Our Hardware is Now Running Quantum Transformers!
If we are to create ‘next-gen’ AI that takes full advantage of the power of quantum computers, we need to start with quantum native transformers. Today we announce yet again that Quantinuum continues to lead by demonstrating concrete progress — advancing from theoretical models to real quantum deployment.
The future of AI won't be built on yesterday’s tech. If we're serious about creating next-generation AI that unlocks the full promise of quantum computing, then we must build quantum-native models—designed for quantum, from the ground up.
Around this time last year, we introduced Quixer, a state-of-the-art quantum-native transformer. Today, we’re thrilled to announce a major milestone: one year on, Quixer is now running natively on quantum hardware.
Why this matters: Quantum AI, born native
This marks a turning point for the industry: realizing quantum-native AI opens a world of possibilities.
Classical transformers revolutionized AI. They power everything from ChatGPT to real-time translation, computer vision, drug discovery, and algorithmic trading. Now, Quixer sets the stage for a similar leap — but for quantum-native computation. Because quantum computers differ fundamentally from classical computers, we expect a whole new host of valuable applications to emerge.
Achieving that future requires models that are efficient, scalable, and actually run on today’s quantum hardware.
That’s what we’ve built.
What makes Quixer different?
Until Quixer, quantum transformers were the result of a brute force “copy-paste” approach: taking the math from a classical model and putting it onto a quantum circuit. However, this approach does not account for the considerable differences between quantum and classical architectures, leading to substantial resource requirements.
Quixer is different: it’s not a translation – it's an innovation.
With Quixer, our team introduced an explicitly quantum transformer, built from the ground up using quantum algorithmic primitives. Because Quixer is tailored for quantum circuits, it's more resource efficient than most competing approaches.
As quantum computing advances toward fault tolerance, Quixer is built to scale with it.
What’s next for Quixer?
We’ve already deployed Quixer on real-world data: genomic sequence analysis, a high-impact classification task in biotech. We're happy to report that its performance is already approaching that of classical models, even in this first implementation.
This is just the beginning.
Looking ahead, we’ll explore using Quixer anywhere classical transformers have proven to be useful; such as language modeling, image classification, quantum chemistry, and beyond. More excitingly, we expect use cases to emerge that are quantum-specific, impossible on classical hardware.
This milestone isn’t just about one model. It’s a signal that the quantum AI era has begun, and that Quantinuum is leading the charge with real results, not empty hype.
Stay tuned. The revolution is only getting started.
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.
But a race needs a destination, a trustworthy answer to a high-value industrial problem, delivered within a useful timeframe and at an acceptable cost, with a measurable advantage over the best classical approach.
Gate speed is often presented as the measure that matters, and our trapped-ion gates are slower than those in superconducting systems. So, are we working too hard on fidelity?
The answer is a resounding no.
Speed matters enormously. But it becomes commercially meaningful only when the quantum information survives long enough to complete the computation.
Put simply: moving slower in the right direction is still progress. Racing toward a random destination is useless.
That is more than a metaphor. As fidelity falls, the output loses its connection to the intended computation. A growing body of theoretical work makes this precise 1-3. For broad classes of circuits and noise, a sufficiently noisy quantum computation can be efficiently approximated on a classical computer.
In the fault-tolerant era, time to solution is paramount. Fidelity is the foundation.
Do fast gates mean fast answers?
For a high-value industrial use case, what counts is the time to an answer that genuinely beats the best available classical approach, not the duration of an individual physical gate. A quantum computer must have enough usable qubits, efficient connectivity, and an error profile that supports scalable error correction. When we say fidelity, we mean that broader error profile: gate, measurement and memory errors, leakage, crosstalk, correlations, and drift.
For a fair comparison, both systems must solve the same computational problem to the same accuracy, with the same chance of getting the answer right. Then measure the total wall-clock time to a result worth acting on. That includes everything: state preparation, compilation and routing, transport and idle time, physical gates, measurement and reset, circuit repetitions, error correction and decoding, and classical pre- and post-processing. A system with slower physical gates can still arrive first if it incurs lower overheads. It may even be the only system that arrives at all, because noise overwhelms the faster system's computation.
To see why fidelity matters to time to solution, consider how errors accumulate during a computation. In a simple independent-error model, the expected number of faults is roughly the number of operations multiplied by the error rate per operation. At an error rate of 10-3 per operation (99.9% fidelity), a circuit with 10,000 operations averages about ten faults. Even one bad fault can be enough to spoil the answer. To keep the expected number of faults below one, that circuit needs an effective error rate below 10-4. A dependable result needs even fewer faults still. Now make every gate ten times faster, without changing its error rate. You still get ten faults. You just get them sooner. Real devices have more complex error models, but the point holds across platforms: faster gates alone do not reduce the number of faults. The error per operation is the bottleneck to scaling to useful computation.
The good news is that to run our 10,000-operation example reliably, we do not need to push the physical error rate itself below 10-4. Instead, quantum error correction uses many physical qubits and operations to build protected logical operations whose error rate is well below 10-4. One important caveat: the physical operations must already be reliable enough for error correction to scale. Error correction itself requires extra operations, each of which can introduce faults. The fault-tolerance threshold is the physical error rate below which increasing the protection reduces the logical error rate. Below this threshold, the added protection outweighs the additional opportunities for faults. Thresholds are code dependent. Experiments therefore demonstrate operation below the threshold of a specific code, such as the color code or surface code. A faster processor must meet the same requirement: speed cannot replace the necessary fidelity.
Physical error rates around 10⁻³ are already below threshold in demonstrated protocols 4,5 including our new color-code experiments on Helios. From there, scaling up error correction pushes the logical error rate down to whatever the application requires. The more operations it needs to complete reliably, the lower that rate must be. That is what makes fault-tolerant computation possible.
A leading indicator
The bottom line is that no single speed metric is enough. We need to know how much reliable computation a system can sustain and how quickly it delivers it. Cost and energy also matter.
The proposed, architecture-neutral, Quantum Universal Operations Performance System (QUOPS) tracks computational reach and speed 6,7. Its score, Q, measures how large a circuit the system can execute successfully, using a standardized count of quantum operations. The benchmark circuits span shapes motivated by utility-scale workloads. Its rate, Ω, measures effective operations per second at that size. It uses wall-clock time and accounts for extra samples and discarded runs caused by noise.
In the first cross-platform QUOPS results, our H2-1 and Helios-1 successfully executed substantially larger benchmark circuits than Google’s Willow and IBM’s Boston. The superconducting systems achieved higher effective operation rates but on smaller circuits 6.
Error mitigation can help a processor run larger circuits but requires many more repetitions to recover useful information. In the QUOPS study, the resulting overhead reduced effective processing rates by factors of thousands 6. Faster physical gates do not necessarily mean a faster answer.
The study estimates what it would take to factor RSA-2048 or calculate FeMoco’s ground-state energy within five days. In QUOPS terms, these tasks require roughly 100,000 times the computational reach demonstrated by today’s physical-qubit processors. Some machines already approach the estimated speed requirement, but only on small circuits. The challenge is to deliver that speed on much larger, reliable computations—including the overhead of error correction 6.
More qubits alone do not close this gap. At fixed error rates, running larger uncorrected circuits also means accumulating more faults. Closing the reach gap requires error correction, which costs qubits and time. Extra physical qubits supply protection; syndrome measurement, decoding, and logical gate construction cost time. Reliability and speed must therefore scale together. QUOPS offers a way to track both 6,7.
Is there still a role for NISQ?
Yes, but only within a narrow window. Without error correction, noise severely limits circuit depth and total gate count, so Noisy Intermediate-Scale Quantum (NISQ) computing does not offer the scalable path that fault tolerance does.
NISQ systems have advanced algorithms and hardware, enabled certified randomness 8, and supported pre-fault-tolerant studies of quantum magnetism 9 as well as superconducting pairing correlations 10. Recent simulations of interacting spins have also reported behavior leading classical methods could not reliably reproduce 11. These are candidates for computational advantage; industrial advantage is a higher bar.
Industrial advantage means solving a practically relevant problem faster or at lower cost than the best classical approach, at matched accuracy and accounting for the entire workflow. The improvement should survive advances in classical algorithms. No uncorrected gate-based NISQ system has yet demonstrated broadly accepted, repeatable, and scalable advantage on a production-level industrial workload. Recent advantage claims still face classical challenge and validation 11.
A useful NISQ circuit must outperform classical alternatives while keeping its depth and gate count low enough for a useful signal to survive. Error mitigation can extend this window, but it is not a scalable substitute for error correction. At fixed error rates, general-purpose mitigation faces exponential sampling costs as circuits grow 12. It estimates an ideal result from additional noisy runs. Postselection discards runs with detected errors, making low acceptance rates costly.
Yet the search is far from over. Demonstrated applications with a clear practical NISQ advantage remain few, but our 98-qubit Helios system already supports circuits beyond the practical reach of known classical simulation methods 13. That is not itself industrial advantage, but it leaves room for useful applications we have yet to discover.
What changes below threshold?
Quantum error correction changes what it means to scale a computation. Below the fault-tolerance threshold, increasing the code distance (a measure of how many errors the code can withstand) drives logical errors down instead of letting them accumulate unchecked.
But crossing the threshold is only the beginning. How quickly logical errors fall as protection increases matters too, as the results below show.
This is why fidelity has economic as well as scientific importance. Better physical operations mean fewer physical qubits per useful logical qubit, fewer rejected runs, lower error-correction overhead and less time, energy and infrastructure per trusted result.
What does high fidelity buy us?
Earlier experiments demonstrated threshold behavior in a concatenated-code state-preparation protocol 14 and improved logical performance through error correction combined with postselected error detection 9,15. An independent study found that Helios ran circuits built from key error-correction primitives with lower effective error rates than the tested IBM superconducting processors 16,17.
In 2026, we reported in Nature that Helios achieved average single-qubit gate fidelity of 99.9975% and average two-qubit gate fidelity of 99.921% across its operational zones, while scaling the trapped-ion architecture to 98 fully connected physical qubits 13. The crucial test is whether this physical performance is good enough for error correction to make computation increasingly reliable as we add protection.
Our October 2026 color-code results show that it is 5. Helios operated below the color-code threshold: increasing the code distance suppressed logical memory errors exponentially across the tested distances, while protected two-qubit Clifford operations improved as well. This is a major milestone toward scalable, fault-tolerant computation.
These experiments tested code distances 3, 5 and 7. With physical two-qubit gate error rates around 10⁻³, error correction on Helios achieved an estimated logical memory failure rate of just 5 × 10⁻⁶ per correction cycle in the largest code—about five failures per million cycles 5. Across those three code sizes, the fitted error suppression was about twelvefold for each increase of two in distance. Protected two-qubit Clifford operations also improved, with roughly ninefold suppression from distance 3 to 5 5.
Recall our 10,000-operation example: at an error rate of 10⁻³ per operation, it averaged ten faults. Error correction lowers the logical error rate, making longer, reliable computations possible. Without that reliability, there may be no useful answer to deliver. First make the computation reliable; then make it faster.
This goes to the heart of the scaling question: stronger protection improved not only stored information, but also logical operations. That is how high physical fidelity can enable longer, reliable computations, rather than just a cleaner component benchmark.
Google has also demonstrated below-threshold error correction, using a surface code on Willow 18. For each increase of two in code distance, memory errors fell about twofold. In our experiments, they fell about twelvefold 5. This matters because error correction is not free: larger codes require more physical qubits and operations. Within a given error-correction scheme, lower physical error rates generally mean stronger suppression and smaller codes for the same application target. The codes and implementations differ, so this is not a direct comparison of speed or cost. But stronger suppression can reduce error-correction overhead and make useful computation more efficient.
Magic-state preparation, another essential ingredient for universal fault-tolerant computation, has also advanced. We demonstrated high-fidelity logical magic states in 2025 19. Google’s Willow result, published in Science on October 15, 2026, demonstrates a complementary approach using magic-state cultivation on superconducting hardware 20. David Hayes explains why magic-state preparation and logical error correction measure different aspects of progress 21.
There is still work to be done. Decoding in the new color-code benchmarks was performed offline. The challenge now is to integrate error correction, magic-state resources and real-time control into longer, reliable computations.
Fidelity first. Time to solution always.
Fidelity remains the priority because it makes reliable computation at scale possible. But that does not mean waiting to improve speed. As physical operations move further below threshold, faster logical operations and parallel execution become increasingly important. We are accelerating transport, cooling, measurement, compilation, and parallel execution alongside continued fidelity improvements. The goal is to reduce both the time and the total cost of solving a useful problem.
The times they are a-changin’. Quantum computing is entering the fault-tolerant era, and the terms of the race are changing with it. Our below-threshold results on Helios mark that transition. Fast gate speed does not mean fast time to solution. What matters is how quickly the complete system solves an industrially relevant problem while outperforming the best classical solution. Our motto is simple: fidelity first, with speed built on that foundation. High fidelity means smaller codes and less overhead, so today’s slower physical gates can become tomorrow’s faster logical gates.
The slow gates now will later be fast…and the first one may later be last.
References
1 E. Fontana, M. S. Rudolph, R. Duncan, I. Rungger and C. Cîrstoiu, "Classical simulations of noisy variational quantum circuits," npj Quantum Information 11, Article 84 (2025). doi:10.1038/s41534-024-00955-1.
2 D. Aharonov, X. Gao, Z. Landau, Y. Liu and U. Vazirani, "A Polynomial-Time Classical Algorithm for Noisy Random Circuit Sampling," Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 945-957 (2023). doi:10.1145/3564246.3585234.
3 T. Schuster, C. Yin, X. Gao and N. Y. Yao, "A Polynomial-Time Classical Algorithm for Noisy Quantum Circuits," Physical Review X 15, 041018 (2025). doi:10.1103/xct1-7kf2.
4 N. Berthusen et al., "Experimental validation of a compact fault-tolerant architecture for trapped ions," arXiv:2609.03194 [quant-ph] (2026). doi:10.48550/arXiv.2609.03194.
5 A. Lavasani et al., "Quantum error correction well below the color code threshold," arXiv:2610.24077 [quant-ph] (2026). doi:10.48550/arXiv.2610.24077.
6 T. Proctor et al., "Benchmarking the computational power of quantum computers," arXiv:2609.12146 [quant-ph] (2026). doi:10.48550/arXiv.2609.12146.
7 Quantinuum, "Introducing the Quantum Universal Operations Performance System: QUOPS," September 14, 2026. https://www.quantinuum.com/blog/introducing-the-quantum-universal-operations-performance-system-quops.
8 M. Liu, R. Shaydulin, P. Niroula et al., "Certified randomness using a trapped-ion quantum processor," Nature 640, 343-348 (2025). doi:10.1038/s41586-025-08737-1.
9 S. Dasu et al., "Computing with many encoded logical qubits beyond break-even," arXiv:2602.22211 [quant-ph] (2026). doi:10.48550/arXiv.2602.22211.
10 E. Granet et al., "Superconducting pairing correlations on a trapped-ion quantum computer," arXiv:2511.02125 [quant-ph] (2025; revised 2026). doi:10.48550/arXiv.2511.02125.
11 A. Kandala, A. Javadi-Abhari and J. Gambetta, "Researchers demonstrate quantum advantage through trusted quantum computation," IBM Quantum Computing Blog, July 30, 2026.
https://www.ibm.com/quantum/blog/quantum-advantage.
12 Y. Quek, D. Stilck França, S. Khatri, J. J. Meyer and J. Eisert, "Exponentially tighter bounds on limitations of quantum error mitigation," Nature Physics 20, 1648-1658 (2024). doi:10.1038/s41567-024-02536-7.
13 A. Ransford et al., "A 98-qubit trapped-ion quantum computer with all-to-all connectivity," Nature 655, 81-86 (2026). doi:10.1038/s41586-026-10676-4.
14 G. M. Sommers, M. Foss-Feig, D. Hayes, D. A. Huse and M. J. Gullans, "Observation of a fault tolerance threshold with concatenated codes," Physical Review Research 7, 043271 (2025). doi:10.1103/v477-jw8l.
15 A. Paetznick et al., "Improved quantum processor logical error rates via correction and detection," Nature 654, 349-355 (2026). doi:10.1038/s41586-026-10628-y.
16 J. A. Montañez-Barrera and K. Michielsen, "Evaluating the performance of QEC primitives on quantum processors at large width and depth," arXiv:2610.05928 [quant-ph] (2026). doi:10.48550/arXiv.2610.05928.
17 Quantinuum, "Independent Study Shows Quantinuum Outperforming Superconducting Systems on Key Operations for Fault Tolerance," October 8, 2026.
https://www.quantinuum.com/blog/independent-study-shows-quantinuum-outperforming-superconducting-systems-on-key-operations-for-fault-tolerance.
18 Google Quantum AI and Collaborators, "Quantum error correction below the surface code threshold," Nature 638, 920-926 (2025). doi:10.1038/s41586-024-08449-y.
19 L. Daguerre, R. Blume-Kohout, N. C. Brown, D. Hayes and I. H. Kim, "Experimental Demonstration of High-Fidelity Logical Magic States from Code Switching," Physical Review X 15, 041008 (2025). doi:10.1103/dck4-x9c2.
20 E. Rosenfeld et al., "Magic state cultivation on a superconducting quantum processor," Science, eaee7961 (2026), published online October 15, 2026. doi:10.1126/science.aee7961.
21 D. Hayes, "Quantinuum and Google: Two Fault-Tolerance Results, Two Different Measures of Progress," Quantinuum Blog, October 21, 2026. https://www.quantinuum.com/blog/quantinuum-and-google-two-fault-tolerance-results-two-different-measures-of-progress.
Quantinuum and GE Vernova’s Advanced Research Center are launching a collaboration to apply quantum computing to one of the research community’s most consequential challenges: understanding the behavior behind high-temperature superconductivity. Advances in this field could, over the long term, transform the energy sector by enabling lighter generators, more compact and powerful motors, and electricity infrastructure that carries more power and better withstands faults.
The research will focus on Fermi–Hubbard modeling, a framework for studying how interacting electrons give rise to complex material properties. By combining Quantinuum’s full-stack quantum computing capabilities—including access to the company’s Helios computer—with GE Vernova’s expertise in material science and engineering simulation applied to industrial applications, the collaboration will bring an industry perspective to a promising area of quantum research.
Why superconductors matter for energy
As energy systems grow, the size, weight, and performance of electrical equipment become increasingly important constraints. High-temperature superconductors offer potential ways to address those limits through their ability to carry large electrical currents without resistance under suitable operating conditions.
Potential applications span power generation, electricity grids, and industrial electrification. Superconducting generators could reduce the weight and footprint of industrial equipment. Superconducting windings could also help make utility generators and industrial or marine propulsion motors more compact and efficient. In electricity networks, superconducting cables could carry more power through constrained spaces, while fault current limiters could help protect equipment during short circuits.
Realizing those benefits more widely depends on overcoming challenges in cooling, material performance, manufacturing, and cost. Better understanding of the physics behind high-temperature superconductivity could help inform the search for materials with more practical operating characteristics.
Why quantum computing may offer new tools for understanding high-temperature superconductivity
High-temperature superconductivity involves the collective behavior of strongly interacting electrons. Accurately modeling these systems is exceptionally difficult: the interactions that make them scientifically interesting also make important aspects of their behavior computationally challenging.
The Fermi–Hubbard model captures essential features of those interactions in a simplified mathematical framework. Although it is not a complete description of a material used in industrial applications, it provides a foundation for investigating mechanisms associated with high-temperature superconductivity.
Quantum computing offers a promising way to explore the physics. Quantinuum researchers have already measured superconducting pairing correlations—including s-wave and d-wave pairing—in Fermi–Hubbard models simulated on the Helios quantum computer. These results demonstrate the ability to prepare and probe quantum states relevant to superconductivity research, establishing a foundation for further investigation.
The collaboration with GE Vernova will build on that foundation, bringing industrial expertise to the selection and interpretation of research questions.
Connecting quantum capabilities with industrial expertise
The research will be enabled through access to Quantinuum’s full-stack capabilities, including cloud-based access to Helios, the world’s most accurate quantum computer1, and Nexus, Quantinuum’s developer platform. Nexus connects quantum hardware with the software tools researchers need to develop and run applications in a unified environment.
GE Vernova’s Advanced Research Center brings the expertise in material science, engineering, and industrial applications to the research and development activity.
“High-temperature superconductors could help us overcome important limits on the size, weight, and efficiency of electrical equipment, but understanding and improving these materials remains a significant scientific challenge,” said Jonathan Owens, PhD, Senior Scientist, Computational Materials Physics at GE Vernova. “By combining our materials simulation and industrial expertise with Quantinuum’s quantum computing capabilities, we can explore new approaches to these difficult problems while building the expertise to assess where quantum computing can make a meaningful contribution.”
The collaboration also extends GE Vernova’s exploration of quantum computing into a new area of materials research while supporting the development of its internal quantum talent. Its previous published work includes investigations of quantum algorithms for carbon-capture materials using simulated quantum processors.
Building toward industrial value
Translating insights from simplified models into practical materials requires sustained scientific and engineering work. Industrial expertise helps connect that effort to the properties, operating conditions, and manufacturing considerations that ultimately determine a material’s usefulness.
For Quantinuum, this collaboration is an opportunity to advance quantum simulation with an industrial collaborator that understands both the potential applications and the barriers to adoption. It brings together demonstrated computing capabilities, a challenging materials problem, and the expertise needed to connect scientific progress with industrial requirements.
The research collaboration aims to deepen understanding of this field and explore how quantum computing may contribute to future energy technologies.
1 Based on two-qubit gate fidelity as of December 31, 2025.
From Oracle's Quantum AI Compute beta program to upcoming Helios installation milestones and Oracle AI World, our partnership continues to gain momentum.
When Quantinuum and Oracle announced a strategic partnership earlier this year, the goal was not simply to deploy a quantum computer in the cloud. It was to create a practical path for bringing quantum computing into the enterprise environments where AI, data, and high-performance computing (HPC) already come together.
By combining Oracle Cloud Infrastructure (OCI)'s AI, data, and cloud capabilities with Helios, Quantinuum's most advanced quantum computer, we're helping lay the foundation for hybrid quantum AI computing at enterprise scale. The partnership reflects our belief that some of the most valuable future applications will emerge when quantum computing, AI and classical HPC work together to tackle problems that remain difficult to solve today.
Oracle Opens Early Access for Quantum AI Compute
That vision is now being put into practice. This month, Oracle launched the OCI Quantum AI Compute Beta Program, giving select organizations an opportunity to begin evaluating how quantum computing fits into existing workflows and computational challenges.
Through the program, approved participants will gain cloud-based access to Helios, the world's most accurate commercial quantum computer, alongside OCI's GPU and HPC infrastructure. For organizations exploring quantum computing, this creates a practical starting point: access to industry-leading hardware within an enterprise cloud environment, without the need to build specialized infrastructure of their own.
The goal is to help organizations understand where hybrid quantum AI workflows may create value across areas such as drug discovery, materials science, energy, financial modeling, and optimization.
For many organizations, the starting point is not quantum technology itself. It's a business or research problem. Where are today's approaches reaching meaningful limits? Which workloads consume disproportionate resources? And where could a new computational capability create an advantage?
Organizations interested in participating can apply through Oracle's OCI Preview Programs portal. Oracle will review applications and select participants based on eligibility, capacity and program requirements.
Quantinuum and Oracle Leadership Takes the Stage at Oracle AI World
The opportunity for hybrid quantum-AI computing will be a major topic at Oracle AI World 2026.
In a main stage session, "Powering the Next Era of Enterprise AI," Larry Stack, Chief Commercial Officer at Quantinuum, will join Mahesh Thiagarajan, Executive Vice President and Head of Oracle Cloud Infrastructure, to discuss how quantum computing, AI and classical HPC can work together to address some of the world's most computationally demanding challenges.
The discussion will focus on a core question many organizations are asking: where should they start? Rather than leading with technology, the conversation will explore how organizations can identify high-value problems, establish today's performance baseline, and evaluate where quantum computing ultimately provides an advantage.
Later in the day, Quantinuum and Oracle experts will take a deeper look at how bringing Helios into OCI provides managed access to quantum computing natively within an enterprise cloud environment to support hybrid quantum-AI workflows.
Building Momentum Toward Hybrid Quantum-AI Computing
The collaboration between Quantinuum and Oracle continues to progress from vision to execution.
With Helios advancing toward installation, Oracle's Quantum AI Compute program now open for early access, and enterprise interest growing around hybrid computing, the pieces are coming together. Organizations now have a practical way to begin exploring quantum computing through cloud-based access to Helios alongside AI, HPC, and existing enterprise workflows.
We look forward to continuing the conversation at Oracle AI World 2026 and working with customers who want to better understand where hybrid quantum-AI computing can create real-world value.
We're excited to continue that conversation at Oracle AI World 2026 and to help customers begin exploring what's possible through the OCI Quantum AI Compute Beta Program.