Quantinuum Sets New Record with Highest Ever Quantum Volume
Simpler, faster and fewer errors: How arbitrary angle gates help increase H1’s quantum volume
New arbitrary angle gate capabilities enable increase in Quantum Volume (QV) to 8192 as Quantinuum continues to achieve its previously stated objective of increasing its QV by 10x every year; TKET downloads surpass 500,000
Quantinuum President and COO Tony Uttley announced three major accomplishments during his keynote address at the IEEE Quantum Week event in Colorado last week.
The three milestones, representing actionable acceleration for the quantum computing eco-system, are: (i) new arbitrary angle gate capabilities on the H-series hardware, (ii) another QV record for the System Model H1 hardware, and (iii) over 500,000 downloads of Quantinuum’s open-sourced TKET, a world-leading quantum software development kit (SDK).
The announcements were made during Uttley’s keynote address titled, “A Measured Approach to Quantum Computing.”
These advancements are the latest examples of the company’s continued demonstration of its leadership in the quantum computing community.
“Quantinuum is accelerating quantum computing’s impact to the world,” Uttley said. “We are making significant progress with both our hardware and software, in addition to building a community of developers who are using our TKET SDK.”
This latest quantum volume measurement of 8192 is particularly noteworthy and is the second time this year Quantinuum has published a new QV record on their trapped-ion quantum computing platform, the System Model H1, Powered by Honeywell.
A key to achieving this latest record is the new capability of directly implementing arbitrary angle two-qubit gates. For many quantum circuits, this new way of doing a two-qubit gate allows for more efficient circuit construction and leads to higher fidelity results.
Dr. Brian Neyenhuis, Director of Commercial Operations at Quantinuum, said, “This new capability allows for several user advantages. In many cases, this includes shorter interactions with the qubits, which lowers the error rate. This allows our customers to run long computations with less noise.”
These arbitrary angle gates build on the overall design strength of the trapped-ion architecture of the H1, Neyenhuis said.
“With the quantum-charged coupled device (QCCD) architecture, interactions between qubits are very simple and can be limited to a small number of qubits which means we can precisely control the interaction and don’t have to worry about additional crosstalk,” he said.
This new gate design represents a third method for Quantinuum to improve the efficiency of the H1 generation, said Dr. Jenni Strabley, Senior Director of Offering Management at Quantinuum.
“Quantinuum’s goal is to accelerate quantum computing. We know we have to make the hardware better and we have to make the algorithms smarter, and we’re doing that,” she said. “Now we can also implement the algorithms more efficiently on our H1 with this new gate design.”
A powerful new capability: More information on arbitrary angle gates
Currently, researchers can do single qubit gates – rotations on a single qubit – or a fully entangling two-qubit gate. It’s possible to build any quantum operation out of just those building blocks.
With arbitrary angle gates, instead of just having a two-qubit gate that's fully entangling, scientists can use a two-qubit gate that is partially entangling.
“There are many algorithms where you want to evolve the quantum state of the system one tiny step at a time. Previously, if you wanted a tiny bit of entanglement for some small time step, you had to entangle it all the way, rotate it a little bit, and then unentangle it almost all the way back,” Neyenhuis said. “Now we can just add this tiny little bit of entanglement natively and then go to the next step of the algorithm.”
There are other algorithms where this arbitrary angle two-qubit gate is the natural building block, according to Neyenhuis. One example is the quantum Fourier transform. Using arbitrary angle two-qubit gates cuts the number of two-qubit gates (and the overall error) in half, drastically improving the fidelity of the circuit. Researchers can use this new gate design to run harder problems that resulted in catastrophic errors in previous experiments.
“By going to an arbitrary angle gate, in addition to cutting the number of two-qubit gates in half, the error we get per gate is lower because it scales with the amplitude of that gate,” Neyenhuis said.
This is a powerful new capability, particularly for noisy intermediate-scale quantum algorithms. Another demonstration from the Quantinuum team was to use arbitrary angle two-qubit gates to study non-equilibrium phase transitions, the technical details of which are available on the arXiv here.
“For the algorithms that we are going to want to run in this NISQ regime that we're in right now, this is a more efficient way to run your algorithm,” Neyenhuis said. “There are lots of different circuits you would want to run where this arbitrary angle gate gives you a fairly significant increase in the fidelity of your overall circuit. This capability also allows for a speed up in the circuit execution by removing unneeded gates, which ultimately reduces the time of executing a job on our machines.”
Researchers working with machine learning algorithms, variational algorithms, and time evolution algorithms would see the most benefit from these new gates. This advancement is particularly relevant for simulating the dynamics of other quantum systems.
“This just gave us a big win in fidelity because we can run the sort of interaction you're after natively, rather than constructing it out of some other Lego blocks,” Neyenhuis said.
A new milestone in quantum volume
Quantum volume tests require running arbitrary circuits. At each slice of the quantum volume circuit, the qubits are randomly paired up and a complex two-qubit operation is performed. This SU(4) gate can be constructed more efficiently using the arbitrary angle two-qubit gate, lowering the error at each step of the algorithm.
The H1-1’s quantum volume of 8192 is due in part to the implementation of arbitrary angle gates and the continued reduction in error rates. Quantinuum’s last quantum volume increase was in April when the System Model H1-2 doubled its performance to become the first commercial quantum computer to pass Quantum Volume 4096.
This new increase is the seventh time in two years that Quantinuum’s H-Series hardware has set an industry record for measured quantum volume as it continues to achieve its goal of 10X annual improvement.
Quantum volume, a benchmark introduced by IBM in 2019, is a way to measure the performance of a quantum computer using randomized circuits, and is a frequently used metric across the industry.
Building a quantum ecosystem among developers
Quantinuum has also achieved another milestone: over 500,000 downloads of TKET.
TKET is an advanced software development kit for writing and running programs on gate-based quantum computers. TKET enables developers to optimize their quantum algorithms, reducing the computational resources required, which is important in the NISQ era.
TKET is open source and accessible through the PyTKET Python package. The SDK also integrates with major quantum software platforms including Qiskit, Cirq and Q#. TKET has been available as an open source language for almost a year.
This universal availability and TKET’s portability across many quantum processors are critical for building a community of developers who can write quantum algorithms. The number of downloads includes many companies and academic institutions which account for multiple users.
Quantinuum CEO Ilyas Khan said, “Whilst we do not have the exact number of users of TKET, it is clear that we are growing towards a million people around the world who have taken advantage of a critical tool that integrates across multiple platforms and makes those platforms perform better. We continue to be thrilled by the way that TKET helps democratize as well as accelerate innovation in quantum computing.”
Arbitrary angle two-qubit gates and other recent Quantinuum advances are all built into TKET.
“TKET is an evolving platform and continues to take advantage of these new hardware capabilities,” said Dr. Ross Duncan, Quantinuum’s Head of Quantum Software. “We’re excited to put these new capabilities into the hands of the rapidly increasing number of TKET users around the world.”
Additional Data for Quantum Volume 8192
The average single-qubit gate fidelity for this milestone was 99.9959(5)%, the average two-qubit gate fidelity was 99.71(3)% with fully connected qubits, and state preparation and measurement fidelity was 99.72(1)%. The Quantinuum team ran 220 circuits with 90 shots each, using standard QV optimization techniques to yield an average of 175.2 arbitrary angle two-qubit gates per circuit.
The System Model H1-1 successfully passed the quantum volume 8192 benchmark, outputting heavy outcomes 69.33% of the time, with a 95% confidence interval lower bound of 68.38% which is above the 2/3 threshold.
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.
A team from Quantinuum and the University of Freiburg found that quantum computers outperform classical for a workhorse calculation often used in accelerators like the Large Hadron Collider (LHC) at CERN.
Quantinuum’s Ifan Williams worked with the University of Freiburg’s Mathieu Pellen to tackle a pernicious problem in accelerator physics: calculating “cross sections”. Together, they developed a general, scalable approach to calculating cross sections that offers a quadratic speed-up compared to its classical counterpart.
A “cross-section” relates to the probability of a certain interaction happening. Scientists who do experiments in particle accelerators compare real measurements with theoretical cross-section calculations (predictions), using the agreement (or disagreement) to reason about the nature of our universe.
Generally, scientists run Monte Carlo simulations to make their theoretical predictions. Monte Carlo simulations are currently the biggest computational bottleneck in experimental high-energy physics (HEP), costing enormous CPU resources, which will only grow larger as new experiments come online.
It’s hard to put a specific number on exactly how costly calculations like this are, but we can say that probing fundamental physics at the LHC probably uses roughly 10 billion CPUH/year for data treatment, simulations, and theory predictions. Knowing that the theory predictions represent approximately 15-25% of this total, putting even a 10% dent in this number would be a massive change.
The collaborators used Quantinuum’s Quantum Monte Carlo integration (QMCI) engine to solve the same problem. Their work is the first published general methodology for performing cross-section calculations in HEP using quantum integration.
Importantly, the team’s methodology is potentially extendable to the problem sizes needed for real-world HEP cross-section calculations (currently done classically). Overall, this work establishes a solid foundation for performing such computations on a quantum computer in the future.
The Large Hadron Collider, the world’s biggest particle accelerator, generates a billion collisions each second, far more data than can be computationally analyzed. Planned future experiments are expected to generate even more. Quantum computers are also accelerating. Quantinuum’s latest H2 System became the highest performing commercially available system in the world when it was launched. When it was upgraded in 2024, it became the first quantum computer that cannot be exactly simulated by any classical computer. Our next generation Helios, on schedule to launch in 2025, will encode at least a trillion times more information than the H2—this is the power of exponential growth.
We can’t wait to see what’s next with quantum computing and high-energy physics.
The Quantinuum team is looking forward to participating in this year’s SCAsia conference from March 10th – 13th in Singapore. Meet our team at Booth B2 to discover how Quantinuum is bridging the gap between quantum computing and high-performance compute with leading industry partners.
Our team will be participating in workshops and presenting at the keynote and plenary sessions to showcase our quantum computing technologies. Join us at the below sessions:
Monday, March 10th, 1:30 – 2:30pm
Workshop: Accelerating Quantum Supercomputing: CUDA-Q Tutorial across Multiple Quantum Platforms
Location: Room P10 – Peony Jr 4512 (Level 4)
This workshop will explore the seamless integration of classical and quantum resources for quantum-accelerated supercomputing. Join Kentaro Yamamoto and Enrico Rinaldi, Lead R&D Scientists at Quantinuum, for an Introduction to our integrated full-stack for quantum computing, Quantum Phase Estimation (QPE) for solving quantum chemistry problems, and a demonstration of a QPE algorithm with CUDA-Q on Quantinuum Systems. If you're interested in access to our quantum computers and emulator for use on the CUDA-Q platform, register here.
Tuesday, March 11th, 11:00 – 11:30pm
Keynote: Quantum Computing: A Transformative Force for Singapore's Regional Economy
Location: Melati Ballroom (Level 4)
Quantum Computing is no longer a distant promise; it has arrived and is poised to revolutionize several economies. Join our President and CEO, Dr. Rajeeb Hazra, to discover how Quantinuum’s approach to Quantum Generative AI is driving breakthroughs in applications which hold significant relevance for Singapore, in fields like chemistry, computational biology, and finance. Additionally, Raj will discuss the challenges and opportunities of adopting quantum solutions from both technical and business perspectives, emphasizing the importance of collaboration to build quantum applications that integrate the best of quantum and AI.
Tuesday, March 11th, 5:40 – 6:00pm
Industry Breakout Track: Transformative value of Quantum and AI: bringing meaningful insights for critical applications today
Location: Room L1 – Lotus Jr (Level 4)
The ability to solve classically intractable problems defines the transformative value of quantum computing, offering new tools to redefine industries and address complex humanity challenges. In this session with Dr. Elvira Shishenina, Senior Director of Strategic Initiatives, discover how Quantinuum’s hardware is leading the way in achieving early fault-tolerance, marking a significant step forward in computational capabilities. By integrating quantum technology with AI and high-performance computing, we are building systems designed to address real-world issues with efficiency, precision and scale. This approach empowers critical applications from hydrogen fuel cells and carbon capture to precision medicine, food security, and cybersecurity, providing meaningful insights at a commercial level today.
Wednesday, March 12th, 4:40 – 5:00pm
Hybrid Quantum Classical Computing Track: Quantifying Quantum Advantage with an End-to-End Quantum Algorithm for the Jones Polynomial
Location: Room O3 – Orchid Jr 4211-2 (Level 4)
Join Konstantinos Meichanetzidis, Head of Scientific Product Development, for this presentation on an end-to-end reconfigurable algorithmic pipeline for solving a famous problem in knot theory using a noisy digital quantum computer. Specifically, they estimate the value of the Jones polynomial at the fifth root of unity within additive error for any input link, i.e. a closed braid. This problem is DQC1-complete for Markov-closed braids and BQP-complete for Plat-closed braids, and we accommodate both versions of the problem. In their research, they demonstrate our quantum algorithm on Quantinuum’s H2 quantum computer and show the effect of problem-tailored error-mitigation techniques. Further, leveraging that the Jones polynomial is a link invariant, they construct an efficiently verifiable benchmark to characterize the effect of noise present in a given quantum processor. In parallel, they implement and benchmark the state-of-the-art tensor-network-based classical algorithms.The practical tools provided in the work presented will allow for precise resource estimation to identify near-term quantum advantage for a meaningful quantum-native problem in knot theory.
Thursday, March 13th, 11:00 – 11:30pm
Industry Plenary: Quantum Heuristics: From Worst Case to Practice
Location: Melati Ballroom (Level 4)
Which problems allow for a quantum speedup, and which do not? This question lies at the heart of quantum information processing. Providing a definitive answer is challenging, as it connects deeply to unresolved questions in complexity theory. To make progress, complexity theory relies on conjectures such as P≠NP and the Strong Exponential Time Hypothesis, which suggest that for many computational problems, we have discovered algorithms that are asymptotically close to optimal in the worst case. In this talk, Professor Harry Buhrman, Chief Scientist for Algorithms and Innovation, will explore the landscape from both theoretical and practical perspectives. On the theoretical side, I will introduce the concept of “queasy instances”—problem instances that are quantum-easy but classically hard (classically queasy). On the practical side, I will discuss how these insights connect to advancements in quantum hardware development and co-design.
*All times in Singapore Standard Time
BY HARRY BUHRMAN
Quantum computing continues to push the boundaries of what is computationally possible. A new study by Marcello Benedetti, Harry Buhrman, and Jordi Weggemans introduces Complement Sampling, a problem that highlights a dramatic separation between quantum and classical sample complexity. This work provides a robust demonstration of quantum advantage in a way that is not only provable but also feasible on near-term quantum devices.
The Complement Sampling Problem
Imagine a universe of N = 2n elements, from which a subset S of size K is drawn uniformly at random. The challenge is to sample from the complement S̅ without explicitly knowing S, but having access to samples of S. Classically, solving this problem requires roughly K samples, as the best a classical algorithm can do is guess at random after observing only some of the elements of S.
To better understand this, consider a small example. Suppose N = 8, meaning our universe consists of the numbers {0,1,2,3,4,5,6,7}. If a subset S of size K = 4 is drawn at random—say {1,3,5,7}—the goal is to sample from the complement S̅, which consists of {0,2,4,6}. A classical algorithm would need to collect and verify enough samples from S before it could infer what S̅ might be. However, a quantum algorithm can use a single superposition state over S (a quantum sample) to instantly generate a sample from S̅, eliminating the need for iterative searching.
Why This Matters: Quantum Advantage in Sample Complexity
Quantum advantage is often discussed in terms of computational speedups, such as those achieved by Shor’s algorithm for factoring large numbers. However, quantum resources provide advantages beyond time efficiency—they also affect how data is accessed, stored, and processed.
Complement Sampling fits into the category of sample complexity problems, where the goal is to minimize the number of samples needed to solve a problem. The authors prove that their quantum approach not only outperforms classical methods but does so in a way that is:
- Provable: It provides rigorous lower bounds on classical sample complexity, demonstrating an exponential separation.
- Verifiable: The correctness of the output of the sampler can be efficiently checked classically.
- NISQable: The quantum circuit required is shallow and feasible for Noisy Intermediate-Scale Quantum (NISQ) devices.
How the Quantum Algorithm Works
At its core, the quantum approach to Complement Sampling relies on the ability to perform a perfect swap between a subset S and its complement S̅. The method draws inspiration from a construction by Aaronson, Atia, and Susskind, which links state distinguishability to state swapping. The quantum algorithm:
- Uses a unitary transformation that maps the quantum sample |S⟩ to |S̅⟩ with high probability.
- For K = N/2, the algorithm works perfectly outputting an element from S̅ with probability 1.
- For other values of K, a probabilistic zero-error approach is used, ensuring correctness while reducing success probability.
This is made possible by quantum interference and superposition, allowing a quantum computer to manipulate distributions in ways that classical systems fundamentally cannot.
Classical Hardness and Cryptographic Implications
A crucial aspect of this work is its robustness. The authors prove that even for subsets generated using strong pseudorandom permutations, the problem remains hard for classical algorithms. This means that classical computers cannot efficiently solve Complement Sampling even with structured input distributions—an important consideration for real-world applications.
This robustness suggests potential applications in cryptography, where generating samples from complements could be useful in privacy-preserving protocols and quantum-secure verification methods.
Towards an Experimental Demonstration
Unlike some quantum advantage demonstrations that are difficult to verify classically (such as the random circuit sampling experiment), Complement Sampling is designed to be verifiable. The authors propose an interactive quantum versus classical game:
- A referee provides a quantum player with quantum samples from S.
- The player must return a sample from S̅
- A classical player, given the same number of classical samples, attempts to do the same.
While the classical player must resort to random guessing, the quantum player can leverage the swap algorithm to succeed with near certainty. Running such an experiment on NISQ hardware could serve as a practical demonstration of quantum advantage in a sample complexity setting.
Future Directions
This research raises exciting new questions:
- Can Complement Sampling be extended to more general probability distributions?
- Are there cryptographic protocols that can directly leverage this advantage?
- How well does the quantum algorithm perform in real-world noisy conditions?
With its blend of theoretical depth and experimental feasibility, Complement Sampling provides a compelling new frontier for demonstrating the power of quantum computing.
Conclusion
Complement Sampling represents one of the cleanest demonstrations of quantum advantage in a practical, verifiable, and NISQ-friendly setting. By leveraging quantum information processing in ways that classical computers fundamentally cannot, this work strengthens the case for near-term quantum technologies and their impact on computational complexity, cryptography, and beyond.
For those interested in the full details, the paper provides rigorous proofs, circuit designs, and further insights into the nature of quantum sample complexity. As quantum computing continues to evolve, Complement Sampling may serve as a cornerstone for future experimental demonstrations of quantum supremacy.
We have commenced work on the experiment – watch this space!