With 20 Qubits, the H1-1 Quantum Runs More Complex Algorithm
Our flagship quantum computer, System Model H1-1, is now running on 20 qubits
We sat down with Brian Neyenhuis, Quantinuum’s director of commercial operations to ask him about the 20-qubit upgrade, some of the technical details, and how this launch paves the way for scaling trapped-ion quantum computers in the future.
What are some of the key upgrades made to the H1-1 machine?
The biggest, or maybe the most notable, is that we expanded the number of fully connected qubits from 12 to 20. That is a significant increase and the most qubits we’ve added to an existing machine. Last year, we added two fully connected qubits to the 10 qubits H1-1 already had. It was a major accomplishment at the time. Now, that seems easy compared to this upgrade because for us, it is not as simple as adding qubits.
To add eight more qubits and maintain all-to-all connectivity, we upgraded the optics that deliver the light used to control our qubits. Previously, we were only delivering the light needed to complete quantum gates to three different regions of the trap, which we call gate zones. Now we can address all five zones in our trap simultaneously. This enables us to complete more single-qubit or two-qubit gates in parallel, which means users can run complex algorithms without experiencing a slowdown.
How does this compare to previous hardware upgrades?
This one was significantly more involved than previous upgrades. Although we didn’t modify the trap at the heart of the computer or the vacuum chamber and cryostat that enclose it, we redesigned the entire optical delivery system. This was necessary so as not to deliver light to more regions of the trap, but also to improve stability.
Increasing the size and complexity of the machine without improving the stability would be a recipe for disaster. Because we were able to improve the stability, we were able to add more qubits without sacrificing performance or key features our users expect such as all-to-all connectivity, high single and two-qubit gate fidelities, and mid-circuit measurement.
Why is the increase in zones significant?
The gate zones are where all the interesting quantum stuff happens. More zones allow us to run more quantum operations in parallel, allowing for faster, more complex algorithms.
What's the connection between more zones and more qubits?
Having more gate zones allows us to use more qubits in an efficient way.
Because we can do all these operations in five different locations in parallel, it finally makes sense to put more qubits into the trap. We could have loaded more qubits into earlier versions of the system, but without additional gate zones, it doesn’t make a lot of sense. In fact, doing that would create a bottleneck with qubits waiting for their turn to do a two-qubit gate, which then slows down an algorithm. Now, we can do five quantum gates in parallel, which allows us to run more complex algorithms without sacrificing speed.
Twenty qubits are probably where this generation of traps ends. There is a possibility to add a handful more, but it feels like this is probably the most efficient number for these H1 Systems due to layout of the trap. But future generations, some of which are already trapping ions in the lab today, will use even more qubits and with the same or better efficiency.
What is the “ion dance”?
In the QCCD architecture, trapped ions are easy to move around. By applying the right set of voltages to the trap — a small, electrode-filled device that holds qubits in place — we can arbitrarily rearrange the chain of qubits so any qubit can pair with any other and perform a quantum gate. So, you can think of any algorithm as a set of steps where we shuffle all the qubits to pair them up for the next set of gates, move them into the gate zones, and then shuffle them again to set them up for the next set of gates. The ions “dance” across the trap moving from partner to partner to execute a quantum circuit.
Some circuits, like quantum volume circuits, are densely packed, meaning that every possible pair wants to do a gate at each step of the circuit. Other circuits are very loosely packed, meaning you can only do a few gates in parallel before moving on to the next slice because you need to reuse one of those qubits with a different partner.
Although this dance may sound complicated, it makes it very easy to program our quantum computer. A user sends us a time-ordered set of gates without having to think about the layout of the qubits, and our compiler figures out how to pair up the appropriate qubits to make it happen. You don't have to worry about which ones are next to each other because any pair of qubits is equal to all the others. And, at any step, we can completely rearrange this chain and put any two qubits next to each other.
It’s like a square dance where someone calls out directions to the dancers.
Anything else in the works for Quantinuum’s hardware this year?
We will continue to work with our customers to improve our system performance and their overall experience. One of the reasons we have a commercial system now is to allow our customers to program their algorithms on a real machine. They're dealing with all the constraints of real quantum hardware. They're pushing on their algorithms while we're pushing on the hardware, to get the fastest iterations.
As they learn new things about their algorithm, we learn what the most important things are to improve. And we work on those. We are learning a lot from our customers, and they are learning a lot by running on our hardware.
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 a novel technology to be successful, it must prove that it is both useful and works as described.
Checking that our computers “work as described” is called benchmarking and verification by the experts. We are proud to be leaders in this field, with the most benchmarked quantum processors in the world. We also work with National Laboratories in various countries to develop new benchmarking techniques and standards. Additionally, we have our own team of experts leading the field in benchmarking and verification.
Currently, a lot of verification (i.e. checking that you got the right answer) is done by classical computers – most quantum processors can still be simulated by a classical computer. As we move towards quantum processors that are hard (or impossible) to simulate, this introduces a problem: how can we keep checking that our technology is working correctly without simulating it?
We recently partnered with the UK’s Quantum Software Lab to develop a novel and scalable verification and benchmarking protocol that will help us as we make the transition to quantum processors that cannot be simulated.
This new protocol does not require classical simulation, or the transfer of a qubit between two parties. The team’s “on-chip” verification protocol eliminates the need for a physically separated verifier and makes no assumptions about the processor’s noise. To top it all off, this new protocol is qubit-efficient.
The team’s protocol is application-agnostic, benefiting all users. Further, the protocol is optimized to our QCCD hardware, meaning that we have a path towards verified quantum advantage – as we compute more things that cannot be classically simulated, we will be able to check that what we are doing is right.
Running the protocol on Quantinuum System Model H1, the team ended up performing the largest verified Measurement Based Quantum Computing (MBQC) circuit to date. This was enabled by our System Model H1’s low cross-talk gate zones, mid-circuit measurement and reset, and long coherence times. By performing the largest verified MBQC computation to date, and by verifying computations significantly larger than any others to be verified before, we reaffirm the Quantinuum Systems as best-in-class.
Particle accelerators like the LHC take serious computing power. Often on the bleeding-edge of computing technology, accelerator projects sometimes even drive innovations in computing. In fact, while there is some controversy over exactly where the world wide web was created, it is often attributed to Tim Berners-Lee at CERN, who developed it to meet the demand for automated information-sharing between scientists in universities and institutes around the world.
With annual data generated by accelerators in excess of exabytes (a billion gigabytes), tens of millions of lines of code written to support the experiments, and incredibly demanding hardware requirements, it’s no surprise that the High Energy Physics community is interested in quantum computing, which offers real solutions to some of their hardest problems. Furthermore, the HEP community is well-positioned to support the early stages of technological development: with budgets in the 10s of billions per year and tens of thousands of scientists and engineers working on accelerator and computational physics, this is a ripe industry for quantum computing to tap.
As the authors of this paper stated: “[Quantum Computing] encompasses several defining characteristics that are of particular interest to experimental HEP: the potential for quantum speed-up in processing time, sensitivity to sources of correlations in data, and increased expressivity of quantum systems... Experiments running on high-luminosity accelerators need faster algorithms; identification and reconstruction algorithms need to capture correlations in signals; simulation and inference tools need to express and calculate functions that are classically intractable”
The authors go on to state: “Within the existing data reconstruction and analysis paradigm, access to algorithms that exhibit quantum speed-ups would revolutionize the simulation of large-scale quantum systems and the processing of data from complex experimental set-ups. This would enable a new generation of precision measurements to probe deeper into the nature of the universe. Existing measurements may contain the signatures of underlying quantum correlations or other sources of new physics that are inaccessible to classical analysis techniques. Quantum algorithms that leverage these properties could potentially extract more information from a given dataset than classical algorithms.”
Our scientists have been working with a team at DESY, one of the world’s leading accelerator centers, to bring the power of quantum computing to particle physics. DESY, short for Deutsches Elektronen-Synchrotron, is a national research center for fundamental science located in Hamburg and Zeuthen, where the Center for Quantum Technologies and Applications (CQTA) is based. DESY operates, develops, and constructs particle accelerators used to investigate the structure, dynamics and function of matter, and conducts a broad spectrum of interdisciplinary scientific research. DESY employs about 3,000 staff members from more than 60 nations, and is part of the worldwide computer network to store and analyze the enormous flood of data that is produced by the LHC in Geneva.
In a recent paper, our scientists collaborated with scientists from DESY, the Leiden Institute of Advanced Computer Science (LIACS), and Northeastern University to explore using a generative quantum machine learning model, called a “quantum Boltzmann machine” to untangle data from CERN’s LHC.
The goal was to learn probability distributions relevant to high energy physics better than the corresponding classical models. The data specifically contains “particle jet events”, which describe how colliders collect data about the subatomic particles generated during the experiments.
In some cases the quantum Boltzmann machine was indeed better, compared to a classical Boltzmann machine. The team is analyzed when and why this happens, understanding better how to apply these new quantum tools in this research setting. The team also studied the effect of the data encoding into a quantum state, noting that it can have a decisive effect on the training performance. Especially enticing is that the quantum Boltzmann machine is efficiently trainable, which our scientists showed in a recent paper published in Nature Communications Physics.
Find the Quantinuum team at this year’s SC24 conference from November 17th – 22nd in Atlanta, Georgia. Meet our team at Booth #4351 to discover how Quantinuum is bridging the gap between quantum computing and high-performance compute with leading industry partners.
The Quantinuum team will be participating various events, panels and poster sessions to showcase our quantum computing technologies. Join us at the below sessions:
Monday, Nov 18, 8:00 - 8:25pm, EST
Panel: KAUST booth 1031
Nash Palaniswamy, Quantinuum’s CCO, will join a panel discussion with quantum vendors and KAUST partners to discuss advancements in quantum technology.
Monday, Nov 18, 9:00 - 11:59pm, EST
Beowulf Bash: World of Coca-Cola
This year, we are proudly sponsoring the Beowulf Bash, a unique event organized to bring the HPC community together for a night of unique entertainment! Join us at the event on Monday, November 18th, 9:00pm at the World of Coca-Cola.
Wednesday, Nov 20, 3:30 – 5:00pm, EST
Panel: Educating for a Hybrid Future: Bridging the Gap between High-Performance and Quantum Computing
Vincent Anandraj, Quantinuum’s Director of Global Ecosystem and Strategic Alliances, will moderate this panel which brings together experts from leading supercomputing centers and the quantum computing industry, including PSC, Leibniz Supercomputing Centre, IQM Quantum Computers, NVIDIA, and National Research Foundation.
Thursday, Nov 21, 11:00 – 11:30am, EST
Presentation: Realizing Quantum Kernel Models at Scale with Matrix Product State Simulation
Pablo Andres-Martinez, Research Scientist at Quantinuum, will present research done in collaboration with HSBC, where the team applied quantum methods to fraud detection.