How a New Quantum Algorithm Could Help Solve Real-world Problems Sooner
Researchers at Honeywell Quantum Solutions demonstrated their new algorithm can accurately simulate a scientific model with fewer qubits than previously required
An algorithm developed by researchers at Honeywell Quantum Solutions could lead to quantum computers running more complex scientific simulations sooner than expected.
The Honeywell team recently demonstrated that its holographic quantum dynamics (holoQUADS) algorithm accurately simulated a quantum dynamics model with fewer qubits than traditional methods. The algorithm used nine qubits to simulate 32 “spins” – or localized electrons. Traditional methods require one qubit per spin.
The demonstration, led by Eli Chertkov, has important implications. Simulating quantum dynamics is a promising application for quantum computers. However, many predict quantum computers will need hundreds or thousands of qubits to run simulations too complex for classical computers.
The holoQUADS algorithm could change that.
“This algorithm allows us to run more complex simulations with less than a third of the qubits,” said Tony Uttley, president of Honeywell Quantum Solutions. “This is an exciting achievement that gets us closer to quantum computers solving real-world problems that classical computers cannot.”
Borrowed from the classical world
Scientists have long sought to better understand how atoms and subatomic particles move, behave, and interact (known as quantum mechanics) and react when disturbed (quantum dynamics).
Such knowledge is critical to the development of new vaccines and gene therapies, and the discovery of novel materials that are stronger, longer lasting, or better conductors of heat or electricity.
Currently, it is impossible to fully simulate the quantum dynamics of systems larger than a few atoms, and many believe it always will be. Classical computers crunch data by manipulating ones and zeroes and represent states as “off” or “on.” Atoms and subatomic particle exist in multiple states and move and behave in different ways.
This is what led to famed American physicist Richard Feynman postulating in the 1980s that only computers that are quantum in nature can adequately simulate quantum dynamics.
That is not to say computational scientists do not have tricks to model some aspects of quantum dynamics on classical computers. They have developed powerful algorithms such as tensor networks to approximate quantum states.
In fact, the holoQUADS algorithm is based on tensor networks. These mathematical tools compress data and scientists use them to study the quantum nature of different materials.
The Honeywell team published a paper last May detailing the steps necessary to adapt tensor networks for a quantum computer and how to extend them to simulate dynamics. They published a second paper explaining how quantum tensor networks can measure the degree to which parts of a system are entangled, or entanglement entropy, which is used for studying topological properties of materials.
The recent demonstration showed the dynamics algorithm described in the original paper is not only efficient but can return quantitatively accurate results with trapped-ion hardware available right now.
Tested and verified
The Honeywell team tested the algorithm by simulating the chaotic dynamics of the “kicked” Ising model, a mathematical framework used to study chaos and thermalization in strongly interacting quantum systems. The results mirrored those generated by simulations on classical computers.
The demonstration served as an important benchmark and will help the team verify performance and accuracy as they scale the algorithm and quantum hardware.
“The model we simulated is a perfect test of the algorithm because it behaves in many ways like a typical chaotic quantum system, but it has a very special feature that lets us check the results classically,” said Dr. Michael Foss-Feig, a physicist who helped develop the algorithm.
Chertkov, Foss-Feig, and the other co-authors are excited by how well the algorithm worked in the real world, and by the performance of the System Model H1. The algorithm relies on mid-circuit measurement and qubit reuse, techniques first demonstrated by Honeywell. The H1 is adept at both. And because of the H1’s high fidelities, the raw data had less “noise” than other state-of-the art simulations.
“The QCCD architecture at the heart of System Model H1 enables high-fidelity qubit reset and mid-circuit measurements with very low crosstalk errors,” said Justin Bohnet, one of the co-authors who led the hardware team. “Those features, along with the long coherence times and high-fidelity gates provided by trapped-ion qubits, are enabling creative advances in the study of quantum systems, as shown by this the holoQUADS demonstration.”
About Quantinuum
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.
Quantinuum is focusing on redefining what’s possible in hybrid quantum–classical computing by integrating Quantinuum’s best-in-class systems with high-performance NVIDIA accelerated computing to create powerful new architectures that can solve the world’s most pressing challenges.
The launch of Helios, Powered by Honeywell, the world’s most accurate quantum computer, marks a major milestone in quantum computing. Helios is now available to all customers through the cloud or on-premise deployment, launched with a go-to-market offering that seamlessly pairs Helios with the NVIDIA Grace Blackwell platform, targeting specific end markets such as drug discovery, finance, materials science, and advanced AI research.
We are also working with NVIDIA to adopt NVIDIA NVQLink, an open system architecture, as a standard for advancing hybrid quantum-classical supercomputing. Using this technology with Quantinuum Guppy and the NVIDIA CUDA-Q platform, Quantinuum has implemented NVIDIA accelerated computing across Helios and future systems to perform real-time decoding for quantum error correction.
In an industry-first demonstration, an NVIDIA GPU-based decoder integrated in the Helios control engine improved the logical fidelity of quantum operations by more than 3% — a notable gain given Helios’ already exceptionally low error rate. These results demonstrate how integration with NVIDIA accelerated computing through NVQLink can directly enhance the accuracy and scalability of quantum computation.
This unique collaboration spans the full Quantinuum technology stack. Quantinuum’s next-generation software development environment allows users to interleave quantum and GPU-accelerated classical computations in a single workflow. Developers can build hybrid applications using tools such as NVIDIA CUDA-Q, NVIDIA CUDA-QX, and Quantinuum’s Guppy, to make advanced quantum programming accessible to a broad community of innovators.
The collaboration also reaches into applied research through the NVIDIA Accelerated Quantum Computing Research Center (NVAQC), where an NVIDIA GB200 NVL72 supercomputer can be paired with Quantinuum’s Helios to further drive hybrid quantum-GPU research, including the development of breakthrough quantum-enhanced AI applications.
A recent achievement illustrates this potential: The ADAPT-GQE framework, a transformer-based Generative Quantum AI (GenQAI) approach, uses a Generative AI model to efficiently synthesize circuits to prepare the ground state of a chemical system on a quantum computer. Developed by Quantinuum, NVIDIA, and a pharmaceutical industry leader—and leveraging NVIDIA CUDA-Q with GPU-accelerated methods—ADAPT-GQE achieved a 234x speed-up in generating training data for complex molecules. The team used the framework to explore imipramine, a molecule crucial to pharmaceutical development. The transformer was trained on imipramine conformers to synthesize ground state circuits at orders of magnitude faster than ADAPT-VQE, and the circuit produced by the transformer was run on Helios to prepare the ground state using InQuanto, Quantinuum's computational chemistry platform.
From collaborating on hardware and software integrations to GenQAI applications, the collaboration between Quantinuum and NVIDIA is building the bridge between classical and quantum computing and creating a future where AI becomes more expansive through quantum computing, and quantum computing becomes more powerful through AI.
By Dr. Noah Berthusen
The earliest works on quantum error correction showed that by combining many noisy physical qubits into a complex entangled state called a "logical qubit," this state could survive for arbitrarily long times. QEC researchers devote much effort to hunt for codes that function well as "quantum memories," as they are called. Many promising code families have been found, but this is only half of the story.
Being able to keep a qubit around for a long time is one thing, but to realize the theoretical advantages of quantum computing we need to run quantum circuits. And to make sure noise doesn't ruin our computation, these circuits need to be run on the logical qubits of our code. This is often much more challenging than performing gates on the physical qubits of our device, as these "logical gates" often require many physical operations in their implementation. What's more, it often is not immediately obvious which logical gates a code has, and so converting a physical circuit into a logical circuit can be rather difficult.
Some codes, like the famous surface code, are good quantum memories and also have easy logical gates. The drawback is that the ratio of physical qubits to logical qubits (the "encoding rate") is low, and so many physical qubits are required to implement large logical algorithms. High-rate codes that are good quantum memories have also been found, but computing on them is much more difficult. The holy grail of QEC, so to speak, would be a high-rate code that is a good quantum memory and also has easy logical gates. Here, we make progress on that front by developing a new code with those properties.
Building on prior error correcting codes
A recent work from Quantinuum QEC researchers introduced genon codes. The underlying construction method for these codes, called the "symplectic double cover," also provided a way to obtain logical gates that are well suited for Quantinuum's QCCD architecture. Namely, these "SWAP-transversal" gates are performed by applying single qubit operations and relabeling the physical qubits of the device. Thanks to the all-to-all connectivity facilitated through qubit movement on the QCCD architecture, this relabeling can be done in software essentially for free. Combined with extremely high fidelity (~1.2 x10-5) single-qubit operations, the resulting logical gates are similarly high fidelity.
Given the promise of these codes, we take them a step further in our new paper. We combine the symplectic double codes with the [[4,2,2]] Iceberg code using a procedure called "code concatenation". A concatenated code is a bit like nesting dolls, with an outer code containing codes within it---with these too potentially containing codes. More technically, in a concatenated code the logical qubits of one code act as the physical qubits of another code.
The new codes, which we call "concatenated symplectic double codes", were designed in such a way that they have many of these easily-implementable SWAP-transversal gates. Central to its construction, we show how the concatenation method allows us to "upgrade" logical gates in terms of their ease of implementation; this procedure may provide insights for constructing other codes with convenient logical gates. Notably, the SWAP-transversal gate set on this code is so powerful that only two additional operations (logical T and S) are necessary for universal computation. Furthermore, these codes have many logical qubits, and we also present numerical evidence to suggest that they are good quantum memories.
Concatenated symplectic double codes have one of the easiest logical computation schemes, and we didn’t have to sacrifice rate to achieve it. Looking forward in our roadmap, we are targeting hundreds of logical qubits at ~ 1x 10-8 logical error rate by 2029. These codes put us in a prime position to leverage the best characteristics of our hardware and create a device that can achieve real commercial advantage.
Every year, the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) brings together the global supercomputing community to explore the technologies driving the future of computing.
At this year’s conference, from November 16th – 21st in St. Louis, Missouri, Quantinuum showcased how our quantum hardware, software, and partnerships are helping define the next era of high-performance and quantum computing.
Quantinuum in the Expo Hall
The Quantinuum team was on-site at booth #4432 to showcase how we’re building the bridge between HPC and quantum. Folks stopped by our booth to see:
- Live demo unit of our quantum hardware
- Our new Helios replica, providing an up-close look at the design behind our next-generation system
- The Helios chip, highlighting the innovation driving the world’s most advanced trapped-ion quantum computers
Our quantum computing experts hosted daily tutorials at our booth on Helios, our next-generation hardware platform, Nexus, our all-in-one quantum computing platform, and Hybrid Workflows, featuring the integration of NVIDIA CUDA-Q with Quantinuum Systems.
Speaking Sessions at SC25
Join our team as they share insights on the opportunities and challenges of quantum integration within the HPC ecosystem:
Panel Session: The Quantum Era of HPC: Roadmaps, Challenges and Opportunities in Navigating the Integration Frontier
November 19th | 10:30 – 12:00pm CST
During this panel session, Kentaro Yamamoto from Quantinuum, will join experts from Lawrence Berkeley National Laboratory, IBM, QuEra, RIKEN, and Pawsey Supercomputing Research Centre to explore how quantum and classical systems are being brought together to accelerate scientific discovery and industrial innovation.
BoF Session: Bridging the Gap: Making Quantum-Classical Hybridization Work in HPC
November 19th | 5:15 – 6:45pm CST
Quantum-classical hybrid computing is moving from theory to reality, yet no clear roadmap exists for how best to integrate quantum processing units (QPUs) into established HPC environments. In this Birds of a Feather discussion, co-led by Quantinuum’s Grahame Vittorini and representatives from BCS, DOE, EPCC, Inria, ORNL NVIDIA, and RIKEN we hope to bring together a global community of HPC practitioners, system architects, quantum computing specialists and workflow researchers, including participants in the Workflow Community Initiative, to assess the state of hybrid integration and identify practical steps toward scalable, impactful deployment.