Join us at Q2B 2022

December 15, 2022

Q2B 2022 kicked off in Santa Clara, CA where Quantinuum presented among important influencers in the quantum computing industry. Our experts presented on a range of topics including industry trends, use cases for quantum hardware, middleware and software and technology updates for an audience of global top academics, industry end users, government representatives and quantum computing vendors. Here’s a recap of Quantinuum presentations:

Industry Trends:

Great Mashups: Steady Progress meets Exponential

President and COO Tony Uttley

Watch here -> https://www.youtube.com/watch?v=jZkcYjyB3rY

Across the Pond: How UK-US partnerships are delivering on the promise of Quantum Computing

President and COO Tony Uttley

Watch here -> https://www.youtube.com/watch?v=gK57xoBTt9I

Quantum Computing Innovation: The 2023 International State of Play

Chief Legal Officer and Chief Compliance Officer, Kaniah Konkoly-Thege 

Watch here -> https://www.youtube.com/watch?v=9Hq1LqpFnxQ

Use Cases for Automotive, Computational Chemistry, Finance, and More:

Entangling the Ecosystem: 5 Diverse Stories of Quantum Collaborations,

Technical Solutions Specialist, Mark Wolf, Ph.D.

Watch here -> https://www.youtube.com/watch?v=6QnRx8koQAw 

Computational chemistry on near-term quantum computers and beyond

Scientific Project Manager, Michal Krompiec

Watch here -> https://www.youtube.com/watch?v=iQwzMCfthD8

Quantum Computing Technology:

Quantinuum H-Series features and capabilities powered through Azure Quantum hybrid quantum computing stack

Sr. Director of Offering And Program Management, Jennifer Strabley and Microsoft’s Principal PM Lead, Azure Quantum, Fabrice Frachon

Watch here -> https://www.youtube.com/watch?v=M2AjQIKCQYQ 

If you’d like to learn more about these presentations and topics, please reach out. 

Quantinuum also celebrated its first anniversary with attendees. At Q2B 2021, Quantinuum announced the combination of Cambridge Quantum Computing and Honeywell Quantum, so it was only fitting to celebrate its one-year anniversary at the 2022 event. 

From Quantinuum’s launch in late 2021 as the world’s first fully integrated quantum tech company, its momentum quickly continued with the launches of Quantum Origin and InQuanto™, achieving 20 fully connected qubits, expanding our work with major partners, and reaching a quantum volume of 8192, just to name a few milestones. Learn more about Quantinuum in its first year below.

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. 

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May 12, 2025
Quantinuum Dominates the Quantum Landscape: New World-Record in Quantum Volume

Back in 2020, we made a promise to increase our Quantum Volume (QV), a measure of computational power, by 10x per year. 

Today, we’re pleased to share that we’ve followed through on our commitment: Our System Model H2 has reached a Quantum Volume of 2²³ = 8,388,608, proving not just that we always do what we say, but that our quantum computers are leading the world forward. 

The QV benchmark was developed by IBM to represent a machine’s performance, accounting for things like qubit count, coherence times, qubit connectivity, and error rates. In IBM's words

“the higher the Quantum Volume, the higher the potential for exploring solutions to real world problems across industry, government, and research."

Our announcement today is precisely what sets us apart from the competition. No one else has been bold enough to make a similar promise on such a challenging metric – and no one else has ever completed a five-year goal like this.

We chose QV because we believe it’s a great metric. For starters, it’s not gameable, like other metrics in the ecosystem. Also, it brings together all the relevant metrics in the NISQ era for moving towards fault tolerance, such as gate fidelity and connectivity. 

Our path to achieve a QV of over 8 million was led in part by Dr. Charlie Baldwin, who studied under the legendary Ivan H. Deutsch. Dr. Baldwin has made his name as a globally renowned expert in quantum hardware performance over the past decade, and it is because of his leadership that we don’t just claim to be the best, but that we can prove we are the best. 

Figure 1: All known published Quantum Volume measurements.
Sources: [1][2][3][4][5]

Alongside the world’s biggest quantum volume, we have the industry’s most benchmarked quantum computers. To that point, the table below breaks down the leading commercial specs for each quantum computing architecture. 

Table 1: Leading commercial spec for each listed architecture or demonstrated capabilities on commercial hardware.
Download Benchmarking Results

We’ve never shied away from benchmarking our machines, because we know the results will be impressive. It is our provably world-leading performance that has enabled us to demonstrate:

As we look ahead to our next generation system, Helios, Quantinuum’s Senior Director of Engineering, Dr. Brian Neyenhuis, reflects: “We finished our five-year commitment to Quantum Volume ahead of schedule, showing that we can do more than just maintain performance while increasing system size. We can improve performance while scaling.” 

Helios’ performance will exceed that of our previous machines, meaning that Quantinuum will continue to lead in performance while following through on our promises. 

As the undisputed industry leader, we’re racing against no one other than ourselves to deliver higher performance and to better serve our customers.

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May 1, 2025
GenQAI: A New Era at the Quantum-AI Frontier

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.

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Blog
April 11, 2025
Quantinuum’s partnership with RIKEN bears fruit

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.  

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