When and where to leverage emerging quantum computing technology for enterprise applications is the framework embodied in a recent U.S. patent awarded to Accenture.
U.S. Patent No. 10,275,721 announced this week is the business consultant’s (NYSE: ACN) second covering quantum computing technology. The latest describes a method for using machine learning to determine when best to use quantum or standard digital computing. The idea behind the tool is to help early adopters of quantum computing balance costs in order to reap the benefits of quantum technology.
“Determining when to employ quantum—as opposed to, or in tandem with classical computing—is critical to realizing this potential,” said Marc Carrel-Billiard, senior managing director of Accenture Labs. As quantum and new digital platforms emerge, Accenture said its patented machine learning module could be used to adapt to what the business consultant predicts will be “computational variety.”
The machine learning framework builds on earlier development, including a 2018 patent award for a “multi-state quantum optimization engine.” The optimization engine is designed to help users identify a range of range of answers to a business problem. By running multiple, simultaneous simulations, the best outcome could be identified, thereby yielding the best decision.
Company researchers have developed quantum-based prototypes used for applications like vehicle rout planning. Accenture Labs also has been investigating the impact of quantum technology on current data encryption methods. One implication is that businesses may soon be unable to ensure data security since quantum machines able to run an “integer factorization” algorithmcould be used to identify secret cryptographic keys.
The heightened research reflects Accenture’s bet that the “tipping point” for enterprise-ready quantum computing will arrive within the next five years, adding that its is “taking a vendor agnostic approach to apply the technology at the right time for its enterprise clients.”
Other efforts include heterogeneous hardware configurations that might eventually combine neuromorphic and optical computing architectures with quantum processing, the company said this week.
This is a syndicated post. Read the original post at Source link .