Analyst(s): Dr. Bob Sutor
Publication Date: November 8, 2024
Document #: AIESBS202411
This is a summary of a full Futurum Group report that discusses a talk delivered by Dr. Bob Sutor, Vice President and Practice Lead of Emerging Technologies at The Futurum Group, at the Inside Quantum Technology Quantum+AI conference in New York City on October 29, 2024. The talk title was Quantum AI: A Quantum Computing Industry Perspective. Sutor discussed the kinds of quantum computers, the three ways quantum computing and AI technologies can be combined, and the essential questions to consider when reading or hearing discussions about the topics.
Key Points:
- Practical Quantum Advantage will occur when quantum and classical systems work together and perform significantly better than classical systems alone.
- We will need hundreds of thousands to millions of physical qubits to create hundreds to tens of thousands of logical qubits to reach Practical Quantum Advantage.
- Will fault-tolerant quantum systems be necessary for Quantum for AI?
- Are you seeing science, engineering, marketing, or hype?
Overview:
As much as it is scientifically or intellectually engaging to see vendor or academic results on exotic problems with no everyday use cases, we want useful results that justify the time and cost of developing these systems.
Why Quantum and AI Together?
There are good reasons to combine the technologies, but the argument that “AI requires a lot of computation and quantum computers are supercomputers” is simplistic and insufficient and ignores other ways of combining the tech for value.
The Three Kinds of Quantum Computers
We first divide quantum computers into universal and non-universal types. Among the universal ones, they support a digital or analog model.
Researchers often use classical simulators for digital quantum computers. If you are not doing AI on quantum hardware and instead are using a simulator, are you really doing “AI and Quantum”?
Integrating AI and Quantum Technologies
Many forms of AI use machine learning and many of those use neural networks. It’s impossible to directly transfer the neural network processing model to quantum because of one fundamental issue: one cannot copy quantum information. Alternative quantum methods show progress but are still very much in the research phase.
Rather than use quantum to perform the calculations for AI, why don’t we go up a level and think of workflows instead of processes? Real problems require workflows for their complete solutions, not just algorithms.
If we have data about how quantum systems respond when we initialize them, run the algorithms, and then measure the results. We can use machine learning to suppress some errors or mitigate their effects. This will lead to better results and less work for eventual fault tolerance in these computers.
The full report is available via subscription to Futurum Intelligence—click here for inquiry and access.
Disclosures: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The analyst has no equity position in any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
Other Insights from The Futurum Group:
Quantum in Context: A Qubit Primer
Quantum Computing Benchmarks – Too Soon?
Quantum in Context: IBM Qiskit Boosts Software Development Speed
Quantum in Context: Quantinuum Announces 5-Year Roadmap to Apollo
Author Information
Dr. Bob Sutor has been a technical leader and executive in the IT industry for over 40 years. Bob’s industry role is to advance quantum and AI technologies by building strong business, partner, technical, and educational ecosystems. The singular goal is to evolve quantum and AI to help solve some of the critical computational problems facing society today. Bob is widely quoted in the press, delivers conference keynotes, and works with industry analysts and investors to accelerate understanding and adoption of quantum technologies. Bob is the Vice President and Practice Lead for Emerging Technologies at The Futurum Group. He helps clients understand sophisticated technologies in order to make the best use of them for success in their organizations and industries. He is also an Adjunct Professor in the Department of Computer Science and Engineering at the University at Buffalo, New York, USA. More than two decades of Bob’s career were spent in IBM Research in New York. During his time there, he worked on or led efforts in symbolic mathematical computation, optimization, AI, blockchain, and quantum computing. He was also an executive on the software side of the IBM business in areas including middleware, software on Linux, mobile, open source, and emerging industry standards. He was the Vice President of Corporate Development and, later, Chief Quantum Advocate, at Infleqtion, a quantum computing and quantum sensing company based in Boulder, Colorado USA. Bob is a theoretical mathematician by training, has a Ph.D. from Princeton University, and an undergraduate degree from Harvard College.
He’s the author of a book about quantum computing called Dancing with Qubits, which was published in 2019, with the Second Edition released in March 2024. He is also the author of the 2021 book Dancing with Python, an introduction to Python coding for classical and quantum computing. Areas in which he’s worked: quantum computing, AI, blockchain, mathematics and mathematical software, Linux, open source, standards management, product management and marketing, computer algebra, and web standards.
