Introducing InQuanto v4.0
The latest version of our advanced quantum computational chemistry platform
Quantinuum is excited to announce the release of InQuanto™ 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 Quantinuum 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.
Quantinuum Nexus
InQuanto v4.0 is fully integrated with Quantinuum Nexus 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.
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’ equivalent methods.
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. 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).
Figure 1. Performance comparison of Jordan Wigner (JW) operator mappings for LiH molecule in several basis sets of increasing size.
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).
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. 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® cuTensorNet library. The `inquanto-cutensornet` extension leverages the `pytket-cutensornet` library, which facilitates the conversion of `pytket` circuits into tensor networks to be evaluated using the NVIDIA® 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`.
“Hybrid quantum-classical supercomputing is accelerating quantum computational chemistry research,” said Tim Costa, Senior Director at NVIDIA®. “With Quantinuum’s InQuanto v4.0 platform and NVIDIA’s cuQuantum SDK, InQuanto users now have access to unique tensor-network-based methods, enabling large-scale and high-precision quantum chemistry simulations.”
Classical Code Interface
As demonstrated by our `inquanto-pyscf` extension, 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’s 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 blogpost 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 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.
For enterprise and public sector executives evaluating quantum computing investment
Quantum computing is now a strategic priority for many organizations. It's on track to help solve some of the world's biggest challenges, from drug discovery, to materials science, to optimization problems – all at a scale classical computers simply can't reach. For executives responsible for R&D, technology strategy, or innovation investment, the question is no longer whether quantum computing matters. It's how to approach it wisely.
That's a harder question than it sounds. The quantum computing market is crowded, technical, and moving fast, and most of the guidance available is written for physicists, not for the executives who actually have to make the investment decision. Vendor claims are difficult to compare, pilot programs are easy to get wrong, and the gap between "quantum is exciting" and "quantum is worth investing in this year" isn't always well explained.
Our new guide, A Strategic Guide to Selecting the Right Quantum Computing Solution, is built to close that gap.
What's Inside
The guide is designed to give business and technology leaders a clear, practical path through four essential questions:
- Why quantum computing matters now, and why the window for early strategic advantage is open today
- How to evaluate vendors objectively, using a structured framework rather than marketing claims
- How to design an effective pilot, so early investment produces real, usable evidence
- How to take your first steps with confidence, whether you're just starting to explore or ready to scale
It also includes a glossary of key terms, so readers new to the field aren't left decoding jargon before they can evaluate a single vendor.
Who Should Read It
The guide is written for CTOs, CIOs, CISOs, R&D leaders, and program directors across enterprise and public sector organizations, at any stage of quantum familiarity. Whether your organization hasn't yet started exploring quantum computing, or you already have a program underway and are looking to sharpen your evaluation process, the framework inside is designed to apply.
How to Use It
The evaluation framework at the core of the guide isn't specific to any one vendor; it's designed to be applied to any quantum computing solution you're considering, so you can make an apples-to-apples comparison based on your organization's actual needs. The guide also walks through how Quantinuum maps to that same framework, and what it looks like to work with Quantinuum as a co-development partner, should you want a concrete reference point alongside the general framework.
Start With Confidence, Not Guesswork
Quantum computing is a strategic decision, not just a technical one. The organizations that approach it with a clear framework, rather than reacting to the noise, will be the ones positioned to capture real value as the technology matures.
- QUOPS is a new, architecture-agnostic benchmark designed to measure quantum performance across physical- and logical-qubit systems, using two metrics: Q (computation size) and Ω (operations per second).
- It addresses the limits of traditional metrics like qubit count, gate fidelity, and gate speed by measuring what a quantum system can actually execute successfully—including the effects of error correction, decoding, mitigation, compilation, and other system-level factors.
- QUOPS aims to create a common language for the industry, helping vendors demonstrate progress, enabling buyers and governments to make more objective procurement decisions, and giving researchers a standardized way to track progress toward utility-scale, fault-tolerant quantum computing.
Quantum computing is entering a new era. As systems move from Noisy Intermediate-Scale Quantum (NISQ) toward Fault-Tolerant Application-Scale Quantum (FASQ), traditional metrics like qubit count, gate fidelity, and gate speed are no longer enough to describe what a machine can actually deliver.
Introducing QUOPS
Developed by Sandia National Laboratories, with input from Quantinuum and NVIDIA, QUOPS—the Quantum Universal Operations Performance System—is a common, architecture-agnostic benchmark for measuring quantum performance across both physical- and logical-qubit systems on the path toward quantum utility.
QUOPS can be applied to different architectures, codes, modalities, and levels of fault tolerance. QUOPS runs the same randomized workloads across different computational shapes, measures whether each workload succeeds, identifies the boundary of a system’s capability region, and reports two summary metrics:
- Q: the largest benchmark circuit size that passes the success threshold inside a utility-motivated region. Size is defined as 2*(width)*(depth).
- Ω: the effective operations per second at that point.
The result is a direct measure of how much computation a system can perform and how quickly it can do so. Together, these measurements provide a two-dimensional view of capability while reducing system performance to a common currency: quantum operations.
Why Quantum Computing Needs a New Metric
Component-level metrics remain essential for engineering. Qubit count, two-qubit fidelity, and gate speed can reveal control errors, crosstalk, leakage, connectivity constraints, and other system limitations. But they do not necessarily predict system-level performance.
Fault tolerance makes this gap even larger. Physical operations become logical computation with the addition of logical encoding, syndrome measurement, decoding, logical gate construction, magic-state production, routing, and control. Ultimately, this means that fault tolerance expands the relevant currencies of computation. Code distance, logical fidelity, magic-state throughput, decoding, connectivity, and space-time volume can matter far more for performance than raw qubit count or individual gate speeds.
This creates a growing challenge for buyers, governments, and researchers. As organizations move from experimentation toward larger-scale and potentially on-premise quantum systems, they need to know a simple thing:
What computation can a machine actually execute successfully?
QUOPS addresses that question by measuring the integrated system rather than inferring performance from individual components.
This is particularly important as the field considers workloads requiring roughly 10⁹–10¹² operations on thousands of qubits. Today's measured capabilities are still orders of magnitude smaller; QUOPS turns that gap into a measurable quantity.
QUOPS for Procurement
QUOPS can also provide a practical layer for quantum procurement and planning.
HPC centers need to understand when quantum computing will become useful for real workloads. Customers may have a goal of procuring a system that can, for example, run a trillion error-free operations. Today, answering these questions can require complex resource estimates that depend on hardware modality, QEC code, magic-state factories, decoding, compilation, and other architectural choices.
In both cases, QUOPS provides a simpler system-level reference point: Q describes the size of computation a machine can execute, while Ω describes its effective throughput. Furthermore, because QUOPS is architecture-neutral and includes anti-gaming provisions, it can also help buyers compare competing systems without relying solely on vendor-selected metrics or announcements.
How Quantinuum stacks up
While QUOPS is a new benchmark, it has already been measured on several vendors’ hardware. This marks an important step for our industry: we can now compare vendors directly, assessing their capabilities in a way that flattens the differences introduced by modality and architecture choices.
Figure 1. The QUOPS capability region and score for state-of-the-art processors from Quantinuum, Google, and IBM (adapted from Figure 2 of the scientific publication co-authored by Quantinuum, Sandia National Laboratories, and NVIDIA). QUOPS specifies a random circuit construction that can be built for a specified width (number of qubits) and size (number of quantum gates). A set of circuits is run at several width and size points and the average fidelity of those circuits are measured and compared to a predefined threshold. Each labeled point above represents experimental data from QUOPS circuits that passed the threshold with high confidence. The lines are filled capability limits of each machine between the points. The stars indicate the QUOPS score (Q), which is the experimental data point that passes the threshold with maximum size inside the shaded cone of width2 ≤ size ≤ width3.
Figure 2. The QUOPS score (Q) vs rate (Ω) for state-of-the-art processors from Quantinuum, Google, and IBM (adapted from Figure 2 of the QUOPS scientific publication co-authored by Quantinuum, Sandia National Laboratories, and NVIDIA). Each point is the maximum QUOPS circuit size that passes the threshold within the specified cone and rate that it was run. The dashed lines indicate the extrapolated effect of error mitigation, which attenuates the rate by including the shot overhead needed for general-purpose error mitigation. The gradient lines show the estimated runtime of a circuit at a given score and rate.
Figures 1 and 2 show how QUOPS quantifies the capability tradeoffs between different systems. Willow and Boston are superconducting systems with very fast gate speeds but limited connectivity, while Helios is a trapped-ion QCCD system with effective all-to-all connectivity but much slower gates. Willow and Boston have smaller capability regions and QUOPS scores but higher QUOPS rates; while Helios reaches larger capability regions and QUOPS scores but lower QUOPS rates. All three systems have the ability to trade speed for larger circuits with error mitigation. This is commonly assumed in the community but is nicely quantified with the QUOPS rate, which accounts for the corresponding sampling overheads of general error mitigation techniques (as shown by the dashed lines in Figure 2).
Building a New Benchmarking Ecosystem
QUOPS will not replace every quantum benchmark. The field will continue to need application-specific suites, component-level measurements, hybrid-HPC benchmarks, and independent verification.
QUOPS instead serves as a common system-level yardstick that can make roadmaps more comparable, procurement more objective, and progress easier to track.
We are calling on vendors to report QUOPS metrics (Q, Ω) and capability regions alongside existing metrics, buyers and agencies to consider QUOPS thresholds in RFPs, and researchers to contribute fault-tolerant architectures and resource estimates.
As quantum computers become fault tolerant, success will no longer be defined simply by how many qubits a machine contains or how low its error rates are.
It will be defined by the computation the machine can deliver.
QUOPS is a step toward measuring that capability—and toward giving the quantum industry a benchmark built for the era ahead.
- A new experiment tests what makes a quantum computer genuinely quantum by using a simple game that demonstrates a provable advantage from quantum superposition, without relying on entanglement or assumptions about classical computational difficulty.
- The test was demonstrated on Quantinuum’s System Model H2, where researchers ran thousands of circuits and observed results close to theoretical quantum predictions. The approach also offers an efficiently verifiable way to test for non-classical behavior.
- The work provides a new way to think about quantum-computing verification: rather than focusing only on metrics like qubit count, we can ask whether a machine demonstrates capabilities that fundamentally distinguish quantum systems from classical ones. This validates that the computer is working as intended.
Building a quantum computer is one thing. Showing that it is genuinely using quantum mechanics is another.
A new experiment, just published in Nature Communications, takes a fresh approach to that question. Instead of relying on entanglement or the complex calculations often used to benchmark quantum computers, researchers designed a simple game (initially published in Physical Review Letters) that tests something more fundamental: quantum superposition.
Using superposition, the team constructed a game where quantum mechanics provides a provable advantage over classical approaches. Once the game was set, the team ran it on real hardware. The results showed a clear performance gap between the best possible classical system and our System Model H2 – a gap that only grew as the test became more difficult.
A game that classical computers can’t win
The game is played by a single player with access to a computer. The player receives a quantum state representing a set of numbers—for example, {0, 1, 5, 7}. Their goal is to return a number that belongs to the complement of that set: {2, 3, 4, 6}.
That sounds simple. But as the size of the sets grows, something remarkable happens.
A classical strategy needs to test many numbers to succeed. A quantum strategy, however, succeeds in one step. The authors show that the quantum strategy has a score that grows exponentially faster.
Importantly, this isn't based on an assumption that this problem is difficult for classical computers. The separation is mathematically proven. In other words, the researchers can show that the quantum advantage exists without relying on unproven assumptions from complexity theory.
Using our System Model H2, the experimenters were able to confirm the theoretically derived separation between the quantum and the classical strategy (up to the largest sizes they could fit on the quantum processor) with high confidence – showing that the violation remained close to exponential.
Testing quantum mechanics without entanglement
Many famous experiments testing quantum behavior rely on entanglement and non-locality, where multiple parties share parts of a quantum system.
This experiment is different.
There is only one player, who has access to the entire quantum system. The advantage comes from superposition—the ability of a quantum system to exist in a combination of states until it is measured.
That distinction matters because it provides another way to ask whether a quantum computer is actually behaving quantum mechanically.
The researchers turned their game into an experimental test and ran thousands of different circuits on Quantinuum's System Model H2. The scores they observed were close to the theoretical predictions for a quantum strategy.
Why verification matters
One of the challenges with existing quantum-computing demonstrations is figuring out whether the machine really produced the result it was supposed to produce.
For example, random circuit sampling can be extremely difficult to verify classically as systems become larger. That creates a tension: you want to demonstrate that a quantum computer is doing something a classical computer cannot easily reproduce, but you also need a practical way to check the result.
The complement-sampling game offers a different approach. The violation of classical performance can be efficiently verified with a classical computer.
That makes the test potentially more scalable: you don't need to reproduce the entire quantum computation on a classical computer just to determine whether the machine demonstrated non-classical behavior.
So, what makes a quantum computer “quantum”?
The deeper message of the experiment is that demonstrating a quantum computer isn't simply about having qubits.
A convincing demonstration should show that the machine is exploiting properties that genuinely distinguish quantum computation from classical computation. Here, the researchers focus on one of those defining properties—superposition—and construct a game where quantum mechanics provides a provable advantage.
This first experimental demonstration of complement sampling doesn't close every possible loophole, which is common for this sort of experiment – closing the major experimental loopholes in Bell-inequality tests took decades—a body of work that ultimately contributed to the 2022 Nobel Prize in Physics. The researchers explicitly note that the implementation relies on assumptions about how the input state is prepared, so the experimental results should be interpreted with some caution.
Still, the work provides a new way to probe the boundary between classical and quantum computation.
And that may be the most interesting part: rather than asking only “How many qubits does the machine have?”, we can ask a more meaningful question—
“What can this machine do that only a quantum system can?”
That is ultimately what it takes for a quantum computer to actually be quantum.