use these applications to their fullest. As companies, such as Google and IBM, continue to innovate and develop their quantum technology, applications like these will be made easier to use.