Announcing the Launch of Quantinuum Nexus: Our All-in-One Quantum Computing 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).

‍

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).

‍

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

Our team is making progress on the path towards “non-Abelian” quantum computing, which promises both fault tolerance and significant resource savings.

Computing with non-Abelian anyons, which are a type of quasiparticle, is sought after as it offers an enticing alternative to some of the biggest challenges in mainstream quantum computing. Estimates vary, but some scientists have calculated that some of the trickiest parts, like T gates and magic states distillation, can take up to 90% of the computer’s resources (when running something such as Shor’s algorithm). The non-abelian approach to quantum computing could mitigate this issue.

In a new paper in collaboration with Harvard and CalTech, our team is one step closer to realizing fault-tolerant non-Abelian quantum computing. This paper is a follow-up to our recent work published in Nature, where we demonstrated control of non-Abelian anyons. This marks a key step toward non-Abelian computing, and we are the only company who has achieved this. Additionally, we are the only company offering commercially-available mid-circuit measurement and feed-forward capabilities, which will be vital as we advance our research in this area.

In this paper, our team prepared the ground state of the “Z3” toric code – meaning this special state of matter was prepared in qutrit (3 states) Hilbert space. Before now, topological order has only been prepared in qubit (2 states) Hilbert spaces. This allowed them to explore the effect of defects in the lattice (for the experts, this was the “parafermion” defect as well as the “charge-conjugation” defect. They then entangled two pairs of charge conjugation defects, making a Bell pair.

All these accomplishments are critical steppingstones towards the non-Abelian anyons of the “S3” toric code, which is the non-Abelian approach that promises both huge resource savings previously discussed because it (unlike most quantum error correction codes) provides a universal gate set. The high-fidelity preparation our team accomplished in this paper suggests that we are very close to achieving a universal topological gate set, which will be an incredible “first” in the quantum computing community.

This work is another feather in our cap in terms of quantum error correction (QEC) research, a field we are leaders in. We recently demonstrated a significant reduction in circuit error rates in collaboration with Microsoft, we performed high fidelity and fault-tolerant teleportation of logical qubits, and we independently demonstrated the first implementation of the Quantum Fourier Transform with error correction. We’ve surpassed the “break-even” point multiple times, recently doing so entangling 4 logical qubits in a non-local code. This latest work in non-Abelian QEC is yet another crucial milestone for the community that we have rigorously passed before anyone else.

This world-class work is enabled by the native flexibility of our Quantum Charge Coupled Device (QCCD) architecture and its best-in-class fidelity. Our world-leading hardware combined with our team of over 350 PhD scientists means that we have the capacity to efficiently investigate a large variety of error correcting codes and fault-tolerant methods, while supporting our partners to do the same. Fault tolerance is one of the most critical challenges our industry faces, and we are proud to be leading the way towards large scale, fault-tolerant quantum computing.

We are thrilled to announce a groundbreaking addition to our technology suite: the Quantum Error Correction (QEC) decoder toolkit. This essential tool empowers users to decode syndromes and implement real-time corrections, an essential step towards achieving fault-tolerant quantum computing. As the only company offering this crucial capability to our users, we are paving the way for the future of quantum technology.

We are dedicated to realizing universal fault-tolerant quantum computing by the end of this decade. A key component of this mission is equipping our customers with essential QEC workflows, making advanced quantum computing more accessible than ever before.

Our QEC decoding toolkit is enabled by our real-time hybrid compute capability, which executes Web Assembly (Wasm) in our stack in both hardware and emulator environments. This enables the use of libraries (like linear algebra and graph libraries) and complex data structures (like vectors and graphs).

Our real-time hybrid compute capability enables a new frontier in classical-quantum hybrid computing. Our release of the QEC decoder toolkit marks a maturing from just running quantum circuits to running full quantum algorithms, interacting in depth with classical resources in real-time so that each platform (quantum, classical) can be focused where it performs best.

QEC decoding is one of the most exciting – and necessary – applications of hybrid computing capacity. Before now, error correction needed to be done with lookup tables: a list specifying the correction for each syndrome. This is not scalable: the number of syndromes grows exponentially with the distance (which is like the “error correcting power”) of the code. This means that codes hefty enough to run, say, Shor’s algorithm would need a lookup table too massive to search or even store properly.

For universal fault-tolerant quantum computing to become a reality, we need to decode error syndromes algorithmically. During algorithmic decoding, the syndrome is sent to a classical computer which solves (for example) a graph problem to determine the correction to make.

Algorithmic decoding is only half of the puzzle though – the other key ingredient is being able to decode syndromes and correct errors in real time. For universal, fully fault-tolerant computing, real-time decoding is necessary: one can’t just push all corrections to the end of the computation because the errors will propagate and overwhelm the computation. Errors must be corrected as the computation proceeds.

In real-time algorithmic decoding, the syndrome is sent to a classical computer while the qubits are held in stasis , then the computed correction is applied before the computation proceeds.  Alternatively, one can compute the correction while the computation proceeds in parallel, then it is retrieved when needed. This flexibility in implementation allows for maximum freedom in algorithmic design.

Our real-time co-compute capability combined with our industry-leading coherence times (up to 10,000x longer than competitors) is what allows us to be the first to release this capacity to our customers. Our long coherence times also enable our users to benefit from more complex decoders that offer superior results.

Our QEC toolkit is fully flexible and will work with any QEC code - allowing our customers to build their own decoders and explore the results. It also enables users to better understand what fault-tolerant computing on actual hardware is like and improve on ideas developed via theory and simulation. This means building better decoders for the real world.

Our toolkit includes three use cases and includes the relevant source-code needed to test and compile the Wasm binaries. These use cases are:

- Repeat Until Success: Conditionally adding quantum operations to a circuit based on equality comparisons with an in-memory Wasm variable.

- Repetition Code: [[3,1,2]] code that encodes 3 physical qubits into 1 logical qubit, with code distance of 2.

- Surface Code: [[9,1,3]] code that encodes 9 physical qubits into 1 logical qubit, with a code distance of 3.

This is just the beginning of our promise to deliver universal, fault-tolerant quantum computing by the end of the decade. We are proud to be the only company offering advanced capabilities like this to our customers, and to be leading the way towards practical QEC.