Quantinuum extends its significant lead in quantum computing, achieving historic milestones for hardware fidelity and Quantum Volume
Quantinuum has raised the bar for the global ecosystem by achieving the historic and much-vaunted “three 9's” 2-qubit gate fidelity in its commercial quantum computer and announcing that its Quantum Volume has surpassed one million – exponentially higher than its nearest competitors.
By Ilyas Khan, Founder and Chief Product Officer, Jenni Strabley, Sr Director of Offering Management
All quantum error correction schemes depend for their success on physical hardware achieving high enough fidelity. If there are too many errors in the physical qubit operations, the error correcting code has the effect of amplifying rather than diminishing overall error rates. For decades now, it has been hoped that one day a quantum computer would achieve “three 9's” – an iconic, inherent 99.9% 2-qubit physical gate fidelity – at which point many of the error-correcting codes required for universal fault tolerant quantum computing would successfully be able to squeeze errors out of the system.
That day has now arrived. Building on several previous laboratory demonstrations 1 2 3, Quantinuum has become the first company ever to achieve “three 9's” in a commercially-available quantum computer, with the first demonstration of 99.914(3)% 2-qubit gate fidelity, showing repeatable performance across all qubit pairs on our H1-1 system that is constantly available to customers. This production-environment announcement is a marked difference to one-offs recorded in carefully contrived laboratory conditions. This demonstrates what will fast become the expected standard for the entire quantum computing sector.
Quantinuum is also announcing another milestone, a seven-figure Quantum Volume (QV) of 1,048,576 – or in terms preferred by the experts, 220 – reinforcing our commitment to building, by a significant margin, the highest-performing quantum computers in the world.
These announcements follow a historic month that started when we proved our ability to scale our systems to the sizes needed to solve some of the world’s most pressing problems – and in a way that offers the best path to universal quantum computing.
On March 5th, 2024, Quantinuum researchers disclosed details of our experiments that provide a solution to a totemic problem faced by all quantum computing architectures, known as the wiring problem. Supported by a video showing qubits being shuffled through a 2-dimensional grid ion-trap, our team presented concrete proof of the scalability of the quantum charge-coupled device (QCCD) architecture used in our H-Series quantum computers.
Stop-motion ion transport video showing a chosen sorting operation implemented on an 8-site 2D grid trap with the swap-or-stay primitive. The sort is implemented by discrete choices of swaps or stays between neighboring sites. The numbers shown (indicated by dashed circles) at the beginning and end of the video show the initial and final location of the ions after the sort, e.g. the ion that starts at the top left site ends at the bottom right site. The stop-motion video was collected by segmenting the primitive operation and pausing mid-operation such that Yb fluorescence could be detected with a CMOS camera exposure.
On April 3rd, 2024 in partnership with Microsoft, our teams announced a breakthrough in quantum error correction that delivered as its crowning achievement the most reliable logical qubits on record.
We revealed detailed demonstrations in an arXiv pre-print paper of the reliability achieved via 4 logical qubits encoded into just 30 physical qubits on our System Model H2 quantum computer. Our joint teams were able to demonstrate logical circuit error rates far below physical circuit error rates, a capability that our full-stack quantum computer is currently the only one in the world with the fidelity required to achieve.
Explaining the importance of 2-qubit gate fidelity
Reaching this level of physical fidelity is not optional for commercial scale computers – it is essential for error correction to work, and that in turn is a necessary foundation for any useful quantum computer. Our record two-qubit gate fidelity of 99.914(3)% marks a symbolic inflection point for the industry: at ”three 9's” fidelity, we are nearing or surpassing the break-even point (where logical qubits outperform physical qubits) for many quantum error correction protocols, and this will generate great interest among research and industrial teams exploring fault-tolerant methods for tackling real-world problems.
Without hardware fidelity this good, error-corrected calculations are noisier than un-corrected computations. This is why we call it a “threshold” – when gate errors are “above threshold”, quantum computers will remain noisy no matter what you do. Below threshold, you can use quantum error correction to push error rates way, way down, so that quantum computers eventually become as reliable as classical computers.
Four years ago, Quantinuum claimed that it would improve the performance of its H-Series quantum computers by 10x each year for five years, when measured by the industry’s most widely recognized benchmark, QV (an industry standard not to be confused with less comprehensive metrics such as Algorithmic Qubits).
Today’s achievement of a 220 QV – which as with all our demonstrations was achieved on our commercially-available machine – shows that our team is living up to this audacious commitment. We are completely confident we can continue to overcome the technical problems that stand in the way of even better fidelity and QV performance. Our QV data is available on GitHub, as are our hardware specifications
The combination of high QV and gate fidelities puts the Quantinuum system in a class by-itself – it is far and away the best of any commercially-available quantum computer.
QCCD: the path to fault tolerance
Additionally, and notably, these benchmarks were achieved “inherently”, without error mitigation, thanks to the H Series’ all-to-all connectivity and QCCD architecture. Full connectivity results in less errors when running large, complicated circuits. While other modalities depend on error mitigation techniques, such techniques are not scalable and present only a modest near-term value.
Lower physical error and high connectivity means our quantum computers have a provably lower overhead for error-corrected computation.
Looking more deeply, experts look for high fidelities that are valid in all operating zones and between any pair of qubits. In contrast to our competitors, this is precisely what our H Series delivers. We do not suffer from a broad distribution of gate fidelities between different pairs of qubits, meaning that some pairs of qubits have significantly lower fidelities. Quantinuum is the only quantum computing company with all qubit pairs boasting above 99.9% fidelity.
Alongside these benefits and demonstrations of scalability, fidelity, connectivity, and reliability, it is worth noting how these features impact what arguably matters the most to users – time to solution. In the QCCD architecture, speed of operations is decoupled from speed to reach a computational solution thanks to a combination of:
- a better signal to noise ratio than other modalities
- drastically reducing or eliminating the number of swap gates required (because we can move our ions through space), and
- reducing the number of trials required for an accurate result.
The net effect is that for increasingly complex circuits it takes a high-fidelity QCCD-type quantum computer less time to achieve accurate results than other 2D connected or lower-fidelity architectures.
“Getting to three 9’s in the QCCD architecture means that ~1000 entangling operations can be done before an error occurs. Our quantum computers are right at the edge of being able to do computations at the physical level that are beyond the reach of classical computers, which would occur somewhere between 3 nines and 4 nines. Some tasks become hard for classical computers before this regime (e.g. Google’s random circuit sampling problem) but this new regime allows for much less contrived problems to be solved. At that point, these machines become real tools for new discoveries – albeit they will still be limited in what they can probe, likely to be physics simulations or closely related problems,” said Dave Hayes, a Senior R&D manager at Quantinuum.
“Additionally, these fidelities put us, some would say comfortably, within the regime needed to build fault-tolerant machines. These fidelities allow us to start adding more qubits without needing to improve performance further, and to take advantage of quantum error correction to improve the computational power necessary for tackling truly large problems. This scaling problem gets easier with even better fidelities (which is why we’re not satisfied with 3 nines) but it is possible in principle.”
Quantinuum’s new records in fidelity and quantum volume on our commercial H1 device are expected to be achieved on the H2, once upgrades are implemented, underscoring the value that we offer to users for whom stability, reliability and robust performance are pre-requisites. The quantum computing landscape is complex and changing, but we remain at the head of the pack in all key metrics. The relationship with our world-class applications teams means that co-designed devices for solving some of the world’s most intractable problems are a big step closer to reality.
Quantinuum is the world’s leading quantum computing company, and our world-class scientists and engineers are continually driving our technology forward while expanding the possibilities for our users. Their work on applications includes cybersecurity, quantum chemistry, quantum Monte Carlo integration, quantum topological data analysis, condensed matter physics, high energy physics, quantum machine learning, and natural language processing – and we are privileged to support them to bring new solutions to bear on some of the greatest challenges we face.
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.
Today’s LLMs are often impressive by past standards – but they are far from perfect
Quietly, and determinedly since 2019, we’ve been working on Generative Quantum AI. Our early focus on building natively quantum systems for machine learning has benefitted from and been accelerated by access to the world’s most powerful quantum computers, and quantum computers that cannot be classically simulated.
Our work additionally benefits from being very close to our Helios generation quantum computer, built in Colorado, USA. Helios is 1 trillion times more powerful than our H2 System, which is already significantly more advanced than all other quantum computers available.
While tools like ChatGPT have already made a profound impact on society, a critical limitation to their broader industrial and enterprise use has become clear. Classical large language models (LLMs) are computational behemoths, prohibitively huge and expensive to train, and prone to errors that damage their credibility.
Training models like ChatGPT requires processing vast datasets with billions, even trillions, of parameters. This demands immense computational power, often spread across thousands of GPUs or specialized hardware accelerators. The environmental cost is staggering—simply training GPT-3, for instance, consumed nearly 1,300 megawatt-hours of electricity, equivalent to the annual energy use of 130 average U.S. homes.
This doesn’t account for the ongoing operational costs of running these models, which remain high with every query.
Despite these challenges, the push to develop ever-larger models shows no signs of slowing down.
Enter quantum computing. Quantum technology offers a more sustainable, efficient, and high-performance solution—one that will fundamentally reshape AI, dramatically lowering costs and increasing scalability, while overcoming the limitations of today's classical systems.
Quantum Natural Language Processing: A New Frontier
At Quantinuum we have been maniacally focused on “rebuilding” machine learning (ML) techniques for Natural Language Processing (NLP) using quantum computers.
Our research team has worked on translating key innovations in natural language processing — such as word embeddings, recurrent neural networks, and transformers — into the quantum realm. The ultimate goal is not merely to port existing classical techniques onto quantum computers but to reimagine these methods in ways that take full advantage of the unique features of quantum computers.
We have a deep bench working on this. Our Head of AI, Dr. Steve Clark, previously spent 14 years as a faculty member at Oxford and Cambridge, and over 4 years as a Senior Staff Research Scientist at DeepMind in London. He works closely with Dr. Konstantinos Meichanetzidis, who is our Head of Scientific Product Development and who has been working for years at the intersection of quantum many-body physics, quantum computing, theoretical computer science, and artificial intelligence.
A critical element of the team’s approach to this project is avoiding the temptation to simply “copy-paste”, i.e. taking the math from a classical version and directly implementing that on a quantum computer.
This is motivated by the fact that quantum systems are fundamentally different from classical systems: their ability to leverage quantum phenomena like entanglement and interference ultimately changes the rules of computation. By ensuring these new models are properly mapped onto the quantum architecture, we are best poised to benefit from quantum computing’s unique advantages.
These advantages are not so far in the future as we once imagined – partially driven by our accelerating pace of development in hardware and quantum error correction.
Making computers “talk”- a short history
The ultimate problem of making a computer understand a human language isn’t unlike trying to learn a new language yourself – you must hear/read/speak lots of examples, memorize lots of rules and their exceptions, memorize words and their meanings, and so on. However, it’s more complicated than that when the “brain” is a computer. Computers naturally speak their native languages very well, where everything from machine code to Python has a meaningful structure and set of rules.
In contrast, “natural” (human) language is very different from the strict compliance of computer languages: things like idioms confound any sense of structure, humor and poetry play with semantics in creative ways, and the language itself is always evolving. Still, people have been considering this problem since the 1950’s (Turing’s original “test” of intelligence involves the automated interpretation and generation of natural language).
Up until the 1980s, most natural language processing systems were based on complex sets of hand-written rules. Starting in the late 1980s, however, there was a revolution in natural language processing with the introduction of machine learning algorithms for language processing.
Initial ML approaches were largely “statistical”: by analyzing large amounts of text data, one can identify patterns and probabilities. There were notable successes in translation (like translating French into English), and the birth of the web led to more innovations in learning from and handling big data.
What many consider “modern” NLP was born in the late 2000’s, when expanded compute power and larger datasets enabled practical use of neural networks. Being mathematical models, neural networks are “built” out of the tools of mathematics; specifically linear algebra and calculus.
Building a neural network, then, means finding ways to manipulate language using the tools of linear algebra and calculus. This means representing words and sentences as vectors and matrices, developing tools to manipulate them, and so on. This is precisely the path that researchers in classical NLP have been following for the past 15 years, and the path that our team is now speedrunning in the quantum case.
Quantum Word Embeddings: A Complex Twist
The first major breakthrough in neural NLP came roughly a decade ago, when vector representations of words were developed, using the frameworks known as Word2Vec and GloVe (Global Vectors for Word Representation). In a recent paper, our team, including Carys Harvey and Douglas Brown, demonstrated how to do this in quantum NLP models – with a crucial twist. Instead of embedding words as real-valued vectors (as in the classical case), the team built it to work with complex-valued vectors.
In quantum mechanics, the state of a physical system is represented by a vector residing in a complex vector space, called a Hilbert space. By embedding words as complex vectors, we are able to map language into parameterized quantum circuits, and ultimately the qubits in our processor. This is a major advance that was largely under appreciated by the AI community but which is now rapidly gaining interest.
Using complex-valued word embeddings for QNLP means that from the bottom-up we are working with something fundamentally different. This different “geometry” may provide advantage in any number of areas: natural language has a rich probabilistic and hierarchical structure that may very well benefit from the richer representation of complex numbers.
The Quantum Recurrent Neural Network (RNN)
Another breakthrough comes from the development of quantum recurrent neural networks (RNNs). RNNs are commonly used in classical NLP to handle tasks such as text classification and language modeling.
Our team, including Dr. Wenduan Xu, Douglas Brown, and Dr. Gabriel Matos, implemented a quantum version of the RNN using parameterized quantum circuits (PQCs). PQCs allow for hybrid quantum-classical computation, where quantum circuits process information and classical computers optimize the parameters controlling the quantum system.
In a recent experiment, the team used their quantum RNN to perform a standard NLP task: classifying movie reviews from Rotten Tomatoes as positive or negative. Remarkably, the quantum RNN performed as well as classical RNNs, GRUs, and LSTMs, using only four qubits. This result is notable for two reasons: it shows that quantum models can achieve competitive performance using a much smaller vector space, and it demonstrates the potential for significant energy savings in the future of AI.
In a similar experiment, our team partnered with Amgen to use PQCs for peptide classification, which is a standard task in computational biology. Working on the Quantinuum System Model H1, the joint team performed sequence classification (used in the design of therapeutic proteins), and they found competitive performance with classical baselines of a similar scale. This work was our first proof-of-concept application of near-term quantum computing to a task critical to the design of therapeutic proteins, and helped us to elucidate the route toward larger-scale applications in this and related fields, in line with our hardware development roadmap.
Quantum Transformers - The Next Big Leap
Transformers, the architecture behind models like GPT-3, have revolutionized NLP by enabling massive parallelism and state-of-the-art performance in tasks such as language modeling and translation. However, transformers are designed to take advantage of the parallelism provided by GPUs, something quantum computers do not yet do in the same way.
In response, our team, including Nikhil Khatri and Dr. Gabriel Matos, introduced “Quixer”, a quantum transformer model tailored specifically for quantum architectures.
By using quantum algorithmic primitives, Quixer is optimized for quantum hardware, making it highly qubit efficient. In a recent study, the team applied Quixer to a realistic language modeling task and achieved results competitive with classical transformer models trained on the same data.
This is an incredible milestone achievement in and of itself.
This paper also marks the first quantum machine learning model applied to language on a realistic rather than toy dataset.
This is a truly exciting advance for anyone interested in the union of quantum computing and artificial intelligence, and is in danger of being lost in the increased ‘noise’ from the quantum computing sector where organizations who are trying to raise capital will try to highlight somewhat trivial advances that are often duplicative.
Quantum Tensor Networks. A Scalable Approach
Carys Harvey and Richie Yeung from Quantinuum in the UK worked with a broader team that explored the use of quantum tensor networks for NLP. Tensor networks are mathematical structures that efficiently represent high-dimensional data, and they have found applications in everything from quantum physics to image recognition. In the context of NLP, tensor networks can be used to perform tasks like sequence classification, where the goal is to classify sequences of words or symbols based on their meaning.
The team performed experiments on our System Model H1, finding comparable performance to classical baselines. This marked the first time a scalable NLP model was run on quantum hardware – a remarkable advance.
The tree-like structure of quantum tensor models lends itself incredibly well to specific features inherent to our architecture such as mid-circuit measurement and qubit re-use, allowing us to squeeze big problems onto few qubits.
Since quantum theory is inherently described by tensor networks, this is another example of how fundamentally different quantum machine learning approaches can look – again, there is a sort of “intuitive” mapping of the tensor networks used to describe the NLP problem onto the tensor networks used to describe the operation of our quantum processors.
What we’ve learned so far
While it is still very early days, we have good indications that running AI on quantum hardware will be more energy efficient.
We recently published a result in “random circuit sampling”, a task used to compare quantum to classical computers. We beat the classical supercomputer in time to solution as well as energy use – our quantum computer cost 30,000x less energy to complete the task than Frontier, the classical supercomputer we compared against.
We may see, as our quantum AI models grow in power and size, that there is a similar scaling in energy use: it’s generally more efficient to use ~100 qubits than it is to use ~10^18 classical bits.
Another major insight so far is that quantum models tend to require significantly fewer parameters to train than their classical counterparts. In classical machine learning, particularly in large neural networks, the number of parameters can grow into the billions, leading to massive computational demands.
Quantum models, by contrast, leverage the unique properties of quantum mechanics to achieve comparable performance with a much smaller number of parameters. This could drastically reduce the energy and computational resources required to run these models.
The Path Ahead
As quantum computing hardware continues to improve, quantum AI models may increasingly complement or even replace classical systems. By leveraging quantum superposition, entanglement, and interference, these models offer the potential for significant reductions in both computational cost and energy consumption. With fewer parameters required, quantum models could make AI more sustainable, tackling one of the biggest challenges facing the industry today.
The work being done by Quantinuum reflects the start of the next chapter in AI, and one that is transformative. As quantum computing matures, its integration with AI has the potential to unlock entirely new approaches that are not only more efficient and performant but can also handle the full complexities of natural language. The fact that Quantinuum’s quantum computers are the most advanced in the world, and cannot be simulated classically, gives us a unique glimpse into a future.
The future of AI now looks very much to be quantum and Quantinuum’s Gen QAI system will usher in the era in which our work will have meaningful societal impact.
At this year’s Q2B Silicon Valley conference from December 10th – 12th in Santa Clara, California, the Quantinuum team will be participating in plenary and case study sessions to showcase our quantum computing technologies.
Schedule a meeting with us at Q2B
Meet our team at Booth #G9 to discover how Quantinuum is charting the path to universal, fully fault-tolerant quantum computing.
Join our sessions:
Tuesday, Dec 10, 10:00 - 10:20am PT
Plenary: Advancements in Fault-Tolerant Quantum Computation: Demonstrations and Results
There is industry-wide consensus on the need for fault-tolerant QPU’s, but demonstrations of these abilities are less common. In this talk, Dr. Hayes will review Quantinuum’s long list of meaningful demonstrations in fault-tolerance, including real-time error correction, a variety of codes from the surface code to exotic qLDPC codes, logical benchmarking, beyond break-even behavior on multiple codes and circuit families.
Wednesday, Dec 11, 4:30 – 4:50pm PT
Keynote: Quantum Tokens: Securing Digital Assets with Quantum Physics
Mitsui’s Deputy General Manager, Quantum Innovation Dept., Corporate Development Div., Koji Naniwada, and Quantinuum’s Head of Cybersecurity, Duncan Jones will deliver a keynote presentation on a case study for quantum in cybersecurity. Together, our organizations demonstrated the first implementation of quantum tokens over a commercial QKD network. Quantum tokens enable three previously incompatible properties: unforgeability guaranteed by physics, fast settlement without centralized validation, and user privacy until redemption. We present results from our successful Tokyo trial using NEC's QKD commercial hardware and discuss potential applications in financial services.
Wednesday, Dec 11, 5:10 – 6:10pm PT
Quantinuum and Mitsui Sponsored Happy Hour
Join the Quantinuum and Mitsui teams in the expo hall for a networking happy hour.