Built for All: Introducing Our New Software Stack
Our next-generation quantum computer, Helios, will come online this year as more than a new chip. It will arrive as a full-stack platform that sets a new standard for the industry.
With our current and previous generation systems, H2 and H1, we have set industry records for the highest fidelities, pioneered the teleportation of logical qubits, and introduced the world’s first commercial application for quantum computers. Much of this success stems from the deep integration between our software and hardware.
Today, we are excited to share the details of our new software stack. Its features and benefits, outlined below, enable a lower barrier to entry, faster time-to-solution, industry-standard access, and the best possible user experience on Helios.
Most importantly, this stack is designed with the future in mind as ĢƵ advances toward universal, fully fault-tolerant quantum computing.
Watch the technical webinar on our new software stack
Our Next-Generation Software Stack: What’s New
Our Current Generation Software Stack
Currently, the solutions our customers explore on our quantum hardware, which span cybersecurity, quantum chemistry, and quantum AI, plus third-party programs, are all powered by two middleware technologies:
- TKET, an open-source tool kit used by developers to build quantum software programs; and
- Nexus, a cloud-based SaaS platform, is the pathway to access our hardware, as well as third-party hardware. Nexus is our all-in-one computing platform, the entry point to everything quantum.
Our Next Generation Software Stack
The launch of Helios will come with an upgraded software stack with new features. We’re introducing two key additions to the stack, specifically:
- Guppy, a new, open-source programming language based on Python, one of the most popular general-use programming languages for classical computing; and
- Selene, a platform that partially emulates Helios, is used to perform program analysis and verification. Selene can be seen almost as a “digital sister” for Helios.
Moving forward, users will now leverage Guppy to run software applications on Helios and our future systems. TKET will be used solely as a compiler tool chain and for the optimization of Guppy programs.
Nexus, which remains as the default pathway to access our hardware, and third-party hardware, has been upgraded to support Guppy and provide access to Selene. Nexus also supports Quantum Intermediate Representation (QIR), an industry standard, which enables developers to program with languages like , ensuring our stack stays accessible to the whole ecosystem.
With this new stack running on our next generation Helios system, several benefits will be delivered to the end user, including, but not limited to, improved time-to-solution and reduced memory error for programs critical to quantum error correction and utility-scale algorithms.
Below, we dive deeper into these upgrades and what they mean for our customers.
Introducing Guppy: A Purpose-Built Language for Quantum Programming
Designed for the Next Era of Quantum Computing
Guppy is a new programming language hosted in Python, providing developers with a familiar, accessible entry point into the next era of quantum computing.
As ĢƵ leads the transition from the noisy intermediate scale quantum (NISQ) era to fault-tolerant quantum computing, represents a fundamental departure from legacy circuit-building tools. Instead of forcing developers to construct programs gate-by-gate, a tedious and error-prone process, Guppy treats quantum programs as structured, dynamic software.
With native support for real-time feedback and common programming constructs like ‘if’ statements and ‘for’loops, Guppy enables developers to write complex, readable programs that adapt as the quantum system evolves. This approach unlocks unprecedented power and clarity, far surpassing traditional tools.
Designed with fault-tolerance in mind, Guppy also optimizes qubit resource management automatically, improving efficiency and reducing developer overhead.
All Guppy programs can be seamlessly submitted and managed through Nexus, our all-in-one quantum computing platform.
Find out more at
The Most Flexible Approach to Quantum Error Correction
When it comes to quantum error correction (QEC), flexibility is everything. That is why we designed Guppy to reduce barriers to entry to access necessary features for QEC.
Unlike platforms locked into rigid, hardware-specific codes, ĢƵ’s QCCD architecture gives developers the freedom to implement any QEC code. In a rapidly evolving field, this adaptability is critical: the ability to test and deploy the latest techniques can mean the difference between achieving quantum advantage and falling behind.
With Guppy, developers can implement advanced protocols such as magic state distillation and injection, quantum teleportation, and other measurement-based routines, all executed dynamically through our real-time control system. This creates an environment where researchers can push the limits of fault-tolerance now—not years from now.
In addition, users can employ NVIDIA’s CUDA-QX for out-of-the-box QEC, without needing to worry about writing their own decoders, simplifying the development of novel QEC codes.
By enabling a modular, programmable approach to QEC, our stack accelerates the path to fault-tolerance and positions us to scale quickly as more efficient codes emerge from the research frontier.
Real-Time Control for True Quantum Computing
Integrated seamlessly with Guppy is a next-generation control system powered by a new real-time engine, a key breakthrough for large-scale quantum computing.
This control layer makes our software stack the first commercial system to deliver full measurement-dependent control with undefined sequence length. In practical terms, that means operations can now be guided dynamically by quantum measurements as they occur—a critical step toward truly adaptive, fault-tolerant algorithms.
At the hardware level, features like real-time transport enable dynamic software capabilities, such as conditionals, loops, and recursion, which are all foundational for scaling from thousands to millions of qubits.
These advances deliver tangible performance gains, including faster time-to-solution, reduced memory error, and greater algorithmic efficiency, providing the foundational support required to convert algorithmic advances into useful real-world applications.
Meet Selene: A “Digital Sister” for Helios and Beyond
Quantum hardware access is limited, but development shouldn't be. Selene is our new open-source emulator, built to model realistic, entangled quantum behavior with exceptional detail and speed.
Unlike generic simulators, Selene captures advanced runtime behavior unique to Helios, including measurement-dependent control flow and hybrid quantum-classical logic. It runs Guppy programs out of the box, allowing developers to start building and testing immediately without waiting for machine time.
supports multiple simulation backends, giving users state-of-the-art options for their specific needs, including backends optimized for matrix product state and tensor network simulations using NVIDIA GPUs and cuQuantum. This ensures maximum performance both on the quantum processor and in simulation.
Nexus: Bringing It All Together
These new features, and more, are available through Nexus, our all-in-one quantum computing platform.
Nexus serves as the middle layer that connects every part of the stack, providing a cloud-native SaaS environment for full-stack workflows, including server-side Selene instances. Users can manage Guppy programs, analyze results, and collaborate with others, all within a single, streamlined platform.
Further, Selene users who submit quantum state-vector simulations—the most complete and powerful method to simulate a general quantum circuit on a classical computer—through Nexus will be leveraging the NVIDIA cuQuantum library for efficient GPU-powered simulation.
Bringing Us All Together
Our entire stack, including Nexus and Selene, supports the industry-standard Quantum Intermediate Representation (QIR) as input, allowing users to program in their preferred programming language. QIR provides a common format for accessing a range of quantum computing backends, and ĢƵ Helios will support the full Adaptive Profile QIR This means developers can generate programs for Helios using tools like NVIDIA CUDA-Q, Microsoft Q#, and ORNL XACC.
Always Looking Forward
Our customers choose ĢƵ as their top quantum computing partner because no one else matches our team or our results. We remain the leaders in quantum computing and the only provider of integrated quantum resources that will address our society’s most complex problems.
That future is already taking shape. With Helios and our new software stack, we are building the foundation for scalable, programmable, real-time quantum computing.
About ĢƵ
ĢƵ, the world’s largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. ĢƵ’s technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With over 500 employees, including 370+ scientists and engineers, ĢƵ leads the quantum computing revolution across continents.
This month, ĢƵ welcomed its global user community to the first-ever Q-Net Connect, an annual forum designed to spark collaboration, share insights, and accelerate innovation across our full-stack quantum computing platforms. Over two days, users came together not only to learn from one another, but to build the relationships and momentum that we believe will help define the next chapter of quantum computing.
Q-Net Connect 2026 drew over 170 attendees from around the world to Denver, Colorado, including representatives from commercial enterprises and startups, academia and research institutions, and the public sector and non-profits - all users of ĢƵ systems.
The program was packed with inspiring keynotes, technical tracks, and customer presentations. Attendees heard from leaders at ĢƵ, as well as our partners at NVIDIA, JPMorganChase and BlueQubit; professors from the University of New Mexico, the University of Nottingham and Harvard University; national labs, including NIST, Oak Ridge National Laboratory, Sandia National Laboratories and Los Alamos National Laboratory; and other distinguished guests from across the global quantum ecosystem.
Congratulations to Q-Net Connect 2026 Award Recipients!
The mission of the ĢƵ Q-Net user community is to create a space for shared learning, collaboration and connection for those who adopt ĢƵ’s hardware, software and middleware platform. At this year’s Q-Net Connect, we awarded four organizations who made notable efforts to champion this effort.
- JPMorganChase received the ‘Guppy Adopter Award’ for their exemplary adoption of our quantum programming language, Guppy, in their research workflows.
- Phasecraft, a UK and US-based quantum algorithms startup, received the ‘Rising Star’ award for demonstrating exceptional early impact and advancing science using ĢƵ hardware, which they published in a December 2025 .
- Qedma, a quantum software startup, received the ‘Startup Partner Engagement’ award for their sustained engagement with ĢƵ platforms dating back to our first commercially deployed quantum computer, H1.
- Anna Dalmasso from the University of Nottingham received our ‘New Student Award’ for her impressive debut project on ĢƵ hardware and for delivering outstanding results as a new Q-Net student user.
Congratulations, again, and thank you to everyone who contributed to the success of the first Q-Net Connect!
Become a Q-Net Member
Q-Net offers year‑round support through user access, developer tools, documentation, trainings, webinars, and events. Members enjoy many exclusive benefits, including being the first to hear about exclusive content, publications and promotional offers.
By joining the community, you will be invited to exclusive gatherings to hear about the latest breakthroughs and connect with industry experts driving quantum innovation. Members also get access to Q‑Net Connect recordings and stay connected for future community updates.
In a follow-up to our recent work with Hiverge using AI to discover algorithms for quantum chemistry, we’ve teamed up with Hiverge, Amazon Web Services (AWS) and NVIDIA to explore using AI to improve algorithms for combinatorial optimization.
With the rapid rise of Large Language Models (LLMs), people started asking “what if AI agents can serve as on-demand algorithm factories?” We have been working with Hiverge, an algorithm discovery company, AWS, and NVIDIA, to explore how LLMs can accelerate quantum computing research.
Hiverge – named for Hive, an AI that can develop algorithms – aims to make quantum algorithm design more accessible to researchers by translating high-level problem descriptions in mostly natural language into executable quantum circuits. The Hive takes the researcher’s initial sketch of an algorithm, as well as special constraints the researcher enumerates, and evolves it to a new algorithm that better meets the researcher’s needs. The output is expressed in terms of a familiar programming language, like Guppy or , making it particularly easy to implement.
The AI is called a “Hive” because it is a collective of LLM agents, all of whom are editing the same codebase. In this work, the Hive was made up of LLM powerhouses such as Gemini, ChatGPT, Claude, Llama, as well as which was accessed through AWS’ Amazon Bedrock service. Many models are included because researchers know that diversity is a strength – just like a team of human researchers working in a group, a variety of perspectives often leads to the strongest result.
Once the LLMs are assembled, the Hive calls on them to do the work writing the desired algorithm; no new training is required. The algorithms are then executed and their ‘fitness’ (how well they solve the problem) is measured. Unfit programs do not survive, while the fittest ones evolve to the next generation. This process repeats, much like the evolutionary process of nature itself.
After evolution, the fittest algorithm is selected by the researchers and tested on other instances of the problem. This is a crucial step as the researchers want to understand how well it can generalize.
In this most recent work, the joint team explored how AI can assist in the discovery of heuristic quantum optimization algorithms, a class of algorithms aimed at improving efficiency across critical workstreams. These span challenges like optimal power grid dispatch and storage placement, arranging fuel inside nuclear reactors, and molecular design and reaction pathway optimization in drug, material, and chemical discovery—where solutions could translate into maximizing operational efficiency, dramatic reduction in costs, and rapid acceleration in innovation.
In other AI approaches, such as reinforcement learning, models are trained to solve a problem, but the resulting "algorithm" is effectively ‘hidden’ within a neural network. Here, the algorithm is written in Guppy or CUDA-Q (or Python), making it human-interpretable and easier to deploy on new problem instances.
This work leveraged the NVIDIA CUDA-Q platform, running on powerful NVIDIA GPUs made accessible by AWS. It’s state-of-the art accelerated computing was crucial; the research explored highly complex problems, challenges that lie at the edge of classical computing capacity. Before running anything on ĢƵ’s quantum computer, the researchers first used NVIDIA accelerated computing to simulate the quantum algorithms and assess their fitness. Once a promising algorithm is discovered, it could then be deployed on quantum hardware, creating an exciting new approach for scaling quantum algorithm design.
More broadly, this work points to one of many ways in which classical compute, AI, and quantum computing are most powerful in symbiosis. AI can be used to improve quantum, as demonstrated here, just as quantum can be used to extend AI. Looking ahead, we envision AI evolving programs that express a combination of algorithmic primitives, much like human mathematicians, such as Peter Shor and Lov Grover, have done. After all, both humans and AI can learn from each other.
As quantum computing power grows, so does the difficulty of error correction. Meeting that demand requires tight integration with high-performance classical computing, which is why we’ve partnered with NVIDIA to push the boundaries of real-time decoding performance.
Realizing the full power of quantum computing requires more than just qubits, it requires error rates low enough to run meaningful algorithms at scale. Physical qubits are sensitive to noise, which limits their capacity to handle calculations beyond a certain scale. To move beyond these limits, physical qubits must be combined into logical qubits, with errors continuously detected and corrected in real time before they can propagate and corrupt the calculation. This approach, known as fault tolerance, is a foundational requirement for any quantum computer intended to solve problems of real-world significance.
Part of the challenge of fault tolerance is the computational complexity of correcting errors in real time. Doing so involves sending the error syndrome data to a classical co-processor, solving a complex mathematical problem on that processor, then sending the resulting correction back to the quantum processor - all fast enough that it doesn’t slow down the quantum computation. For this reason, Quantum Error Correction (QEC) is currently one of the most demanding use-cases for tight coupling between classical and quantum computing.
Given the difficulty of the task, we have partnered with NVIDIA, leaders in accelerated computing. With the help of NVIDIA’s ultra-fast GPUs (and the GPU-accelerated BP-OSD decoder developed by NVIDIA as part of library), we were able to demonstrate real-time decoding of Helios’ qubits, all in a system that can be connected directly to our quantum processors using .
While real-time decoding has been demonstrated before (notably, by our own scientists in this study), previous demonstrations were limited in their scalability and complexity.
In this demonstration, we used Brings’ code, a high-rate code that is possible with our all-to-all connectivity, to encode our physical qubits into noise-resilient logical qubits. Once we had them encoded, we ran gates as well as let them idle to see if we could catch and correct errors quickly and efficiently. We submitted the circuits via both as well as our own Guppy language, underlining our commitment to accessible, ecosystem-friendly quantum computing.
The results were excellent: we were able to perform low-latency decoding that returned results in the time we needed, even for the faster clock cycles that we expect in future generation machines.
A key part of the achievement here is that we performed something called “correlated” decoding. In correlated decoding, you offload work that would normally be performed on the QPU onto the classical decoder. This is because, in ‘standard’ decoding, as you improve your error correction capabilities, it takes more and more time on the QPU. Correlated decoding elides this cost, saving QPU time for the tasks that only the quantum computer can do.
Stay tuned for our forthcoming paper with all the details.