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.
- Mitsui & Co. and Mitsubishi Electric demonstrated one of the world’s largest approximate Quantum Fourier Transforms (QFT) on Quantinuum Helios, scaling from prior records to 98 physical qubits.
- The collaboration also implemented a logical QFT using a QEC (Quantum Error Correction) code with up to 12 logical qubits.
- The work highlights Quantinuum’s accuracy and flexible architecture.
While there is ongoing debate around the pace of quantum computing’s development, a more grounded way to assess progress is through concrete demonstrations of foundational algorithms at meaningful scale. In this context, Mitsui & Co. and Mitsubishi Electric are taking a pragmatic view of quantum progress—focusing on how close the field is to executing core algorithmic primitives that underpin many potential industrial applications, rather than relying on abstract milestones or timelines.
In a new white paper, the industrial giants teamed up with Quantinuum to measure how close we are to running the Quantum Fourier Transform (QFT), a widely-used algorithmic primitive, at scales necessary for industrial applications. In the process, the team successfully ran one of the largest instances of the approximate QFT ever demonstrated. This achievement matters because the QFT is an essential primitive that underpins many of the quantum algorithms expected to deliver practical advantages.
You may have heard of the (classical) Fourier transform (FT), due to its ubiquity throughout modern computing. The FT is essential in everything from image analysis to data compression, with almost limitless applications in between. The quantum Fourier transform (QFT) is similar; it’s used in everything from chemistry to finance.
Because the QFT is a foundational primitive underpinning many quantum algorithms, demonstrating it at larger scales and higher fidelity is a practical way to measure quantum computing readiness. This is exactly the type of benchmarking that organizations should consider to understand where today’s systems are useful, and to see how fault-tolerant approaches are progressing. Ultimately, algorithm-level benchmarking like this is one of the most useful ways to understand not just where we are, but where we are going.
A Transformative Approach
Primitives like Fourier Transform are so widespread because they simplify problems by transforming them into something that is easier to deal with. At Quantinuum, we are very interested in transforms: not only are they crucial for industrial applications but they can also simplify algorithms, making them possible to run now instead of later. This ‘transformational’ approach extends beyond the QFT - other transforms exist, and we have even invented our own quantum-native transforms.
Using our Helios quantum computer and Guppy language, the joint team explored running the QFT on both physical qubits and on logical qubits, showing that fault tolerance is progressing quickly. Running the QFT on 98 physical qubits; the paper shows a clear progression from previous results.
Then, using the Steane code, one of the best-studied quantum error correcting codes, the team used Helios’ 98 physical qubits to form 12 logical qubits, successfully running the QFT with the mechanisms of quantum error correction interwoven into the algorithm. This marks a crucial step forward for the field.
Foundational Progress
Taken together, these results provide a more concrete lens through which to view progress in quantum computing: not as abstract projections, but as measurable advances in the execution of foundational algorithms at increasing scale. By benchmarking the Quantum Fourier Transform on both physical and logical qubits, Mitsui & Co. and Mitsubishi Electric are helping to clarify what today’s hardware can already achieve, and where fault-tolerant approaches begin to extend those limits.
More broadly, the organizations best positioned to benefit from quantum computing will be those that focus on these foundational capabilities early, and use them to build a clear, evidence-based understanding of how the technology fits into their business goals.
It is believed that unlocking answers to some of the most complex scientific and industrial problems will require the seamless integration of high-performance computing (HPC), generative AI (GenAI), and quantum computing. Toward this goal, Quantinuum, NVIDIA, and a major pharmaceutical company have successfully demonstrated the first step in a proof-of-principle framework designed to connect these three distinct computing paradigms for industrially relevant computational chemistry.
This milestone, enabled by three industry leaders and experts in their respective domains, serves as a foundational capability that could support the development of future hybrid quantum-AI workflows to help optimize industrial research and development (R&D).
The potential value is a path toward more automated, repeatable, and scalable workflows for translating chemistry problems into executable quantum programs—capabilities that could eventually make hybrid computing easier to deploy in industrial R&D.
The GenQAI Framework
The framework, termed Generative Quantum AI (GenQAI), involved a quantum computer simulating a pharmaceutical compound using programming instructions generated by an AI model, which itself was trained on quantum data that was simulated using HPC.
While the vision for GenQAI explores how future industrial simulation workflows might be optimized by training AI models using quantum data derived directly from a quantum computer, the framework currently consists of four main technical steps:
- Simulating quantum data: The process began by simulating quantum data with NVIDIA accelerated computing using NVIDIA CUDA-Q.
- Fine-tuning the AI: This simulated quantum data was used to fine-tune a pre-trained AI model from the open NVIDIA Nemotron family.
- Generating instructions: The AI model then generated quantum circuits, which are the programming instructions required for the quantum simulation.
- Validating accuracy: To validate the results, the circuits were run on Quantinuum’s Helios quantum computer using its InQuanto quantum chemistry platform.
The core novelty of this development lies within the process of the framework itself. In this proof-of-principle experiment, an AI model fine-tuned on simulated quantum data generated circuits that were successfully executed and validated on Quantinuum’s Helios system. Rather than delivering an immediate commercial advantage, this achievement establishes a credible, verifiable baseline for how HPC, AI, and quantum computing can function in tandem.
The Case Study
The validation of the framework represents an early step toward the goal of developing scalable architectures for the pharmaceutical industry.
With a shared view toward eventually scaling the framework for pharmaceutical R&D applications, the researchers simulated a pharmaceutical compound: imipramine. This anti-depressant was chosen because it serves as a model compound for drug degradation and shelf-life studies, which are standard components of the pharmaceutical R&D lifecycle.
Developing hybrid infrastructure that enterprises may adopt requires a deep, coordinated effort among domain experts. As such, this successful test highlights the value of combining the strengths of a quantum computing hardware and software leader (Quantinuum), with a hybrid-quantum classical platform (NVIDIA), and a leading enterprise end-user to build and test future computing capabilities for industrial chemistry.
Scaling the Framework
Although demonstrated on a pharmaceutical compound, the architecture could eventually inform similar molecular-simulation workflows in sectors such as energy, agriculture, advanced materials, and electronics. At this stage, it provides a reference for further testing and development.
Engage Further
Access the paper here to explore the full technical details of this demonstration and contact our team to learn more about joining Quantinuum’s enterprise partner network.
Quantinuum is pleased to announce that applications are now open for the Quantinuum SG Grand Challenge 2026, a global innovation challenge designed to bring together researchers, developers, scientists and innovators to explore practical applications of quantum computing.
Organized by Quantinuum and supported by Singapore's National Quantum Office and Aqora, the three-month program aims to foster collaboration across academia, industry and the quantum developer community while supporting the continued growth of Singapore's quantum ecosystem.
A Platform for Quantum Computing Innovation
Participants will work in teams to develop solutions across a range of challenge areas, including chemistry and molecular simulation, optimization, AI for quantum systems, quantum error correction, condensed matter and materials science, and open innovation. Throughout the program, participants will have access to mentoring, technical enablement and Quantinuum quantum computing resources.
Singapore Grand Finale
Selected finalist teams will be invited to present their work at the Grand Finale hosted in Singapore before representatives from academia, and industry. The event will celebrate innovative applications of quantum computing while providing an opportunity for participants to engage with Singapore's growing quantum community.
Join the Challenge
The Quantinuum SG Grand Challenge welcomes participants from around the world. Whether you are an experienced quantum researcher or beginning your quantum computing journey, the program offers an opportunity to collaborate, learn and contribute to the development of practical quantum applications.
Applications are now open. Spaces are limited and subject to review and approval.