Some games aren’t mere entertainment
System Model H1 beats classical system at game designed to test quantum mechanics
By Kevin Jackson for ҹɫֱ
Some might view games as merely entertainment but for Professor Emanuele Dalla Torre at and his team, playing games is useful for measuring the effectiveness of today’s commercial quantum computers.
In a recent study published in , Dalla Torre and two of his students, Meron Sheffer and Daniel Azses, describe how they ran a collaborative, mathematical game on different technologies to evaluate 1) whether the systems demonstrated quantum mechanical properties and 2) how often the machines delivered the correct results. The team then compared the results to those generated by a classical computer.
Of the technologies tested, only the ҹɫֱ System Model H1-1, Powered by Honeywell, outperformed the classical results. Dalla Torre said classical computers return the correct answer only 87.5 percent of the time. The H1-1 returned the correct answer 97 percent of the time. (The team also tested the game on the now-retired System Model H0, which achieved 85 percent.)
“What we see in the H1 is that the probability is not 100 percent, so it's not a perfect machine, but it is still significantly above the classical threshold. It's behaving quantum mechanically,” Dalla Torre said.
Playing the game
The mathematical game Dalla Torre and his team played requires non-local correlations. In other words, it’s a collaborative game in which parts of the system can’t communicate to solve challenges or score points.
“It's a collaborative game based on some mathematical rules, and the players score a point if they can satisfy all of them,” said Dalla Torre. “The key challenge is that during the game, the players cannot communicate among themselves. If they could communicate, it would be easy – but they can’t. Think of building something without being able to talk to each other. So, there is a limit to how much you can do. For the machines in this game, this is the classical threshold.”
Quantum computers are uniquely suited to solve such problems because they follow quantum mechanical properties, which allow for non-local effects. According to quantum mechanics, something that is in one place can instantaneously affect something else that is in a different place.
“What this experiment demonstrates is that there is a non-local effect, meaning that when you measure one of the qubits, you are actually affecting the others instantaneously,” Dalla Torre said.
Less noise, higher performance
Dalla Torre attributes the performance of the ҹɫֱ technology to their low level of “noise”.
All commercial quantum computers operating today experience noise or interference from a variety of sources. Eliminating or suppressing such noise is essential to scaling the technology and achieving fault tolerant systems, a design principle that prevents errors from cascading throughout a system and corrupting circuits.
“Noise in this context just means an imperfection – it’s like a typo,” Dalla Torre said “So, a quantum computer does a computation and sometimes it gives you the wrong answer. The technical term is NISQ, noisy intermediate scale quantum computing. This is the general name of all the devices that we have right now. These are devices that are quantum, but they are not perfect ones. They make some mistakes.”
For Dr. Brian Neyenhuis, Commercial Operations Group Leader at ҹɫֱ, projects such as Dalla Torre's are useful benchmarks of early quantum computers and, also help demonstrate and more clearly understand the difference between classical and quantum computation.
After seeing the initial results from the H0 system, he worked with Dalla Torre to run it again on the upgraded H1 system (still only using six qubits).
"We knew from a large number of standard benchmarks that the H1 system was a big step forward for us, but it was still nice to see such a clear signal that the improvements that we had made translated directly to better performance on this non-local game,” Dr. Neyenhuis said.
What’s next
Dalla Torre and his students completed the experiment through the platform. “Being able to do this kind of work on the cloud is vital for the growth of quantum experimentation,” he said. “The fact that I was sitting in Israel at and I could connect to the computers and use them using on the internet, that's something amazing.”
Dalla Torre and his team would like to expand this sort of research in the future, especially as commercial quantum computers add qubits and reduce noise.
About ҹɫֱ
ҹɫֱ, the world’s largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. ҹɫֱ’s technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With over 500 employees, including 370+ scientists and engineers, ҹɫֱ leads the quantum computing revolution across continents.
Back in 2020, we to increase our Quantum Volume (QV), a measure of computational power, by 10x per year for 5 years.
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. :
“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.
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Alongside the world’s biggest quantum volume, we have the industry’s . To that point, the table below breaks down the leading commercial specs for each quantum computing architecture.
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, ҹɫֱ’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 ҹɫֱ 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.
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 (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 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 ҹɫֱ Systems.
Today, marks the first of many breakthroughs coming from this international supercomputing partnership. The team from RIKEN and ҹɫֱ 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 , and read more about our partnership with RIKEN here.