Introducing InQuanto v4.0
The latest version of our advanced quantum computational chemistry platform
夜色直播 is excited to announce the release of InQuanto鈩 v4.0, the latest version of our advanced quantum computational chemistry software. This update introduces new features and significant performance improvements, designed to help both industry and academic researchers accelerate their computational chemistry work.
If you're new to InQuanto or want to learn more about how to use it, we encourage you to explore our documentation.
InQuanto v4.0 is being released alongside 夜色直播 Nexus, our cloud-based platform for quantum software. Users with Nexus access can leverage the `inquanto-nexus` extension to, for example, take advantage of multiple available backends and seamless cloud storage.
In addition, InQuanto v4.0 introduces enhancements that allow users to run larger chemical simulations on quantum computers. Systems can be easily imported from classical codes using the widely supported FCIDUMP file format. These fermionic representations are then efficiently mapped to qubit representations, benefiting from performance improvements in InQuanto operators. For systems too large for quantum hardware experiments, users can now utilize the new `inquanto-cutensornet` extension to run simulations via tensor networks.
These updates enable users to compile and execute larger quantum circuits with greater ease, while accessing powerful compute resources through Nexus.
夜色直播 Nexus聽
InQuanto v4.0 is fully integrated with via the `inquanto-nexus` extension. This integration allows users to easily run experiments across a range of quantum backends, from simulators to hardware, and access results stored in Nexus cloud storage.
Results can be annotated for better searchability and seamlessly shared with others. Nexus also offers the Nexus Lab, which provides a preconfigured Jupyter environment for compiling circuits and executing jobs. The Lab is set up with InQuanto v4.0 and a full suite of related software, enabling users to get started quickly.聽
Enhanced Operator Performance
The `inquanto.mappings` submodule has received a significant performance enhancement in InQuanto v4.0. By integrating a set of operator classes written in C++, the team has increased the performance of the module past that of other open-source packages鈥 equivalent methods.聽
Like any other Python package, InQuanto can benefit from delegating tasks with high computational overhead to compiled languages such as C++. This prescription has been applied to the qubit encoding functions of the `inquanto.mappings` submodule, in which fermionic operators are mapped to their qubit operator equivalents. One such qubit encoding scheme is the Jordan-Wigner (JW) transformation.聽With respect to JW encoding as a benchmarking task, the integration of C++ operator classes in InQuanto v4.0 has yielded an execution time speed-up of two and a half times that of open-source competitors (Figure 1).
鈥
Figure 1. Performance comparison of Jordan Wigner (JW) operator mappings for LiH molecule in several basis sets of increasing size.聽
This is a substantial increase in performance that all users will benefit from. InQuanto users will still interact with the familiar Python classes such as `FermionOperator` and `QubitOperator` in v4.0. However, when the `mappings` module is called, the Python operator objects are converted to C++ equivalents and vice versa before and after the qubit encoding procedure (Figure 2). With future total integration of C++ operator classes, we can remove the conversion step and push the performance of the `mappings` module further. Tests, once again using the JW mappings scheme, show a 40 times execution time speed-up as compared to open-source competitors (Figure 1).
鈥
Figure 2. Representation of the conversion step from Python objects to C++ objects in the qubit encoding processes handled by the `inquanto.mappings` submodule in InQuanto v4.0.
Efficient classical pre-processing implementations such as this are a crucial step on the path to quantum advantage.聽As the number of physical qubits available on quantum computers increases, so will the size and complexity of the physical systems that can be simulated. To support this hardware upscaling, computational bottlenecks including those associated with the classical manipulation of operator objects must be alleviated. Aside from keeping pace with hardware advancements, it is important to enlarge the tractable system size in situations that do not involve quantum circuit execution, such as tensor network circuit simulation and resource estimation.
Leveraging Tensor Networks
Users with access to GPU capabilities can now take advantage of tensor networks to accelerate simulations in InQuanto v4.0. This is made possible by the `inquanto-cutensornet` extension, which interfaces InQuanto with the NVIDIA庐 cuTensorNet library. The `inquanto-cutensornet` extension leverages the `pytket-cutensornet` , which facilitates the conversion of `pytket` circuits into tensor networks to be evaluated using the NVIDIA庐 cuTensorNet library. This extension increases the size limit of circuits that can be simulated for chemistry applications. Future work will seek to integrate this functionality with our Nexus platform, allowing InQuanto users to employ the extension without requiring access to their own local GPU resources.
Here we demonstrate the use of the `CuTensorNetProtocol` passed to a VQE experiment. For the sake of brevity, we use the `get_system` method of `inquanto.express` to swiftly define the system, in this case H2 using the STO-3G basis-set.
from inquanto.algorithms import AlgorithmVQE
from inquanto.ansatzes import FermionSpaceAnsatzUCCD
from inquanto.computables import ExpectationValue, ExpectationValueDerivative
from inquanto.express import get_system
from inquanto.mappings import QubitMappingJordanWigner
from inquanto.minimizers import MinimizerScipy
from inquanto.extensions.cutensornet import CuTensorNetProtocol
fermion_hamiltonian, space, state = get_system("h2_sto3g.h5")
qubit_hamiltonian = fermion_hamiltonian.qubit_encode()
ansatz = FermionSpaceAnsatzUCCD(space, state, QubitMappingJordanWigner())
expectation_value = ExpectationValue(ansatz, qubit_hamiltonian)
gradient_expression = ExpectationValueDerivative(
ansatz, qubit_hamiltonian, ansatz.free_symbols_ordered()
)
protocol_tn = CuTensorNetProtocol()
vqe_tn = (
AlgorithmVQE(
objective_expression=expectation_value,
gradient_expression=gradient_expression,
minimizer=MinimizerScipy(),
initial_parameters=ansatz.state_symbols.construct_zeros(),
)
.build(protocol_objective=protocol_tn, protocol_gradient=protocol_tn)
.run()
)
print(vqe_tn.generate_report()["final_value"])
# -1.136846575472054
The inherently modular design of InQuanto allows for the seamless integration of new extensions and functionality. For instance, a user can simply modify existing code using `SparseStatevectorProtocol` to enable GPU acceleration through `inquanto-cutensornet`. It is worth noting that the extension is also compatible with shot-based simulation via the `CuTensorNetShotsBackend` provided by `pytket-cutensornet`.
鈥淗ybrid quantum-classical supercomputing is accelerating quantum computational chemistry research,鈥 said Tim Costa, Senior Director at NVIDIA庐. 鈥淲ith 夜色直播鈥檚 InQuanto v4.0 platform and NVIDIA鈥檚 cuQuantum SDK, InQuanto users now have access to unique tensor-network-based methods, enabling large-scale and high-precision quantum chemistry simulations.鈥
Classical Code Interface
As demonstrated by our `inquanto-pyscf` , we want InQuanto to easily interface with classical codes. In InQuanto v4.0, we have clarified integration with other classical codes such as Gaussian and Psi4. All that is required is an FCIDUMP file, which is a common output file for classical codes. An FCIDUMP file encodes all the one and two electron integrals required to set up a CI Hamiltonian. Users can bring their system from classical codes by passing an FCIDUMP file to the `FCIDumpRestricted` class and calling the `to_ChemistryRestrictedIntegralOperator` method or its unrestricted counterpart, depending on how they wish to treat spin. The resulting InQuanto operator object can be used within their workflow as they usually would.
Exposing TKET Compilation
Users can experiment with TKET鈥檚 latest circuit compilation tools in a straightforward manner with InQuanto v4.0. Circuit compilation now only occurs within the `inquanto.protocols` module. This allows users to define which optimization passes to run before and/or after the backend specific defaults, all in one line of code. Circuit compilation is a crucial step in all InQuanto workflows. As such, this structural change allows us to cleanly integrate new functionality through extensions such as `inquanto-nexus` and `inquanto-cutensornet`. Looking forward, beyond InQuanto v4.0, this change is a positive step towards bringing quantum error correction to InQuanto.
Conclusion
InQuanto v4.0 pushes the size of the chemical systems that a user can simulate on quantum computers. Users can import larger, carefully constructed systems from classical codes and encode them to optimized quantum circuits. They can then evaluate these circuits on quantum backends with `inquanto-nexus` or execute them as tensor networks using `inquanto-cutensornet`. We look forward to seeing how our users leverage InQuanto v4.0 to demonstrate the increasing power of quantum computational chemistry. If you are curious about InQuanto and want to read further, our initial release is very informative or visit the InQuanto website.
How to Access InQuanto
If you are interested in trying InQuanto, please request access or a demo at inquanto@quantinuum.com
About 夜色直播
夜色直播,聽the world鈥檚 largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. 夜色直播鈥檚 technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With over 500 employees, including 370+ scientists and engineers, 夜色直播 leads the quantum computing revolution across continents.聽
Quantum computing companies are poised to exceed $1 billion in revenues by the close of 2025, to McKinsey & Company, underscoring how today鈥檚 quantum computers are already delivering customer value in their current phase of development.
This figure is projected to reach upwards of $37 billion by 2030, rising in parallel with escalating demand, as well as with the scale of the machines and the complexity of problem sets of which they will be able to address. 聽
Several systems on the market today are fault-tolerant by design, meaning they are capable of suppressing error-causing noise to yield reliable calculations. However, the full potential of quantum computing to tackle problems of true industrial relevance, in areas like medicine, energy, and finance, remains contingent on an architecture that supports a fully fault-tolerant universal gate set with repeatable error correction鈥攁 capability that, until now, has eluded the industry. 聽
夜色直播 is the first鈥攁nd only鈥攃ompany to achieve this critical technical breakthrough, universally recognized as the essential precursor to scalable, industrial-scale quantum computing. This milestone provides us with the most de-risked development roadmap in the industry and positions us to fulfill our promise to deliver our universal, fully fault-tolerant quantum computer, Apollo, by 2029.
In this regard, 夜色直播 is the first company to step from the so-called 鈥淣ISQ鈥 (noisy intermediate-scale quantum) era towards utility-scale quantum computers.
Unpacking our achievement: first, a 鈥榝ull鈥 primer
A quantum computer uses operations called gates to process information in ways that even today鈥檚 fastest supercomputers cannot. The industry typically refers to two types of gates for quantum computers:
- Clifford gates, which can be easily simulated by classical computers, and are relatively easy to implement; and
- Non-Clifford gates, which are usually harder to implement, but are required to enable true quantum computation (when combined with their siblings).
A system that can run both gates is classified as and has the machinery to tackle the widest range of problems. Without non-Clifford gates, a quantum computer is non-universal and restricted to smaller, easier sets of tasks - and it can always be simulated by classical computers. This is like painting with a full palette of primary colors, versus only having one or two to work with. Simply put, a quantum computer that cannot implement 鈥榥on-Clifford鈥 gates is not really a quantum computer.
A fault-tolerant, or error-corrected, quantum computer detects and corrects its own errors (or faults) to produce reliable results. 夜色直播 has the best and brightest scientists dedicated to keeping our systems鈥 error rates the lowest in the world.
For a quantum computer to be fully fault-tolerant, every operation must be error-resilient, across Clifford gates and non-Clifford gates, and thus, performing 鈥渁 full gate set鈥 with error correction. While some groups have performed fully fault-tolerant gate sets in academic settings, these demonstrations were done with only a few qubits and 鈥攖oo high for any practical use.
Today, we have published that establishes 夜色直播 as the first company to develop a complete solution for a universal fully fault-tolerant quantum computer with repeatable error correction, and error rates low enough for real-world applications.
This is where the magic happens
The describes how scientists at 夜色直播 used our System Model H1-1 to perfect magic state production, a crucial technique for achieving a fully fault-tolerant universal gate set. In doing so, they set a record magic state infidelity (7x10-5), 10x better than any .
Our simulations show that our system could reach a magic state infidelity of 10^-10, or about one error per 10 billion operations, on a larger-scale computer with our current physical error rate. We anticipate reaching 10^-14, or about one error per 100 trillion operations, as we continue to advance our hardware. This means that our roadmap is now derisked.
Setting a record magic state infidelity was just the beginning. The paper also presents the first break-even two-qubit non-Clifford gate, demonstrating a logical error rate below the physical one. In doing so, the team set another record for two-qubit non-Clifford gate infidelity (2x10-4, almost 10x better than our physical error rate). Putting everything together, the team ran the first circuit that used a fully fault-tolerant universal gate set, a critical moment for our industry.
Flipping the switch
In the , co-authored with researchers at the University of California at Davis, we demonstrated an important technique for universal fault-tolerance called 鈥渃ode switching鈥.
Code switching describes switching between different error correcting codes. The team then used the technique to demonstrate the key ingredients for universal computation, this time using a code where we鈥檝e previously demonstrated full error correction and the other ingredients for universality.
In the process, the team set a new record for magic states in a distance-3 error correcting code, over 10x better than with error correction. Notably, this process only cost 28 qubits . This completes, for the first time, the ingredient list for a universal gate setin a system that also has real-time and repeatable QEC.
Fully equipped for fault-tolerance
Innovations like those described in these two papers can reduce estimates for qubit requirements by an order of magnitude, or more, bringing powerful quantum applications within reach far sooner.
With all of the required pieces now, finally, in place, we are 鈥榝ully鈥 equipped to become the first company to perform universal fully fault-tolerant computing鈥攋ust in time for the arrival of Helios, our next generation system launching this year, and what is very likely to remain as the most powerful quantum computer on the market until the launch of its successor, Sol, arriving in 2027.
If we are to create 鈥榥ext-gen鈥 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 夜色直播 continues to lead by demonstrating concrete progress 鈥 advancing from theoretical models to real quantum deployment.
The future of AI won't be built on yesterday鈥檚 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鈥攄esigned for quantum, from the ground up.
Around this time last year, we introduced Quixer, a state-of-the-art quantum-native transformer. Today, we鈥檙e 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 鈥 but for quantum-native computation. Because quantum computers differ fundamentally from classical computers, we expect a whole new host of valuable applications to emerge. 聽
Achieving that future requires models that are efficient, scalable, and actually run on today鈥檚 quantum hardware.
That鈥檚 what we鈥檝e built.
What makes Quixer different?
Until Quixer, quantum transformers were the result of a brute force 鈥渃opy-paste鈥 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鈥檚 not a translation 鈥 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鈥檚 next for Quixer?
We鈥檝e 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鈥檒l 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鈥檛 just about one model. It鈥檚 a signal that the quantum AI era has begun, and that 夜色直播 is leading the charge with real results, not empty hype.
Stay tuned. The revolution is only getting started.
Our team is participating in (ISC 2025) from June 10-13 in Hamburg, Germany!
As quantum computing accelerates, so does the urgency to integrate its capabilities into today鈥檚 high-performance computing (HPC) and AI environments. At ISC 2025, meet the 夜色直播 team to learn how the highest performing quantum systems on the market, combined with advanced software and powerful collaborations, are helping organizations take the next step in their compute strategy.
夜色直播 is leading the industry across every major vector: performance, hybrid integration, scientific innovation, global collaboration and ease of access.
- Our industry-leading quantum computer holds the record for performance with a Quantum Volume of 2虏鲁 = 8,388,608 and the highest fidelity on a commercially available QPU available to our users every time they access our systems.
- Our systems have been validated by a #1 ranking against competitors in a recent benchmarking study by J眉lich Research Centre.
- We鈥檝e laid out a clear roadmap to reach universal, fully fault-tolerant quantum computing by the end of the decade and will launch our next-generation system, Helios, later this year.
- We are advancing real-world hybrid compute with partners such as RIKEN, NVIDIA, SoftBank, STFC Hartree Center and are pioneering applications such as our own GenQAI framework.
Exhibit Hall
From June 10鈥13, in Hamburg, Germany, visit us at Booth B40 in the Exhibition Hall or attend one of our technical talks to explore how our quantum technologies are pushing the boundaries of what鈥檚 possible across HPC.
Presentations & Demos
Throughout ISC, our team will present on the most important topics in HPC and quantum computing integration鈥攆rom near-term hybrid use cases to hardware innovations and future roadmaps.
Multicore World Networking Event
- 鈥Monday, June 9 | 7:00pm 鈥 9:00 PM at Hofbr盲u Wirtshaus Esplanade
鈥In partnership with Multicore World, join us for a 夜色直播-sponsored Happy Hour to explore the present and future of quantum computing with 夜色直播 CCO, Dr. Nash Palaniswamy, and network with our team.
H1 x CUDA-Q Demonstration
- 鈥All Week at Booth B40
鈥We鈥檙e showcasing a live demonstration of NVIDIA鈥檚 CUDA-Q platform running on 夜色直播鈥檚 industry-leading quantum hardware. This new integration paves the way for hybrid compute solutions in optimization, AI, and chemistry.
鈥Register for a demo
HPC Solutions Forum
- 鈥Wednesday, June 11 | 2:20 鈥 2:40 PM
鈥鈥淓nabling Scientific Discovery with Generative Quantum AI鈥 鈥 Presented by Maud Einhorn, Technical Account Executive at 夜色直播, discover how hybrid quantum-classical workflows are powering novel use cases in scientific discovery.
See You There!
Whether you're exploring hybrid solutions today or planning for large-scale quantum deployment tomorrow, ISC 2025 is the place to begin the conversation.
We look forward to seeing you in Hamburg!