Technical perspective: By the end of the decade, we will deliver universal, fully fault-tolerant quantum computing
By Dr. Harry Buhrman, Chief Scientist for Algorithms and Innovation, and Dr. Chris Langer, Fellow
This week, we confirm what has been implied by the rapid pace of our recent technical progress as we reveal a major acceleration in our hardware road map. By the end of the decade, our accelerated hardware roadmap will deliver a fully fault-tolerant and universal quantum computer capable of executing millions of operations on hundreds of logical qubits.
The next major milestone on our accelerated roadmap is Quantinuum Helios™, Powered by Honeywell, a device that will definitively push beyond classical capabilities in 2025. That sets us on a path to our fifth-generation system, Quantinuum Apollo™, a machine that delivers scientific advantage and a commercial tipping point this decade.
What is Apollo?
We are committed to continually advancing the capabilities of our hardware over prior generations, and Apollo makes good on that promise. It will offer:
- thousands of physical qubits
- physical error rates less than 10-4
- All of our most competitive features: all-to-all connectivity, low crosstalk, mid-circuit measurement and qubit re-use
- Conditional logic
- Real-time classical co-compute
- Physical variable angle 1 qubit and 2 qubit gates
- Hundreds of logical qubits
- Logical error rates better than 10-6 with analysis based on recent literature estimating as low as 10-10
By leveraging our all-to-all connectivity and low error rates, we expect to enjoy significant efficiency gains in terms of fault-tolerance, including single-shot error correction (which saves time) and high-rate and high-distance Quantum Error Correction (QEC) codes (which mean more logical qubits, with stronger error correction capabilities, can be made from a smaller number of physical qubits).
Studies of several efficient QEC codes already suggest we can enjoy logical error rates much lower than our target 10-6 – we may even be able to reach 10-10, which enables exploration of even more complex problems of both industrial and scientific interest.
Error correcting code exploration is only just beginning – we anticipate discoveries of even more efficient codes. As new codes are developed, Apollo will be able to accommodate them, thanks to our flexible high-fidelity architecture. The bottom line is that Apollo promises fault-tolerant quantum advantage sooner, with fewer resources.
Like all our computers, Apollo is based on the quantum charged coupled device (QCCD) architecture. Here, each qubit’s information is stored in the atomic states of a single ion. Laser beams are applied to the qubits to perform operations such as gates, initialization, and measurement. The lasers are applied to individual qubits or co-located qubit pairs in dedicated operation zones. Qubits are held in place using electromagnetic fields generated by our ion trap chip. We move the qubits around in space by dynamically changing the voltages applied to the chip. Through an alternating sequence of qubit rearrangements via movement followed by quantum operations, arbitrary circuits with arbitrary connectivity can be executed.
The ion trap chip in Apollo will host a 2D array of trapping locations. It will be fabricated using standard CMOS processing technology and controlled using standard CMOS electronics. The 2D grid architecture enables fast and scalable qubit rearrangement and quantum operations – a critical competitive advantage. The Apollo architecture is scalable to the significantly larger systems we plan to deliver in the next decade.
What is Apollo good for?
Apollo’s scaling of very stable physical qubits and native high-fidelity gates, together with our advanced error correcting and fault tolerant techniques will establish a quantum computer that can perform tasks that do not run (efficiently) on any classical computer. We already had a first glimpse of this in our recent work sampling the output of random quantum circuits on H2, where we performed 100x better than competitors who performed the same task while using 30,000x less power than a classical supercomputer. But with Apollo we will travel into uncharted territory.
The flexibility to use either thousands of qubits for shorter computations (up to 10k gates) or hundreds of qubits for longer computations (from 1 million to 1 billion gates) make Apollo a versatile machine with unprecedented quantum computational power. We expect the first application areas will be in scientific discovery; particularly the simulation of quantum systems. While this may sound academic, this is how all new material discovery begins and its value should not be understated. This era will lead to discoveries in materials science, high-temperature superconductivity, complex magnetic systems, phase transitions, and high energy physics, among other things.
In general, Apollo will advance the field of physics to new heights while we start to see the first glimmers of distinct progress in chemistry and biology. For some of these applications, users will employ Apollo in a mode where it offers thousands of qubits for relatively short computations; e.g. exploring the magnetism of materials. At other times, users may want to employ significantly longer computations for applications like chemistry or topological data analysis.
But there is more on the horizon. Carefully crafted AI models that interact seamlessly with Apollo will be able to squeeze all the “quantum juice” out and generate data that was hitherto unavailable to mankind. We anticipate using this data to further the field of AI itself, as it can be used as training data.
The era of scientific (quantum) discovery and exploration will inevitably lead to commercial value. Apollo will be the centerpiece of this commercial tipping point where use-cases will build on the value of scientific discovery and support highly innovative commercially viable products.
Very interestingly, we will uncover applications that we are currently unaware of. As is always the case with disruptive new technology, Apollo will run currently unknown use-cases and applications that will make perfect sense once we see them. We are eager to co-develop these with our customers in our unique co-creation program.
How do we get there?
Today, System Model H2 is our most advanced commercial quantum computer, providing 56 physical qubits with physical two-qubit gate errors less than 10-3. System Model H2, like all our systems, is based on the QCCD architecture.
Starting from where we are today, our roadmap progresses through two additional machines prior to Apollo. The Quantinuum Helios™ system, which we are releasing in 2025, will offer around 100 physical qubits with two-qubit gate errors less than 5x10-4. In addition to expanded qubit count and better errors, Helios makes two departures from H2. First, Helios will use 137Ba+ qubits in contrast to the 171Yb+ qubits used in our H1 and H2 systems. This change enables lower two-qubit gate errors and less complex laser systems with lower cost. Second, for the first time in a commercial system, Helios will use junction-based qubit routing. The result will be a “twice-as-good" system: Helios will offer roughly 2x more qubits with 2x lower two-qubit gate errors while operating more than 2x faster than our 56-qubit H2 system.
After Helios we will introduce Quantinuum Sol™, our first commercially available 2D-grid-based quantum computer. Sol will offer hundreds of physical qubits with two-qubit gate errors less than 2x10-4, operating approximately 2x faster than Helios. Sol being a fully 2D-grid architecture is the scalability launching point for the significant size increase planned for Apollo.
Opportunity for early value creation discovery in Helios and Sol
Thanks to Sol’s low error rates, users will be able to execute circuits with up to 10,000 quantum operations. The usefulness of Helios and Sol may be extended with a combination of quantum error detection (QED) and quantum error mitigation (QEM). For example, the [[k+2, k, 2]] iceberg code is a light-weight QED code that encodes k+2 physical qubits into k logical qubits and only uses an additional 2 ancilla qubits. This low-overhead code is well-suited for Helios and Sol because it offers the non-Clifford variable angle entangling ZZ-gate directly without the overhead of magic state distillation. The errors Iceberg fails to detect are already ~10x lower than our physical errors, and by applying a modest run-time overhead to discard detected failures, the effective error in the computation can be further reduced. Combining QED with QEM, a ~10x reduction in the effective error may be possible while maintaining run-time overhead at modest levels and below that of full-blown QEC.
Why accelerate our roadmap now?
Our new roadmap is an acceleration over what we were previously planning. The benefits of this are obvious: Apollo brings the commercial tipping point sooner than we previously thought possible. This acceleration is made possible by a set of recent breakthroughs.
First, we solved the “wiring problem”: we demonstrated that trap chip control is scalable using our novel center-to-left-right (C2LR) protocol and broadcasting shared control signals to multiple electrodes. This demonstration of qubit rearrangement in a 2D geometry marks the most advanced ion trap built, containing approximately 40 junctions. This trap was deployed to 3 different testbeds in 2 different cities and operated with 2 different collections of dual-ion-species, and all 3 cases were a success. These demonstrations showed that the footprint of the most complex parts of the trap control stay constant as the number of qubits scales up. This gives us the confidence that Sol, with approximately 100 junctions, will be a success.
Second, we continue to reduce our two-qubit physical gate errors. Today, H1 and H2 have two-qubit gate errors less than 1x10-3 across all pairs of qubits. This is the best in the industry and is a key ingredient in our record >2 million quantum volume. Our systems are the most benchmarked in the industry, and we stand by our data - making it all publicly available. Recently, we observed an 8x10-4 two-qubit gate error in our Helios development test stand in 137Ba+, and we’ve seen even better error rates in other testbeds. We are well on the path to meeting the 5x10-4 spec in Helios next year.
Third, the all-to-all connectivity offered by our systems enables highly efficient QEC codes. In Microsoft’s recent demonstration, our H2 system with 56 physical qubits was used to generate 12 logical qubits at distance 4. This work demonstrated several experiments, including repeated rounds of error correction where the error in the final result was ~10x lower than the physical circuit baseline.
In conclusion, through a combination of advances in hardware readiness and QEC, we have line-of-sight to Apollo by the end of the decade, a fully fault-tolerant quantum advantaged machine. This will be a commercial tipping point: ushering in an era of scientific discovery in physics, materials, chemistry, and more. Along the way, users will have the opportunity to discover new enabling use cases through quantum error detection and mitigation in Helios and Sol.
Quantinuum has the best quantum computers today and is on the path to offering fault-tolerant useful quantum computation by the end of the decade.
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.
- The Sanger Institute illustrates the value of quantum computing to genomics research
- Quantinuum supports developments in a field that promises to deliver a profound and positive societal impact
Twenty-five years ago, scientists accomplished a task likened to a biological moonshot: the sequencing of the entire human genome.
The Human Genome Project revealed a complete human blueprint comprising around 3 billion base pairs, the chemical building blocks of DNA. It led to breakthrough medical treatments, scientific discoveries, and a new understanding of the biological functions of our body.
Thanks to technological advances in the quarter-century since, what took 13 years and cost $2.7 billion then can now be done in under 12 minutes for a few hundred dollars. Improved instruments such as next-generation sequencers and a better understanding of the human genome – including the availability of a “reference genome” – have aided progress, alongside enormous advances in algorithms and computing power.
But even today, some genomic challenges remain so complex that they stretch beyond the capabilities of the most powerful classical computers operating in isolation. This has sparked a bold search for new computational paradigms, and in particular, quantum computing.
Quantum Challenge: Accepted
The Wellcome Leap Quantum for Bio (Q4Bio) challenge is pioneering this new frontier. The program funds research to develop quantum algorithms that can overcome current computational bottlenecks. It aims to test the classical boundaries of computational genetics in the next 3-5 years.
One consortium – led by the University of Oxford and supported by prestigious partners including the Wellcome Sanger Institute, the Universities of Cambridge, Melbourne, and Kyiv Academic University – is taking a leading role.
“The overall goal of the team’s project is to perform a range of genomic processing tasks for the most complex and variable genomes and sequences – a task that can go beyond the capabilities of current classical computers” – Wellcome Sanger Institute press release, July 2025
Selecting Quantinuum
Earlier this year, the Sanger Institute selected Quantinuum as a technology partner in their bid to succeed in the Q4Bio challenge.
Our flagship quantum computer, System H2, has for many years led the field of commercially available systems for qubit fidelity and consistently holds the global record for Quantum Volume, currently benchmarked at 8,388,608 (223).
In this collaboration, the scientific research team can take advantage of Quantinuum’s full stack approach to technology development, including hardware, software, and deep expertise in quantum algorithm development.
“We were honored to be selected by the Sanger Institute to partner in tackling some of the most complex challenges in genomics. By bringing the world’s highest performing quantum computers to this collaboration, we will help the team push the limits of genomics research with quantum algorithms and open new possibilities for health and medical science.” – Rajeeb Hazra, President and CEO of Quantinuum
Quantum for Biology
At the heart of this endeavor, the consortium has announced a bold central mission for the coming year: to encode and process an entire genome using a quantum computer. This achievement would be a potential world-first and provide evidence for quantum computing’s readiness for tackling real-world use cases.
Their chosen genome, the bacteriophage PhiX174, carries symbolic weight, as its sequencing earned Fred Sanger his second Nobel Prize for Chemistry in 1980. Successfully encoding this genome quantum mechanically would represent a significant milestone for both genomics and quantum computing.
Sooner than many expect, quantum computing may play an essential role in tackling genomic challenges at the very frontier of human health. The Sanger Institute and Quantinuum’s partnership reminds us that we may soon reach an important step forward in human health research – one that could change medicine and computational biology as dramatically as the original Human Genome Project did a quarter-century ago.
“Quantum computational biology has long inspired us at Quantinuum, as it has the potential to transform global health and empower people everywhere to lead longer, healthier, and more dignified lives.” – Ilyas Khan, Founder and Chief Product Officer of Quantinuum
Every year, The IEEE International Conference on Quantum Computing and Engineering – or IEEE Quantum Week – brings together engineers, scientists, researchers, students, and others to learn about advancements in quantum computing.
This year’s conference from August 31st – September 5th, is being held in Albuquerque, New Mexico, a burgeoning epicenter for quantum technology innovation and the home to our new location that will support ongoing collaborative efforts to advance the photonics technologies critical to furthering our product development.
Throughout IEEE Quantum Week, our quantum experts will be on-site to share insights on upgrades to our hardware, enhancements to our software stack, our path to error correction, and more.
Meet our team at Booth #507 and join the below sessions to discover how Quantinuum is forging the path to fault-tolerant quantum computing with our integrated full-stack.
September 2nd
Quantum Software Workshop
Quantum Software 2.1: Open Problems, New Ideas, and Paths to Scale
1:15 – 2:10pm MDT | Mesilla
We recently shared the details of our new software stack for our next-generation systems, including Helios (launching in 2025). Quantinuum’s Agustín Borgna will deliver a lighting talk to introduce Guppy, our new, open-source programming language based on Python, one of the most popular general-use programming languages for classical computing.
September 3rd
PAN08: Progress and Platforms in the Era of Reliable Quantum Computing
1:00 – 2:30pm MDT | Apache
We are entering the era of reliable quantum computing. Across the industry, quantum hardware and software innovators are enabling this transformation by creating reliable logical qubits and building integrated technology stacks that span the application layer, middleware and hardware. Attendees will hear about current and near-term developments from Microsoft, Quantinuum and Atom Computing. They will also gain insights into challenges and potential solutions from across the ecosystem, learn about Microsoft’s qubit-virtualization system, and get a peek into future developments from Quantinuum and Microsoft.
BOF03: Exploring Distributed Quantum Simulators on Exa-scale HPC Systems
3:00 – 4:30pm MDT | Apache
The core agenda of the session is dedicated to addressing key technical and collaborative challenges in this rapidly evolving field. Discussions will concentrate on innovative algorithm design tailored for HPC environments, the development of sophisticated hybrid frameworks that seamlessly combine classical and quantum computational resources, and the crucial task of establishing robust performance benchmarks on large-scale CPU/GPU HPC infrastructures.
September 4th
PAN11: Real-time Quantum Error Correction: Achievements and Challenges
1:00 – 2:30pm MDT | La Cienega
This panel will explore the current state of real-time quantum error correction, identifying key challenges and opportunities as we move toward large-scale, fault-tolerant systems. Real-time decoding is a multi-layered challenge involving algorithms, software, compilation, and computational hardware that must work in tandem to meet the speed, accuracy, and scalability demands of FTQC. We will examine how these challenges manifest for multi-logical qubit operations, and discuss steps needed to extend the decoding infrastructure from intermediate-scale systems to full-scale quantum processors.
September 5th
Keynote by NVIDIA
8:00 – 9:30am MDT | Kiva Auditorium
During his keynote talk, NVIDIA’s Head of Quantum Computing Product, Sam Stanwyck, will detail our partnership to fast-track commercially scalable quantum supercomputers. Discover how Quantinuum and NVIDIA are pushing the boundaries to deliver on the power of hybrid quantum and classical compute – from integrating NVIDIA’s CUDA-Q Platform with access to Quantinuum’s industry-leading hardware to the recently announced NVIDIA Quantum Research Center (NVAQC).
Featured Research at the IEEE Poster Session:
Visible Photonic Component Development for Trapped-Ion Quantum Computing
September 2nd from 6:30 - 8:00pm MDT | September 3rd from 9:30 - 10:00am MDT | September 4th from 11:30 - 12:30pm MDT
Authors: Elliot Lehman, Molly Krogstad, Molly P. Andersen, Sara Cambell, Kirk Cook, Bryan DeBono, Christopher Ertsgaard, Azure Hansen, Duc Nguyen, Adam Ollanik, Daniel Ouellette, Michael Plascak, Justin T. Schultz, Johanna Zultak, Nicholas Boynton, Christopher DeRose,Michael Gehl, and Nicholas Karl
Scaling Up Trapped-Ion Quantum Processors with Integrated Photonics
September 2nd from 6:30 - 8:00pm MDT and 2:30 - 3:00pm MDT | September 4th from 9:30 - 10:00am MDT
Authors: Molly Andersen, Bryan DeBono, Sara Campbell, Kirk Cook, David Gaudiosi, Christopher Ertsgaard, Azure Hansen, Todd Klein, Molly Krogstad, Elliot Lehman, Gregory MacCabe, Duc Nguyen, Nhung Nguyen, Adam Ollanik, Daniel Ouellette, Brendan Paver, Michael Plascak, Justin Schultz and Johanna Zultak
Research Collaborations with the Local Ecosystem
In a partnership that is part of a long-standing relationship with Los Alamos National Laboratory, we have been working on new methods to make quantum computing operations more efficient, and ultimately, scalable.
Learn more in our Research Paper: Classical shadows with symmetries
Our teams collaborated with Sandia National Laboratories demonstrating our leadership in benchmarking. In this paper, we implemented a technique devised by researchers at Sandia to measure errors in mid-circuit measurement and reset. Understanding these errors helps us to reduce them while helping our customers understand what to expect while using our hardware.
Learn more in our Research Paper: Measuring error rates of mid-circuit measurements
From machine learning to quantum physics, tensor networks have been quietly powering the breakthroughs that will reshape our society. Originally developed by the legendary Nobel laureate Roger Penrose, they were first used to tackle esoteric problems in physics that were previously unsolvable.
Today, tensor networks have become indispensable in a huge number of fields, including both classical and quantum computing, where they are used everywhere from quantum error correction (QEC) decoding to quantum machine learning.
In this latest paper, we teamed up with luminaries from the University of British Columbia, California Institute of Technology, University of Jyväskylä, KBR Inc, NASA, Google Quantum AI, NVIDIA, JPMorgan Chase, the University of Sherbrooke, and Terra Quantum AG to provide a comprehensive overview of the use of tensor networks in quantum computing.
Standing on the shoulders of giants
Part of what drives our leadership in quantum computing is our commitment to building the best scientific team in the world. This is precisely why we hired Dr. Reza Haghshenas, one of the world’s leading experts in tensor networks, and a co-author on the paper.
Dr. Haghshenas has been researching tensor networks for over a decade across both academia and industry. Dr. Haghshenas did postdoctoral work under Professor Garnet Chan at Caltech, a leading figure in the use of tensor networks for quantum physics and chemistry.
“Working with Dr. Garnet Chan at Caltech was a formative experience for me”, remarked Dr. Haghshenas. “While there, I contributed to the development of quantum simulation algorithms and advanced classical methods like tensor networks to help interpret and simulate many-body physics.”
Since joining Quantinuum, Dr. Haghshenas has led projects that bring tensor network methods into direct collaboration with experimental hardware teams — exploring quantum magnetism on real quantum devices and helping demonstrate early signs of quantum advantage. He also contributes to widely used simulation tools like QUIMB, helping the broader research community access these methods.
Dr. Haghshenas’ work sits in a broad and vibrant ecosystem exploring novel uses of tensor networks. Collaborations with researchers like Dr. Chan at Caltech, and NVIDIA have brought GPU-accelerated tools to bear on the forefront of applying tensor networks to quantum chemistry, quantum physics, and quantum computing.
A powerful simulation tool
Of particular interest to those of us in quantum computing, the best methods (that we know of) for simulating quantum computers with classical computers rely on tensor networks. Tensor networks provide a nice way of representing the entanglement in a quantum algorithm and how it spreads, which is crucial but generally quite difficult for classical algorithms. In fact, it’s partly tensor networks’ ability to represent entanglement that makes them so powerful for quantum simulation. Importantly, it is our in-house expertise with tensor networks that makes us confident we are indeed moving past classical capabilities.
A theory of evolution
Tensor networks are not only crucial to cutting-edge simulation techniques. At Quantinuum, we're working on understanding and implementing quantum versions of classical tensor network algorithms, from quantum matrix product states to holographic simulation methods. In doing this, we are leveraging decades of classical algorithm development to advance quantum computing.
A topic of growing interest is the role of tensor networks in QEC, particularly in a process known as decoding. QEC works by encoding information into an entangled state of multiple qubits and using syndrome measurements to detect errors. These measurements must then be decoded to identify the specific error and determine the appropriate correction. This decoding step is challenging—it must be both fast (within the qubit’s coherence time) and accurate (correctly identifying and fixing errors). Tensor networks are emerging as one of the most effective tools for tackling this task.
Looking forward (and backwards, and sideways...)
Tensor networks are more than just a powerful computational tool — they are a bridge between classical and quantum thinking. As this new paper shows, the community’s understanding of tensor networks has matured into a robust foundation for advancing quantum computing, touching everything from simulation and machine learning to error correction and circuit design.
At Quantinuum, we see this as an evolutionary step, not just in theory, but in practice. By collaborating with top minds across academia and industry, we're charting a path forward that builds on decades of classical progress while embracing the full potential of quantum mechanics. This transition is not only conceptual but algorithmic, advancing how we formulate and implement methods utilizing efficiently both classical and quantum computing. Tensor networks aren’t just helping us keep pace with classical computing; they’re helping us to transcend it.