Quantinuum introduces hybrid solver for industrially relevant chemical modeling
New solution could have significant impacts for the automotive, aerospace, semiconductor and oil and gas industries
It’s believed that quantum computing will transform the way we solve chemistry problems, and the Quantinuum scientific team continues to push the envelope towards making that a reality.
In their latest research paper published on the arXiv, Quantinuum scientists describe a new hybrid classical-quantum solver for chemistry. The method they developed can model complex molecules at a new level of efficiency and precision.
Dr. Michał Krompiec, Scientific Project Manager, and his colleague Dr. David Muñoz Ramo, Head of Quantum Chemistry, co-authored the paper, "Strongly Contracted N-Electron Valence State Perturbation Theory Using Reduced Density Matrices from a Quantum Computer".
The implications are significant as their innovation “tackles one of the biggest bottlenecks in modelling molecules on quantum computers,” according to Dr. Krompiec.
Quantum computers are a natural platform to solve chemistry problems. Chemical molecules are made of many interacting electrons, and quantum mechanics can describe the behavior and energies of these electrons.
As Dr. Krompiec explains, “nature is not classical, it is quantum. We want to map the quantum system of interacting electrons into a quantum system of interacting qubits, and then solve it.”
Solving the full picture of electron interactions is extremely difficult, but fortunately it is not always necessary. Scientists usually simplify the task by focusing on the active space of the molecule, a smaller subset of the problem which matters most.
Even with these simplifications, difficulties remain. One challenge is carefully choosing this smaller subset, which describes strongly correlated electrons and is therefore more complex. Another challenge is accurately solving the rest of the system. Solving the chemistry of the complex subset can often be done from perturbation theory using so-called “multi-reference” methods.
In their work, the Quantinuum team came up with a new multi-reference technique. They maintain that only the strongly correlated part of the molecule should be calculated on a quantum computer. This is important, as this part usually scales exponentially with the size of the molecule, making it classically intractable.
The quantum algorithm they used on this part relied on measuring reduced density matrices and feeding them into a multi-reference perturbation theory calculation, a combination that had never been used in this context. Implementing the quantum electronic structure solver on the active space and using measured reduced density matrices makes the problem less computationally expensive and the solution more accurate.
The team tested their workflow on two molecules - H2 and Li2 – using Quantinuum’s hybrid solver implemented in the InQuanto quantum computational chemistry platform and IBM’s 27-qubit device. Quantinuum software is platform inclusive and is often tested on both its own H Series ion-trap quantum systems as well as others.
The non-strongly correlated regions of the molecules were run classically, as they would not benefit from a quantum speedup. The team’s results showed excellent agreement with previous models, meaning their method worked. Beyond that, the method showed great promise for reaching new levels of speed and accuracy for larger molecules.
The future impact of this work could create a new paradigm to perform quantum chemistry. The authors of the paper believe it may represent the best way of computing dynamic correlation corrections to active space-type quantum methods.
As Dr. Krompiec said, “Quantum chemistry can finally be solved with an application of a quantum solver. This can remove the factorial scaling which limits the applicability of this rigorous method to a very small subsystem.”
The idea to use a multi-reference method along with reduced density matrix measurement is quite novel and stems from the diverse backgrounds of the team at Quantinuum. It is a unique application of well-known quantum algorithms to a set of theoretical quantum chemistry problems.
What’s Next
The use cases are vast. Analysis of catalyst and material properties may first benefit from this new method, which will have a tremendous impact in the automotive, aerospace, fine chemicals, semiconductor, and energy industries.
Implementing this method on real hardware is limited by the current noise levels. But as the quality of the qubits increases, the method will unleash its full potential. Quantinuum’s System Model H1 trapped-ion hardware, Powered by Honeywell, benefits from high fidelity qubits, and will be a valuable resource for quantum chemists wishing to follow this work.
This hybrid quantum-classical method promises a path to quantum advantage for important chemistry problems, as machines become more powerful.
As Dr. Krompiec summarizes, “we haven’t just created a toy model that works for near-term devices. This is a fundamental method that will still be relevant as quantum computers continue to mature.”
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.
At the heart of quantum computing’s promise lies the ability to solve problems that are fundamentally out of reach for classical computers. One of the most powerful ways to unlock that promise is through a novel approach we call Generative Quantum AI, or GenQAI. A key element of this approach is the Generative Quantum Eigensolver (GQE).
GenQAI is based on a simple but powerful idea: combine the unique capabilities of quantum hardware with the flexibility and intelligence of AI. By using quantum systems to generate data, and then using AI to learn from and guide the generation of more data, we can create a powerful feedback loop that enables breakthroughs in diverse fields.
Unlike classical systems, our quantum processing unit (QPU) produces data that is extremely difficult, if not impossible, to generate classically. That gives us a unique edge: we’re not just feeding an AI more text from the internet; we’re giving it new and valuable data that can’t be obtained anywhere else.
The Search for Ground State Energy
One of the most compelling challenges in quantum chemistry and materials science is computing the properties of a molecule’s ground state. For any given molecule or material, the ground state is its lowest energy configuration. Understanding this state is essential for understanding molecular behavior and designing new drugs or materials.
The problem is that accurately computing this state for anything but the simplest systems is incredibly complicated. You cannot even do it by brute force—testing every possible state and measuring its energy—because the number of quantum states grows as a double-exponential, making this an ineffective solution. This illustrates the need for an intelligent way to search for the ground state energy and other molecular properties.
That’s where GQE comes in. GQE is a methodology that uses data from our quantum computers to train a transformer. The transformer then proposes promising trial quantum circuits; ones likely to prepare states with low energy. You can think of it as an AI-guided search engine for ground states. The novelty is in how our transformer is trained from scratch using data generated on our hardware.
Here's how it works:
- We start with a batch of trial quantum circuits, which are run on our QPU.
- Each circuit prepares a quantum state, and we measure the energy of that state with respect to the Hamiltonian for each one.
- Those measurements are then fed back into a transformer model (the same architecture behind models like GPT-2) to improve its outputs.
- The transformer generates a new distribution of circuits, biased toward ones that are more likely to find lower energy states.
- We sample a new batch from the distribution, run them on the QPU, and repeat.
- The system learns over time, narrowing in on the true ground state.
To test our system, we tackled a benchmark problem: finding the ground state energy of the hydrogen molecule (H₂). This is a problem with a known solution, which allows us to verify that our setup works as intended. As a result, our GQE system successfully found the ground state to within chemical accuracy.
To our knowledge, we’re the first to solve this problem using a combination of a QPU and a transformer, marking the beginning of a new era in computational chemistry.
The Future of Quantum Chemistry
The idea of using a generative model guided by quantum measurements can be extended to a whole class of problems—from combinatorial optimization to materials discovery, and potentially, even drug design.
By combining the power of quantum computing and AI we can unlock their unified full power. Our quantum processors can generate rich data that was previously unobtainable. Then, an AI can learn from that data. Together, they can tackle problems neither could solve alone.
This is just the beginning. We’re already looking at applying GQE to more complex molecules—ones that can’t currently be solved with existing methods, and we’re exploring how this methodology could be extended to real-world use cases. This opens many new doors in chemistry, and we are excited to see what comes next.
Last year, we joined forces with RIKEN, Japan's largest comprehensive research institution, to install our hardware at RIKEN’s campus in Wako, Saitama. This deployment is part of RIKEN’s project to build a quantum-HPC hybrid platform consisting of high-performance computing systems, such as the supercomputer Fugaku and Quantinuum Systems.
Today, a paper published in Physical Review Research marks the first of many breakthroughs coming from this international supercomputing partnership. The team from RIKEN and Quantinuum joined up with researchers from Keio University to show that quantum information can be delocalized (scrambled) using a quantum circuit modeled after periodically driven systems.
"Scrambling" of quantum information happens in many quantum systems, from those found in complex materials to black holes. Understanding information scrambling will help researchers better understand things like thermalization and chaos, both of which have wide reaching implications.
To visualize scrambling, imagine a set of particles (say bits in a memory), where one particle holds specific information that you want to know. As time marches on, the quantum information will spread out across the other bits, making it harder and harder to recover the original information from local (few-bit) measurements.
While many classical techniques exist for studying complex scrambling dynamics, quantum computing has been known as a promising tool for these types of studies, due to its inherently quantum nature and ease with implementing quantum elements like entanglement. The joint team proved that to be true with their latest result, which shows that not only can scrambling states be generated on a quantum computer, but that they behave as expected and are ripe for further study.
Thanks to this new understanding, we now know that the preparation, verification, and application of a scrambling state, a key quantum information state, can be consistently realized using currently available quantum computers. Read the paper here, and read more about our partnership with RIKEN here.
In our increasingly connected, data-driven world, cybersecurity threats are more frequent and sophisticated than ever. To safeguard modern life, government and business leaders are turning to quantum randomness.
What is quantum randomness, and why should you care?
The term to know: quantum random number generators (QRNGs).
QRNGs exploit quantum mechanics to generate truly random numbers, providing the highest level of cryptographic security. This supports, among many things:
- Protection of personal data
- Secure financial transactions
- Safeguarding of sensitive communications
- Prevention of unauthorized access to medical records
Quantum technologies, including QRNGs, could protect up to $1 trillion in digital assets annually, according to a recent report by the World Economic Forum and Accenture.
Which industries will see the most value from quantum randomness?
The World Economic Forum report identifies five industry groups where QRNGs offer high business value and clear commercialization potential within the next few years. Those include:
- Financial services
- Information and communication technology
- Chemicals and advanced materials
- Energy and utilities
- Pharmaceuticals and healthcare
In line with these trends, recent research by The Quantum Insider projects the quantum security market will grow from approximately $0.7 billion today to $10 billion by 2030.
When will quantum randomness reach commercialization?
Quantum randomness is already being deployed commercially:
- Early adopters use our Quantum Origin in data centers and smart devices.
- Amid rising cybersecurity threats, demand is growing in regulated industries and critical infrastructure.
Recognizing the value of QRNGs, the financial services sector is accelerating its path to commercialization.
- Last year, HSBC conducted a pilot combining Quantum Origin and post-quantum cryptography to future-proof gold tokens against “store now, decrypt-later” (SNDL) threats.
- And, just last week, JPMorganChase made headlines by using our quantum computer for the first successful demonstration of certified randomness.
On the basis of the latter achievement, we aim to broaden our cybersecurity portfolio with the addition of a certified randomness product in 2025.
How is quantum randomness being regulated?
The National Institute of Standards and Technology (NIST) defines the cryptographic regulations used in the U.S. and other countries.
- NIST’s SP 800-90B framework assesses the quality of random number generators.
- The framework is part of the FIPS 140 standard, which governs cryptographic systems operations.
- Organizations must comply with FIPS 140 for their cryptographic products to be used in regulated environments.
This week, we announced Quantum Origin received NIST SP 800-90B Entropy Source validation, marking the first software QRNG approved for use in regulated industries.
What does NIST validation mean for our customers?
This means Quantum Origin is now available for high-security cryptographic systems and integrates seamlessly with NIST-approved solutions without requiring recertification.
- Unlike hardware QRNGs, Quantum Origin requires no network connectivity, making it ideal for air-gapped systems.
- For federal agencies, it complements our "U.S. Made" designation, easing deployment in critical infrastructure.
- It adds further value for customers building hardware security modules, firewalls, PKIs, and IoT devices.
The NIST validation, combined with our peer-reviewed papers, further establishes Quantum Origin as the leading QRNG on the market.
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It is paramount for governments, commercial enterprises, and critical infrastructure to stay ahead of evolving cybersecurity threats to maintain societal and economic security.
Quantinuum delivers the highest quality quantum randomness, enabling our customers to confront the most advanced cybersecurity challenges present today.