Traditional computers operate based on data that is encoded in a binary system. Essentially, each bit of data is represented in zeroes and ones only — no more, no less than the two forms. Hence, the binary computing system. However, there is a new generation of computers emerging on the horizon called quantum computing and it's taking computing systems beyond the normal binary.
Instead of just the zeroes and the ones, quantum computing depends on quantum bits or qubits. Quantum computing breaks the boundaries of traditional computing by allowing the information — coded in qubits — to have multiple states at once. This phenomenon, known as superposition, unlocks more computing power than previously imagined. With such a complicated background story, where does the use of quantum computing make the most sense in the current context and what benefits does it offer practically?
No doubt, the technology is still in the early stages of development, but despite the novelty of the innovation, there is a shortlist of tech mammoths in line to invest in it. Companies like IBM, Google and Microsoft have taken initial steps to invest in and adopt quantum computing.
One of the areas where quantum computing is more lucrative and promising is artificial intelligence. As AI operates on the analysis of large datasets, the margin of error and inaccuracy in the process of learning has significant room for improvement — and quantum computing may well allow us to improve the algorithm's ability to learn and interpret.
How does quantum computing exactly help in artificial intelligence and more specifically, in machine learning? The truth is modern-day machine learning's extent of efficiency and success largely depends on the dataset it's given. The size of the dataset determines the quality of the results, so if the information isn't ample, the output won't be promising either. However, thanks to quantum computing's ability to go beyond the traditional binary coding system, it makes it possible to enlarge and enrich the data set, both in terms of volume and diversity. With better and more in-depth datasets, it becomes possible to better train machine learning models, which can then contribute to real-life problem-solving.
Another enhancement that quantum computing introduces to AI is the improvement of "natural language processing" capabilities that allow for a more in-depth understanding and analysis of textual data. With quantum computing, the algorithms can become more aware of the content of the textual data. In other words, the machine will be able to truly understand the meaning behind the data, being able to analyze whole sentences and phrases instead of just words.
When it comes to the industry-specific impact that quantum computing can render, the list of advantages it introduces is also long and very well possible in the near future. Some of the most promising breakthroughs quantum computing offers is in health care. Since traditional computers are limited in the amount of data they can hold and analyze, quantum computing steps in to expand the sheer size and variety of various molecules available for research and comparison. As the process of molecule simulation and comparison is the backbone of any drug development initiative, quantum computing will push the boundaries of medical research by allowing for more informed and diverse simulations designed to test the interactions between drugs and human body molecules. In addition to research, quantum computing can improve capacities for pattern recognition, create enlarged data sets and improve MRI image accuracy thereby allowing medical professionals to diagnose and treat conditions sooner.
Similar to diagnostics in health care, quantum computing will unlock new capabilities in the finance industry. Specifically, with fraud detection, which relies largely on pattern recognition. Quantum computers can help detect fraud early on and significantly increase the speed of analysis thanks to its improved power and capacity.
Beyond health care and finance, quantum computing will also completely redefine marketing practices in place today. With enlarged consumer data sets and analysis capabilities, brands and companies will be able to introduce a completely new level of customization to meet the needs of individual customers and users. Quantum computing will make it possible to target customers on a much more granular level, developing communication campaigns that accurately meet — and anticipate — customer needs and preferences.
By enhancing AI's ability to analyze and learn based on data sets, quantum computing will eliminate the main concerns about data quality and analysis accuracy that still serve as obstacles in the application process of machine learning algorithms in various scenarios. Even though quantum computing needs more time to find full integration and adoption across industries today, the possibilities it may introduce are, by all means, revolutionary and promising.
Originally published on Forbes