When a new product is launched, it often takes a year or longer to get a grasp on public response to the item. Instead of waiting until sentiment is readily available, we could scrape the web for all relevant product information and feedback from multiple locations, including online retail stores, online shopping reviews, professional reviews sites, social media, news, and financial releases. This could allow us to identify common topics/themes that occur in those documents. For example, we can see what features of the new product the users appreciated, and which features were commonly eschewed. Quantum computing has recently begun to venture into this realm in the form of quantum latent semantic analysis and quantum NLP. It has been shown that quantum topic analysis can outperform classical computing algorithms in certain situations and potentially could allow the processing of greater amounts of data in less time than a classical computation would take.
Quantum optimization programs are not only computationally more efficient but also produce improved results
Another very big area where quantum computing can have a significant impact is freight logistics. Often, large companies need to move products, both components and finished level goods, between locations using a variety of different modes of transportation. Moving components into factories, shipping them across oceans, and loading ships and ground transports with the correct items as well as giving them the routes which best minimize time and cost is an area of intensive research in business. If a company knew the optimal routes to move inventory, it would allow faster response times in meeting customer demand, thus driving sales while also keeping inventory costs down. Various companies have already begun experimenting with quantum computers to obtain advantageous freight routes. Quantum optimization programs are not only computationally more efficient but also produce improved results. The technology is still a few years behind full-scale implementation, but the first companies to develop the techniques and prove their superiority will certainly gain an edge over their competition.
We are truly standing on the precipice of a new era of computing, but there still are numerous hardware and software challenges before successfully applying quantum computers. First, they need to be cooled to nearly absolute zero. Second, they require a myriad of advanced technologies, and building them can easily run into the high numbers of millions. Third, using a quantum computer requires manually stringing together qubits, about as base a level of computer programming as you can get. This means that the flashy machine learning packages and even standard programming commands taken for granted in classical computers do not exist in the quantum world.
Commercial applications of quantum computers are still being discovered, and technology giants such as Google, IBM, and Microsoft have even opened their machines to the public to use for free as people test the waters and begin to figure out uses and applications. Although it is unlikely, quantum computers will ever replace classical computers, a future in which the two will walk side by side is almost inevitable. Putting in place quantum algorithm research divisions to research and develop novel applications will drive businesses into tomorrow, putting them on the cutting edge of technological development and light years ahead of the competition.
Aaron McClendon is a data scientist and practice lead at Aimpoint Digital, specializing in advanced applications of machine learning and artificial intelligence within business facing data science problems. His team focuses on time series analysis, natural language processing, deep learning, and AI within a research context to include quantum machine learning and potential applications of quantum computing within business.
Aleksandar Lazarevic is a VP Advanced Analytics & Data Engineering at Stanley Black & Decker. Aleks leads the company’s efforts to drive savings through advanced Big Data analytics. Aleks is responsible for providing scalable enterprise wide data lake / warehouse platform and leveraging machine learning / AI tools to solve various business problems. Previously, Aleks worked at Aetna, where he was responsible for overall analytics solution in health care fraud, waste and abuse detection. In addition, he has extensive experience in applying analytics in various industries ranging from banking, credit and insurance industry to smart manufacturing and computer security. He is also a frequent speaker at national data science and analytics conferences.
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