For more than two decades, there has been a general consensus among physicists pursuing the development of universal, fault-tolerant quantum computers that non-Abelian topological states would offer a promising path to success, if the states could ever be created.
These states host exotic quasi-particles—called anyons—that allow the storage of quantum information in their internal states which can only be changed by "braiding" them around each other in spacetime. Small perturbations in the trajectory of these braids would then leave the topology of the braid unchanged, making this paradigm inherently robust. It is as if they are ‘deaf’ to the noise of a system.
The problem however, is that non-Abelian anyons have never yet been detected, much less controlled.
Until now.
Now, Quantinuum scientists, in collaboration with researchers from Harvard University and Caltech, have turned years of theory regarding topological states into reality, using the unique capabilities of the new H2 trapped-ion processor to create and control non-Abelian anyons. Using a shallow adaptive circuit on the H2, the research team prepared a non-Abelian quantum state on 27 qubits with a fidelity per site exceeding 98.4%.
This demonstration hinges on crucial advances in theory and experiment. On the theory side, Dr. Ruben Verresen, Prof. Ashvin Vishwanath (Harvard) and Dr. Nathanan Tantivasadakarn (Caltech) have shown how to use mid-circuit measurement to significantly simplify the route towards this kind of non-Abelian state. On the experimental side, the increased qubit capacity of the H2 system allows for sufficient complexity to create collective non-Abelian particles, while keeping the extremely low gate and mid-circuit measurement errors of previous generations.
The achievement has set the stage for an accelerated path to fault-tolerant quantum computing while also paving the way for new fields of research within condensed matter physics and high-energy physics.
The paper documenting the research, "Creation of Non-Abelian Topological Order and Anyons on a Trapped-Ion Processor," is posted in Nature. This research was one of several papers published at the launch of H2, the next generation in Quantinuum's H-Series quantum computer, Powered by Honeywell.
Quantinuum has been advancing this area of research in “stealth mode” for some considerable time.
Ilyas Khan, Quantinuum’s Chief Product Officer said "I recall vividly discussing topological quantum computing with Henrik 7 years ago during a long hot summer when devices such as our H2 processor were hard to even dream about. This research represents a milestone that benefits the industry as a whole and yet again demonstrates our ability to not only be world leaders today but also long into the future.”
"Topological order is our best shot at creating a quantum computer with very low error rates," Henrik said. "We need to be able to operate on the system while keeping it protected from the environment," he said. "Topological order can offer that protection. This research demonstrates that the more exotic kind of topological state, the non-Abelian kind, can be created with today's devices on-demand and with high fidelity. One of next steps will be to demonstrate stability by repetitive error-correction, utilizing the same ingredients used to prepare the state in the first place."
According to Tony Uttley, President and COO of Quantinuum, this advance represents a breakaway moment for Quantinuum.
"We've reached a point with our technology that we can build a quantum computer on top of a quantum computer," Tony said. "These non-Abelian topological qubits can layer on top of physical qubits without changing how our quantum computer operates. That accomplishment will accelerate our work on the path to fault-tolerant quantum computing."
Quantinuum, the world’s largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. Quantinuum’s technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With over 500 employees, including 370+ scientists and engineers, Quantinuum leads the quantum computing revolution across continents.
At the heart of quantum computing’s promise lies the ability to solve problems that are fundamentally out of reach for classical computers. One of the most powerful ways to unlock that promise is through a novel approach we call Generative Quantum AI, or GenQAI. A key element of this approach is the Generative Quantum Eigensolver (GQE).
GenQAI is based on a simple but powerful idea: combine the unique capabilities of quantum hardware with the flexibility and intelligence of AI. By using quantum systems to generate data, and then using AI to learn from and guide the generation of more data, we can create a powerful feedback loop that enables breakthroughs in diverse fields.
Unlike classical systems, our quantum processing unit (QPU) produces data that is extremely difficult, if not impossible, to generate classically. That gives us a unique edge: we’re not just feeding an AI more text from the internet; we’re giving it new and valuable data that can’t be obtained anywhere else.
One of the most compelling challenges in quantum chemistry and materials science is computing the properties of a molecule’s ground state. For any given molecule or material, the ground state is its lowest energy configuration. Understanding this state is essential for understanding molecular behavior and designing new drugs or materials.
The problem is that accurately computing this state for anything but the simplest systems is incredibly complicated. You cannot even do it by brute force—testing every possible state and measuring its energy—because the number of quantum states grows as a double-exponential, making this an ineffective solution. This illustrates the need for an intelligent way to search for the ground state energy and other molecular properties.
That’s where GQE comes in. GQE is a methodology that uses data from our quantum computers to train a transformer. The transformer then proposes promising trial quantum circuits; ones likely to prepare states with low energy. You can think of it as an AI-guided search engine for ground states. The novelty is in how our transformer is trained from scratch using data generated on our hardware.
Here's how it works:
To test our system, we tackled a benchmark problem: finding the ground state energy of the hydrogen molecule (H₂). This is a problem with a known solution, which allows us to verify that our setup works as intended. As a result, our GQE system successfully found the ground state to within chemical accuracy.
To our knowledge, we’re the first to solve this problem using a combination of a QPU and a transformer, marking the beginning of a new era in computational chemistry.
The idea of using a generative model guided by quantum measurements can be extended to a whole class of problems—from combinatorial optimization to materials discovery, and potentially, even drug design.
By combining the power of quantum computing and AI we can unlock their unified full power. Our quantum processors can generate rich data that was previously unobtainable. Then, an AI can learn from that data. Together, they can tackle problems neither could solve alone.
This is just the beginning. We’re already looking at applying GQE to more complex molecules—ones that can’t currently be solved with existing methods, and we’re exploring how this methodology could be extended to real-world use cases. This opens many new doors in chemistry, and we are excited to see what comes next.
Last year, we joined forces with RIKEN, Japan's largest comprehensive research institution, to install our hardware at RIKEN’s campus in Wako, Saitama. This deployment is part of RIKEN’s project to build a quantum-HPC hybrid platform consisting of high-performance computing systems, such as the supercomputer Fugaku and Quantinuum Systems.
Today, a paper published in Physical Review Research marks the first of many breakthroughs coming from this international supercomputing partnership. The team from RIKEN and Quantinuum joined up with researchers from Keio University to show that quantum information can be delocalized (scrambled) using a quantum circuit modeled after periodically driven systems.
"Scrambling" of quantum information happens in many quantum systems, from those found in complex materials to black holes. Understanding information scrambling will help researchers better understand things like thermalization and chaos, both of which have wide reaching implications.
To visualize scrambling, imagine a set of particles (say bits in a memory), where one particle holds specific information that you want to know. As time marches on, the quantum information will spread out across the other bits, making it harder and harder to recover the original information from local (few-bit) measurements.
While many classical techniques exist for studying complex scrambling dynamics, quantum computing has been known as a promising tool for these types of studies, due to its inherently quantum nature and ease with implementing quantum elements like entanglement. The joint team proved that to be true with their latest result, which shows that not only can scrambling states be generated on a quantum computer, but that they behave as expected and are ripe for further study.
Thanks to this new understanding, we now know that the preparation, verification, and application of a scrambling state, a key quantum information state, can be consistently realized using currently available quantum computers. Read the paper here, and read more about our partnership with RIKEN here.
In our increasingly connected, data-driven world, cybersecurity threats are more frequent and sophisticated than ever. To safeguard modern life, government and business leaders are turning to quantum randomness.
The term to know: quantum random number generators (QRNGs).
QRNGs exploit quantum mechanics to generate truly random numbers, providing the highest level of cryptographic security. This supports, among many things:
Quantum technologies, including QRNGs, could protect up to $1 trillion in digital assets annually, according to a recent report by the World Economic Forum and Accenture.
The World Economic Forum report identifies five industry groups where QRNGs offer high business value and clear commercialization potential within the next few years. Those include:
In line with these trends, recent research by The Quantum Insider projects the quantum security market will grow from approximately $0.7 billion today to $10 billion by 2030.
Quantum randomness is already being deployed commercially:
Recognizing the value of QRNGs, the financial services sector is accelerating its path to commercialization.
On the basis of the latter achievement, we aim to broaden our cybersecurity portfolio with the addition of a certified randomness product in 2025.
The National Institute of Standards and Technology (NIST) defines the cryptographic regulations used in the U.S. and other countries.
This week, we announced Quantum Origin received NIST SP 800-90B Entropy Source validation, marking the first software QRNG approved for use in regulated industries.
This means Quantum Origin is now available for high-security cryptographic systems and integrates seamlessly with NIST-approved solutions without requiring recertification.
The NIST validation, combined with our peer-reviewed papers, further establishes Quantum Origin as the leading QRNG on the market.
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It is paramount for governments, commercial enterprises, and critical infrastructure to stay ahead of evolving cybersecurity threats to maintain societal and economic security.
Quantinuum delivers the highest quality quantum randomness, enabling our customers to confront the most advanced cybersecurity challenges present today.