One might think nuclear energy and quantum technology as unrelated. One concerns power plants, uranium, and electrical grids. The other revolves around next-generation computers, ultra-precise sensors, and advanced encryption. However, both are built on the same scientific foundations: quantum physics, or the study of how matter and energy behave at the smallest scales. A nuclear reactor and a quantum computer are governed by the same physical laws. Increasingly, tools developed in one field are becoming useful to the other.

The connection already works in both directions. Nuclear science has quietly contributed to quantum computing for years. The International Atomic Energy Agency (IAEA) has highlighted how ion beams — streams of charged particles — produced by accelerators used in nuclear research are now used to manufacture quantum devices. A well-established process known as ion implantation allows scientists to place individual atoms inside materials such as silicon and diamond with extraordinary precision. Those atoms make up qubits, which are the basic units of information inside quantum processors, many times smaller and faster than traditional computer information bits. The infrastructure built for atomic research is now the basis for creating machines capable of simulating atomic behavior more accurately than ever before.

Once that connection becomes clear, the next question is obvious: what can quantum technology do for the nuclear energy industry? The answer is substantial.

Quantum Computing Can Dramatically Speed Up Nuclear Simulations

Operating a nuclear reactor safely requires understanding the behavior of neutrons inside a reactor core. Those particles collide, scatter, and are absorbed by fuel, continuously changing the temperature and pressure of the surrounding system. Since these interactions are driven by statistical processes, quality information can only be obtained through large number sampling, often around a quadrillion (or a million billions) of interactions. Suffice it to say that designing safer reactors or testing new fuels requires simulations of enormous complexity.

The primary computational method used to track neutrons is the Monte Carlo method, which tracks particles as they move through materials and interact with reactor components. The method is essential for reactor physics and the transport of radioactive materials, but it is computationally expensive. Complex simulations can take days or weeks on classical computers and still rely on approximations because no conventional machine can model the entire system directly.

Quantum computers offer a different approach to the computer application. Because they operate according to quantum principles, they can model nuclear interactions more naturally. A UK research project led by ANSWERS Software Service, part of Amentum, alongside Oxford Quantum Circuits, the National Nuclear Laboratory, Sellafield, and the University of Cambridge, has already demonstrated that quantum algorithms can accelerate Monte Carlo simulations beyond the capabilities of classical systems. Algorithms complete in thousands of steps that calculations on conventional hardware would require millions. For an industry where regulatory approval can take years, reducing simulation times from weeks to hours could significantly alter reactor development timelines.

Both the nuclear and quantum computing fields, however, face the same obstacle: instability. Quantum computers are sensitive to environmental interference, a problem known as quantum noise. The Amentum ANSWERS project has evaluated methods for reducing interferences on working hardware. The more stable quantum computers become, the more dependable they are for the complex calculations that reactor design demands.

Quantum Computing Can Find Better Reactor Materials

Faster simulation is only one positive aspect of quantum computing. Quantum computing may also address one of nuclear energy’s most persistent challenges: materials degradation. The inside of a reactor is one of the harshest environments on Earth: extreme heat, intense radiation, and corrosive chemicals slowly degrade every component. Developing better materials traditionally requires years of testing as damage occurs at the atomic level. Engineers create samples, expose them to reactor conditions, measure the damage, and repeat the process. The cost and duration of that cycle contribute heavily to the slow development of advanced reactors, including Small Modular Reactor projects.

Quantum algorithms could shorten that process dramatically. By modelling atomic interactions under radiation and heat, quantum computers may predict how metal alloys and ceramics will behave before they are physically produced. That capability could reduce development costs and accelerate the deployment of advanced reactor designs.

Quantum Computing Can Make Nuclear Facilities Safer and More Secure

Quantum technology also has practical applications for reactor safety and security. Inside nuclear facilities, early detection is critical. Minor changes in neutron levels, temperature, or structural integrity can determine whether a problem remains in routine maintenance or indicates a larger concern. Quantum sensors, based upon quantum computing components and which can measure radiation, magnetic fields, and temperature with great precision, may provide details that aid in safety analysis.

Security is another concern. The IAEA safeguards system — the international network of inspectors, cameras, seals, and radiation detectors designed to prevent nuclear diversion — depends on securely transmitting sensitive information. Existing encryption methods may eventually become vulnerable to sufficiently advanced quantum computers. Quantum key distribution offers a potential solution. By using quantum-based systems to transmit encryption keys, the method makes interception immediately detectable. Applying it to safeguards communications would help protect nuclear security systems against future quantum-enabled threats.

Quantum Computing Makes Fusion Possible

The most consequential application of quantum technology may involve fusion power. Fusion reactors produce no long-lived radioactive waste, do not suffer from latent core overheating accidents, and rely on hydrogen fuel. Yet fusion remains elusive because plasma behavior is extraordinarily difficult to predict and control. Scientists must contain superheated plasma using complex magnetic fields while optimizing reactor conditions in real time. These are precisely the kinds of optimization problems quantum computers are expected to manage most effectively. Projects such as Commonwealth Fusion Systems and the international ITER project are pursuing fusion on timelines measured in decades. More capable quantum simulations could shorten those timelines and help solve fusion’s remaining engineering challenges.

The evidence linking nuclear and quantum technologies is already substantial. Research projects, work at U.S. national laboratories, and IAEA initiatives on ion-beam technology all point toward deeper integration. What remains limited is institutional coordination.

What Remains to be Done?

Nuclear engineering and quantum computing laboratories still operate in isolation. Governments and funding agencies need programs that bridge the two disciplines. The IAEA and the Nuclear Energy Agency should also formally evaluate how quantum technologies can improve reactor safety, materials research, and safeguards systems.

Nuclear energy and quantum technology have been treated as separate fields for too long. They share the same scientific foundations, increasingly rely on the same tools, and confront the same technical problems. A future built on nuclear power will depend on quantum computing to design reactors, quantum sensors to monitor them, and quantum encryption to protect them. The institutions responsible for both technologies are only beginning to recognize how connected they already are.

Hira Bashir is an Associate Research Officer at the Center for International Strategic Studies, Azad Jammu & Kashmir. Her research focuses on the peaceful uses of nuclear technology. She can be reached on X at @HiraBK5090 and on LinkedIn Hira Bashir. Views expressed in this article are the author’s own. 

The Interconnection of Nuclear Energy and Quantum Technology was originally published on Global Security Review.

In his speech before the United Nations General Assembly on 8 December 1953, President Dwight Eisenhower proposed – in paraphrased terms- that the atom bomb be given to those who can “strip its military casing and adapt it to the arts of peace.” Commonly referred to as the ‘Atoms for Peace’ speech, Eisenhower’s words launched an International Atomic Energy Agency and a generation of research into nuclear energy. Since the Cold War’s end, America’s relationship with nuclear power has attracted less attention, but the artificial intelligence (AI) revolution is forcing the United States to take a “new look” at its power grid.

Throughout 2025, senators, think tanks, and federal commissions likened the pursuit of better AI to the Manhattan Project that built the bomb. The vast sums of energy required to fuel such a task, however, may need its own project. Although President Donald Trump issued an executive order to reinvigorate the nuclear industrial base last May, these energy demands have been overshadowed by mounting fascination with the need to win a technology race with China. Considering U.S. public opinion toward atomic energy reached a near record high last year, there is no better time to expand the atom’s role in support of a coherent AI strategy.

The Dawn of a Nuclear Renaissance

During the early Cold War, nuclear technology drove a revolution in energy generation, powering everything from American cities to aircraft carriers. The skyrocketing number of AI data facilities in the United States, on the other hand, represents a potential crisis in energy consumption. When asked if the country can support the growing demands of its data centers, former President of Energy at Microsoft Brian Janous responded: “No. Utilities have not experienced a period of load growth in almost two decades and are not prepared for—or even capable of matching—the speed at which AI technology is developing.” The White House is exploring nuclear options to meet this challenge, yet its AI strategy released last July only mentions nuclear power briefly on page sixteen. This point deserves more attention.

America’s 94 reactors currently supply twenty percent of its energy with 97 gigawatts (GW), and the largest of them—located in Georgia—has a generating capacity of 4.5 GW. A recent Goldman Sachs report projected that the United States needs 47 GW of additional energy to power its AI centers through 2030—the equivalent of half the country’s nuclear capacity. Meta CEO Mark Zuckerberg has taken notice. In January, he secured a series of nuclear energy deals to power his 6.6 GW AI compound under development in Ohio. Companies that did not exist twenty years ago, such as Meta and OpenAI, could soon demand more than ten percent of the nation’s power grid, and the needs are only increasing.

Professor Joohyun Moon of Dankook University suggested recently that small modular reactors (SMRs)—automobile-sized nuclear batteries—could offer energy solutions for national security purposes in forward areas, such as the Indo-Pacific. Although the United States approved its first SMR design in 2022, it will not be operational until 2029, and only three SMRs are currently active in Japan, China, and Russia. Some studies cast doubt on the affordability of SMRs and question whether they would increase the risk of proliferation given the enriched uranium they need to operate. Moreover, these reactors only generate up to 300 megawatts, so while they could be useful in certain military contingencies, their output pales in comparison to the forecasted energy demands of AI.

Microsoft alone plans to build at least six data centers in Texas, each of which might consume enough energy to power more than 100,000 homes. Once Meta completes its Ohio facilities, it will have at its disposal energy reserves capable of powering roughly five million homes. Data centers in the United States could therefore devour nearly one quarter of the energy used by all American households before 2030. Without tighter integration between a national AI strategy and America’s nuclear sector, these numbers appear unsustainable.

Reversing the Ship

Going all in on nuclear energy also requires sustainable solutions to disposing of spent nuclear fuel and investing in high-capacity pressurized water reactors, but such solutions have not been forthcoming. President Barack Obama’s administration slashed funding for Nevada’s Yucca Mountain disposal facility in 2009 and suspended development of a nuclear waste repository there. Despite the first Trump administration’s requests to fund the disposal program between 2018 and 2020, Congress has yet to approve a plan. Any rapid increase in nuclear energy must be accompanied by a commensurate spike in disposal capacity.

In addition to these concerns, the United States closed thirteen reactors between 2013 and 2022, which has encouraged the current administration to reverse course. Last year, the Department of Energy pledged to quadruple America’s nuclear output from 100 GW to 400 GW by 2050. President Trump also issued an executive order to unburden AI companies of federal regulations and requested that they shoulder the burden of energy costs. The next step is to fuse these developments with a theory of success that explains what “winning” the AI race looks like and then align that vision with the energy requirements needed to support it—much of which will be nuclear.

The Long Shadow of 1945

In her historical account of U.S. citizenship during the early atomic age, Sarah Robey explains how “American culture has never truly partitioned the difference between ‘atoms for peace’ and ‘atoms for war.’” Over the last eighty years, these blurred lines generated both hyperbolic and apathetic responses to the nation’s relationship with nuclear power. The atom became equal parts provider and destroyer, but these conversations disappeared once public fears of a Cold War going hot subsided. With American optimism toward nuclear energy now sitting at 61 percent, there is no better time to reignite the discussion about the atom’s role in American society.

Despite the Trump administrations’ efforts to break ground on new nuclear plants over the last ten years, AI theory has outpaced the long-term realities of AI application, especially regarding the energy equation. Advancing AI research will force western societies to embrace the atom for the purpose of sustaining life rather than destroying it much as Eisenhower theorized in 1953. Accepting this reality by establishing deeper connections between energy generation and AI strategy is the first step toward finding sustainable solutions to AI’s role in war and peace.

MAJ Michael P. Ferguson, U.S. Army, is an instructor in the Department of History and War Studies at the United States Military Academy and a Ph.D. student at the University of North Carolina at Chapel Hill. Specializing in early Cold War history and nuclear strategy, he has published several dozen articles and columns on a wide range of topics. His latest research appeared in the International Journal of Military History and Historiography and Texas National Security Review. The views expressed here are those of the author and do not reflect the policies or position of the U.S. Army, the U.S. Department of War, or the U.S. Government.

Learning to Love the Atom Again: Why the Future of Artificial Intelligence is Nuclear was originally published on Global Security Review.