MAY 2018CIOAPPLICATIONS.COM 19Applying quantum algorithms to real-world problems will provide the greatest competitive advantage in futureWhat are the potential applications and impacts?Despite the hype, this technology is still experimental and nascent. On the Gartner Hype Cycle for Emerging Technologies 2017, quantum computing is climbing the Innovation Trigger phase. It currently offers limited business applications and can only run very specific quantum algorithms. Furthermore, the equipment is expensive, fragile, lacks standardization, with materials, designs and approaches varying wildly.Gartner predicts that quantum computing-as-a-service (QCaaS) will be the predominant method used by data scientists to obfuscate this risk. Gartner recommends that organizations first focus on QCaaS to gain experience with quantum algorithms as they apply to business solutions. Because it's a new field, everything must be built from ground up, plus it's difficult to even comprehend the potential of the technology or the problems QCaaS could potentially solve. However, the potential that quantum computing has for solving problems in ML, AI and big data, where classic computing limits potential, is driving a lot of innovation and growth among data scientists. Investors are putting millions of dollars toward the technology, and more than 50 companies, universities and research companies are working on development.What are the applications?Current and future applications for quantum computing will be narrow and focused. General-purpose quantum computing will most likely never be realized. However, the technology does hold the potential to revolutionize certain industries, including AI, cryptography, and even weather prediction.For example, billions and billions of IoT devices are providing petabytes/second of information, most of which is discarded because of the storage requirements. A weather prediction model might require millions of IoT devices, sensors, and external feeds such as satellite imagery and radar information all transmitting continuous data that ideally, could be analyzed instantaneously. Due to this, all this information would have to be loaded directly into quantum memory resulting in immediate analysis. This continuous analysis could potentially provide meteorologist with more accurate weather forecasting. Other applications include:· Machine learning: Improved ML through faster structured prediction. Examples include Boltzmann machines, quantum Boltzmann machines, semi-supervised learning, unsupervised learning, and deep learning.· Artificial intelligence: Faster calculations could improve perception, comprehension, self-awareness, and circuit fault diagnosis/binary classifiers.· Finance: Quantum computing could enable faster, more complex Monte Carlo simulations, for example, trading, trajectory optimization, market instability, price optimization and hedging strategies.· Healthcare: DNA gene sequencing, such as radiotherapy treatment optimization/brain tumor detection, could be performed in seconds instead of hours or weeks.· Computer science: Faster multidimensional search functions, for example, query optimization, mathematics, and simulations.Not every CIO needs to worry about quantum computing, but for now, those looking to explore the technology should focus their data scientists on the advancement of quantum algorithms and how they can be applied to solve practical business problems. Applying quantum algorithms to real-world problems will also provide the greatest competitive advantage in future.
< Page 9 | Page 11 >