With IBM leading in Deep Learning AI technology at scale – and one of the most visible in Quantum computing research – a lot of us were wondering when we’d see a presentation from the company combining the two technologies. Well, this week that wondering ended as IBM briefed us on what appeared to be the beginning of a new hybrid computer. One that combines the power of Watson with the power of their Quantum effort to create something very new and different.
What made this particularly interesting is that the two technologies are very different. Watson largely sprang out of a Neural Networking effort, partially focused on emulating the human brain and initially winning game shows as a showcase. So, at its heart, Watson is kind of an electronic computer emulating an organic computer. But Quantum technology is vastly different: it really didn’t even come from the technology market but from Physics Theory. And it not only has little in common with existing computers, it pretty much has nothing in common with organic computers – meaning that combining the two technologies is monumentally difficult.
But IBM has apparently figured out a path to this future. Let’s talk this week a bit about what that means.
We had a lot of issues when we moved from single core processors to multi-core solutions. The problem we had was that most programs were meant to execute sequentially, and this meant that when you put them on a typical multi-core computer you pegged one of the cores, leaving the rest idling unused. It took us awhile to figure out how to write and rewrite code so that it could be executed in parallel and performance jumped dramatically.
The change to Quantum computing makes that arduous process look exceedingly simple in comparison, because Quantum computers deal with data far differently. They computing elements don’t even have the same states, which both allows for more flexibility and creates a huge problem with regard to writing optimized code, because few coders understand this difference.
Even taking a simple program and converting would be problematic. And current thought is that you’d generally have to start from scratch with one of the handful of folks that might be able to write code for this platform. Ceating an application that was vastly different than anything that had been seen before.
Now what motivates you to do this is that the processing potential for a Quantum Computer is massively higher than we have yet experienced. And potential performance growth rates make Moore’s law look frozen in place in comparison.
This means taking a platform like Watson and converting it to run optimally on a Quantum computer will likely be beyond our skill set for the foreseeable future. But you could create a hybrid, though, and have the Quantum computer do a task that the AI doesn’t do well to allow the AI to perform more quickly.
Think of it like a turbocharger for a car. A turbocharger is a compressor and more similar to a jet engine in design, but turbines and cars didn’t work. But tied into an engine they compress the air/fuel mixture and make the car far faster. Together they are better than separately, and that is similar to what I’m talking about here. If a Quantum computer can turbocharge Watson, the result should be a significant performance boost.
One of the things IBM has discovered that Quantum computers do very well is structure unstructured data like images. They can, with an incredibly high degree of accuracy, sort highly complex objects.
Now Watson needs to be able to make decisions from unstructured data and traditional CPUs typically aren’t great, from performance standpoint, at structuring unstructured data. That is why we use GPUs instead for high performance efforts. Watson uses a lot of GPUs in its most advanced form, but Quantum computers are potentially far faster than a GPU. Thus, combining the two systems should result in vastly faster unstructured data analysis.
This won’t obsolesce GPUs because they will still be needed in the decision-making process. But now they will be fed data at far higher speeds than they could have previously accepted and, much like compressing the charge in that high-performance engine, the result should be a vastly more capable result.
Now there apparently needs to be an intermediate computer that bridges what the Quantum Computer structures and what the AI accepts. That intermediate computer is called a NISQ (noisy intermediate-scale quantum) computer. And it creates something like a translation bridge between the Quantum Computer and the AI. But the result should be massively beyond, in terms of both data complexity and performance for unstructured data, what a classical computer can accomplish.
AI by nature is performance limited, particularly with regard to unstructured data. What IBM proposes is kind of a Quantum Turbocharger for unstructured data, which could not only be applied to AIs but any computer solution that uses unstructured data by using a two-step approach.
That approach starts with the Quantum computer structuring the data so it can be consumed more quickly, a NISQ computer further organizing the data so it can be consumed at the primary computers full speed, and that primary computer which may or may not be an AI.
The result should be, according to IBM, logarithmically faster than anything we have on the market today and a true revolutionary game changer. If you think things are moving fast now, just wait until this puppy ships in the mid to late 2020s.
Ethics and Artificial Intelligence: Driving Greater Equality
FEATURE | By James Maguire,
December 16, 2020
AI vs. Machine Learning vs. Deep Learning
FEATURE | By Cynthia Harvey,
December 11, 2020
Huawei’s AI Update: Things Are Moving Faster Than We Think
FEATURE | By Rob Enderle,
December 04, 2020
Keeping Machine Learning Algorithms Honest in the ‘Ethics-First’ Era
ARTIFICIAL INTELLIGENCE | By Guest Author,
November 18, 2020
Key Trends in Chatbots and RPA
FEATURE | By Guest Author,
November 10, 2020
FEATURE | By Samuel Greengard,
November 05, 2020
ARTIFICIAL INTELLIGENCE | By Guest Author,
November 02, 2020
How Intel’s Work With Autonomous Cars Could Redefine General Purpose AI
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
October 29, 2020
Dell Technologies World: Weaving Together Human And Machine Interaction For AI And Robotics
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
October 23, 2020
The Super Moderator, or How IBM Project Debater Could Save Social Media
FEATURE | By Rob Enderle,
October 16, 2020
FEATURE | By Cynthia Harvey,
October 07, 2020
ARTIFICIAL INTELLIGENCE | By Guest Author,
October 05, 2020
CIOs Discuss the Promise of AI and Data Science
FEATURE | By Guest Author,
September 25, 2020
Microsoft Is Building An AI Product That Could Predict The Future
FEATURE | By Rob Enderle,
September 25, 2020
Top 10 Machine Learning Companies 2021
FEATURE | By Cynthia Harvey,
September 22, 2020
NVIDIA and ARM: Massively Changing The AI Landscape
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
September 18, 2020
Continuous Intelligence: Expert Discussion [Video and Podcast]
ARTIFICIAL INTELLIGENCE | By James Maguire,
September 14, 2020
Artificial Intelligence: Governance and Ethics [Video]
ARTIFICIAL INTELLIGENCE | By James Maguire,
September 13, 2020
IBM Watson At The US Open: Showcasing The Power Of A Mature Enterprise-Class AI
FEATURE | By Rob Enderle,
September 11, 2020
Artificial Intelligence: Perception vs. Reality
FEATURE | By James Maguire,
September 09, 2020
Datamation is the leading industry resource for B2B data professionals and technology buyers. Datamation's focus is on providing insight into the latest trends and innovation in AI, data security, big data, and more, along with in-depth product recommendations and comparisons. More than 1.7M users gain insight and guidance from Datamation every year.
Advertise with TechnologyAdvice on Datamation and our other data and technology-focused platforms.
Advertise with Us
Property of TechnologyAdvice.
© 2025 TechnologyAdvice. All Rights Reserved
Advertiser Disclosure: Some of the products that appear on this
site are from companies from which TechnologyAdvice receives
compensation. This compensation may impact how and where products
appear on this site including, for example, the order in which
they appear. TechnologyAdvice does not include all companies
or all types of products available in the marketplace.