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QC Ware’s Breakthroughs in Quantum Machine Learning

By IQT News posted 28 Jul 2020

(Forbes) QC Ware recently announced several significant breakthroughs in quantum machine learning (QML).
Quantum machine learning is at the intersection of classical machine learning and quantum computing. However, because of current limitations in quantum computing technology, useful machine learning is primarily confined to the realm of classical computing.
QC Ware’s new developments are significant for several reasons.
–QC Ware researchers have developed Data Loaders, a QRAM alternative that can efficiently and easily load classical data onto quantum hardware.
–They are also an efficient method to perform distance estimations on a quantum computer. Distance estimation is an algorithm used in machine learning that tries to group each data point with other points or clusters with similar properties.
–QC Ware’s Data Loaders are available on its cloud platform called Forge.
“QC Ware estimates that with Forge Data Loaders, the industry’s 10-to-15-year timeline for practical applications of QML will be reduced significantly,” said Yianni Gamvros, Head of Product and Business Development at QC Ware. “What our algorithms team has achieved for the quantum computing industry is equivalent to a quantum hardware manufacturer introducing a chip that is 10 to 100 times faster than their previous offering. This exciting development will require business analysts to update their quad charts and innovation scouts to adjust their technology timelines.”
Because useful quantum machine learning is still many years away, QC Ware’s new products are definitely a positive contribution to not only QML, but quantum computing in general.
The new Forge additions will promote more research which will ultimately push the development of QML forward and at a faster pace.

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