Analyst(s): Dr. Bob Sutor
Publication Date: December 19, 2024
The future is quantum, classical, and AI. We will combine these technologies into workflows to solve problems critical to society, with each component handling the kind of computation it does best. NVIDIA and Infleqtion have shown that they can use the CUDA-Q quantum software development kit with NVIDIA GPUs and Infleqtion quantum computers to begin attacking materials science problems. Infleqtion went beyond their neutral atom physical qubits to implement logical qubits with lower error rates.
What is Covered in this Article:
- Logical qubits and their murky definition
- NVIDIA’s CUDA-Q SDK and its increasingly broad use for quantum computing
- Integrating classical and quantum computing for practical applications
- The materials science computation performed by the companies
- Targeting practical applications versus useless benchmarks
- Corporate giants partnering with neutral-atom quantum computing vendors
The News: On December 10, NVIDIA and the Colorado-based quantum company Infleqtion announced they had successfully worked together on a demonstration workflow using classical and quantum computers for a materials science calculation. The companies highlighted their use of NVIDIA GPUs and its CUDA-Q open-source quantum computing software development kit (SDK) with logical qubits running on Inflection’s neutral atom quantum computer.
Quantum in Context: Infleqtion and NVIDIA Put CUDA-Q to Practical Use
Analyst Take: In a week whose news was briefly dominated by the Google Willow chip announcement, NVIDIA and Infleqtion described how they partnered to demonstrate the integration of classical GPU and quantum technologies on a materials science computation. Practical Quantum Advantage will occur when quantum and classical systems work together and perform significantly better than classical systems alone. Rather than showing outstanding (and historic) performance on a use case with no practical applications, the two companies showed that the CUDA-Q SDK has the expressive power for eventual useful integrated classical-quantum workflows. The combination of CPUs, GPUs, and QPUs (quantum processing units) will be critical for advancing computational solutions and innovations in this century.
Tools to Create Quantum Computing Applications
A quantum computing application combines classical code written in languages such as Python, C++, and Rust, and instructions that are sent to a quantum computer for execution. Much of the latter is generated by classical code via one or more layers of abstraction and pre-written libraries for well-defined functionality and algorithm implementation. When we put all the tools, libraries, and documentation together, we get a “quantum software development kit (SDK).”
Qiskit (“kiss kit”) is the most widely used quantum SDK. The top-level interface is Python, and IBM has been rewriting the underlying code in Rust for improved performance. This is significant because tools that work well enough with a few dozen qubits can become quadratically or exponentially slow when the qubit count gets over one hundred.
Cirq is Google’s quantum SDK and its programming interface is also written in Python. It is widely used within the quantum coder community but has seen less active development than Qiskit. Like Qiskit, hardware providers that support the gate-and-circuit quantum programming paradigm can (and often do) provide backends for the two SDKs to allow coders to execute their circuits and applications on the quantum systems.
Pennylane from Xanadu and Q# from Microsoft are two other commonly used quantum SDKs.
NVIDIA introduced CUDA in 2006 as a set of programming application interfaces for developers to create applications using the company’s GPUs. Analysts and users widely attribute NVIDIA’s success to its creation of a well-supported ecosystem of developers around the CUDA platform. As quantum computing attracted more hardware vendors and coders, NVIDIA introduced CUDA-Q in July 2022. You can view this quantum SDK as a move by NVIDIA to extend its platform from GPUs for classical and AI applications into the quantum world. NVIDIA lists 23 quantum hardware and software partners on its website for CUDA-Q. NVIDIA wants its GPUs and systems to work seamlessly with quantum computers from other vendors. Note that NVIDIA does not have its own quantum computing hardware, yet.
At the time of writing, NVIDIA does not list Infleqtion as a partner, but this announcement shows that they are working closely together. CUDA-Q is systematically growing in popularity among vendors and coders. Do the developers of other quantum SDKs need to be worried?
Progressing Toward Practical Applications
Quantum computers are too small today for practical applications in society and business. For most of the last decade, people have spoken about potential application areas where quantum and classical computers could solve problems far faster and more accurately than classical systems alone. To put it politely, the kinds of applications and their timelines are subject to extreme hype and misunderstanding. Even major vendors have not been immune from promulgating the confusion.
I think we need to focus on quantum computing progress in practical areas versus marketing performance benchmarks for useless problems. If I were to tell you, “I have developed a breakthrough medicine that can cure you of a terrible disease in one second, but it is impossible for you to contract the disease,” how impressed would you be with the bio-medical industry?
For this reason, I am pleased that Infleqtion and NVIDIA chose to demonstrate progress on a materials science computation. The products of materials science are all around us: metallic alloys, semiconductors, plastics, and ceramics.
The Importance of Logical Qubits, Whatever They Are
The result the companies demonstrated was small but in the right direction. Infleqtion created two “logical qubits,” and the scientists and engineers ran the algorithm on them via CUDA-Q.
What is a logical qubit? It depends on who you ask.
Quantum computing system providers build quantum processing units (QPUs) from physical qubits that use or emulate how quantum mechanics works on small particles such as electrons and photons. They can use at least ten modalities to implement physical qubits with many sub-variations.
Unfortunately, for quantum computing, our world, or at least the small bits of it, also runs via quantum mechanics. The environment around the physical qubits, the material from which we build the qubits, and how we control the qubits can interfere with computations and cause errors.
Researchers have been working on quantum error correction since 1995. When we use multiple qubits and software to reduce the error rate dramatically, we can create a “logical qubit.” The vagueness in the definition of this kind of qubit comes from several sources:
- How “dramatically” is the error rate reduced? Ten times? One hundred times? One million times?
- Can we detect if errors still occur and where? How many can we detect?
- Can we correct errors? How many?
In 2025, I hope the industry rallies around more precise definitions. In particular, we need a new name for logical qubits that detect but do not correct errors.
What to Watch:
- Will CUDA-Q eventually surpass older quantum SDKs such as IBM’s Qiskit, Google’s Cirq, and Xanadu’s Pennylane?
- Is this partnership an example of the trend of large corporations partnering with neutral-atom quantum computing vendors, such as we have seen with IBM and Pasqal, Google and QuEra, and Microsoft and Atom Computing?
- Will Infleqtion update its early 2024 quantum computing roadmap to take into account this and other recent progress?
For additional details, see
- the “Infleqtion Delivers First Quantum Material Design Application Powered by Logical Qubits and NVIDIA CUDA-Q” press release,
- the “NVIDIA CUDA-Q Runs Breakthrough Logical Qubit Application on Infleqtion QPU” blog post, and
- the “Turn Down the Noise: CUDA-Q Enables Industry-First Quantum Computing Demo With Logical Qubits” blog post.
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. Bob Sutor is a former employee of Infleqtion and has an equity position in the company. The analyst has no equity position in any other 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: Timing is Everything for Infleqtion and the DoD
Quantum in Context: Infleqtion’s Quantum Computer Live at UK Testbed
Quantum in Context: Infleqtion Key to UK Quantum PNT Tech Test
Quantum in Context: Quantum Companies Rotate in New Leaders
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.
