Infleqtion is a quantum technology company developing neutral atom quantum computers, quantum sensors, and software platforms that combine quantum computing with AI applications.
Can you briefly talk about your own background and the origins of the company?
I’m a computer scientist by training and started my career in AI, working as a machine learning engineer in Silicon Valley after college. I also grew up in Maryland, close to the NIST laboratory, which gave me early exposure to quantum technology and its potential for computing and sensing. After Silicon Valley, I went to graduate school to study how to accelerate the development of quantum computing using software and machine learning techniques.
My PhD focused on building a software stack for quantum processing units, inspired by what NVIDIA’s CUDA did for GPUs. My advisor and I spun that work into a company called Super.tech, which developed machine learning tools to improve quantum hardware performance. In 2021, while working with different quantum technologies, we became very interested in a neutral atom qubit approach being developed by a Colorado company called ColdQuanta. We ultimately merged with them in 2022 to form Infleqtion. Since then, the company has focused heavily on software, applications, and the relationship between AI and quantum technology.
What technologies is Infleqtion commercializing today, and how do those platforms translate into real-world applications?
Everything we do builds on the hardware heritage. At the core is a compact vacuum cell that isolates atoms so they can be used either for quantum computing or for quantum sensing. We have developed multiple products from that same foundational technology, including atomic clocks, inertial sensors, and our quantum computing platform.
Our atomic clocks provide extremely accurate timing, which is critical not only for GPS systems but also for synchronizing AI data centers and GPUs. We also build a quantum spectrum receiver, the first fundamental shift in radio frequency (RF) sensing architecture in decades that can detect signals across an enormous range of frequencies in a single compact device, and an inertial sensor that is being deployed with NASA to measure gravity from orbit for applications such as monitoring aquifers and ice caps. All of these systems are connected through our software platform, Superstaq, which uses machine learning and reinforcement learning techniques throughout its operation.
There is often a tendency to frame AI and quantum computing as separate or even competing technologies. How do you see the relationship between the two evolving?
We do not see AI and quantum as competitors. We see quantum as the next extension of the AI curve that began with CPUs and accelerated dramatically with GPUs. Modern AI systems are already exposing the limitations of today’s hardware, so we believe quantum becomes the next unlock for AI rather than a replacement for it.
We see three major areas where quantum enhances AI. First, quantum computing can dramatically expand memory and context windows for AI models. We’ve demonstrated roughly a 10x extension using qubits instead of traditional bits. Second, quantum sensors can provide significantly higher-quality training data, improving AI model outputs. Third, physical AI and world models struggle today with chemistry, materials science, and drug discovery because those problems operate at the quantum level. Embedding quantum solvers into AI systems allows them to model the microscopic behavior of the physical world far more accurately.
Quantum computers are often described as fragile and unstable. How is the industry addressing that challenge related to their real-world operability?
That description was absolutely accurate before 2023. The major breakthrough over the last few years has been the transition from fragile physical qubits to what are called logical qubits. Historically, quantum systems suffered from noise and instability, with qubits lasting only microseconds or milliseconds before errors emerged.
What changed is that companies like Google and Infleqtion learned how to combine many imperfect physical qubits into a single stable logical qubit. It’s similar to how Wi-Fi transmits imperfect packets but still delivers a flawless signal to the user. Google first demonstrated one logical qubit in 2023, and Infleqtion followed in 2024 with two logical qubits developed jointly with NVIDIA. We later demonstrated 12 logical qubits and expect to show 30 this year. Once the industry reaches around 100 logical qubits, we believe quantum systems will be capable of running long, commercially valuable computations reliably.
In this case, why has quantum computing not yet been adopted by businesses, and which sectors do you expect to move first?
Quantum sensing has already been widely adopted, even if people do not always think of it as quantum technology. Atomic clocks are a perfect example. Every time someone uses GPS, they are relying on quantum systems. Governments and enterprises are already deploying quantum sensing technologies for navigation, communications, and infrastructure resilience.
Quantum computing itself is more difficult because it represents one of the greatest engineering challenges humanity has undertaken. We are approaching an inflection point, however. Every additional logical qubit doubles potential computational power, which means progress scales exponentially. Today, large GPU clusters can still simulate quantum systems, but only for a limited time. Once we move from around 30 logical qubits toward 50 or 100, the computational advantage becomes overwhelming. We expect enterprises to begin meaningful adoption around 2028, and many organizations are already preparing their infrastructure for that transition.
How do you see scalability and accessibility evolving for quantum computing?
One fascinating connection between AI and quantum is that AI is making quantum systems far easier to use. A few years ago, there was concern that enterprises would need teams of PhDs to operate quantum computers. Today, people can increasingly interact with these systems using natural language interfaces instead of highly specialized coding environments.
That shift changes the accessibility equation dramatically. Users can now ask systems in plain English to optimize logistics routes, improve investment portfolios, or assist with drug discovery. Our software platform, Superstaq, was designed specifically to abstract away the underlying physics and mathematics so that financial professionals, material scientists, and business users could interact directly with the technology. The barriers are no longer purely technical, which should accelerate broader adoption across industries.
What are your priorities for the next few years, and what excites you most about Infleqtion’s future?
Our commercialization strategy has been heavily inspired by NVIDIA’s evolution. NVIDIA did not begin with AI as its primary market. It started with graphics, then expanded into scientific computing and cryptocurrency before AI became its defining opportunity. We are taking a similar path by first commercializing quantum sensing technologies such as atomic clocks, RF sensors, and inertial sensors en route to practical quantum computing.
What excites us most is reaching the 100 logical qubit milestone, because that is where quantum computing begins solving problems that classical systems realistically cannot. We believe this will fundamentally reshape the compute fabric of society and unlock entirely new classes of applications. I’m particularly excited by the universality of neutral atom technology. Because it does not require dilution refrigeration, it can be deployed in space, underwater, on land, and eventually across many industries. We believe quantum technology will move beyond the lab and become something that impacts everyday life in the coming few years.
