Spreading the Word About Quantum Computing
Senior Quantum Evangelist
Mark Jackson is a man on a mission. As Quantinuum’s senior quantum evangelist, Mark’s job is to create awareness and understanding about quantum computing and its world-changing potential. Based in New York, Mark holds a Ph.D. in theoretical physics from Columbia University with a background in mathematical modeling and computational physics. In 2017 he joined Cambridge Quantum, which combined with Honeywell Quantum Solutions to form Quantinuum in 2021. He has an academic background and remains an adjunct faculty member at Singularity University. He sat down earlier this month to talk about his unique job and the future of quantum computing.
Senior Quantum Evangelist is such a unique title. What does your job entail?
A lot of my job is speaking at conferences, doing interviews, participating in podcasts, and posting on social media. I focus on creating awareness and excitement for quantum computing, letting people know what we do at Quantinuum, and educating them about the ways this amazing technology will help solve complex problems and improve people’s lives.
Most people just don’t know much about quantum computing, or they have misunderstandings or reservations about the technology and its potential impact on society.
Half the people don’t believe quantum computers really exist yet. They think it’s some sort of science fiction idea that we’ve cooked up and, if it happens at all, it’ll be 20 years from now. They just can’t believe we have these computers today. The other half think quantum computers are just really fast computers. They believe we can take all our existing software and run it on a quantum computer, and it will be a million times faster. Neither is true, and it’s my job to educate people about what quantum computers can actually do to make the world better.
Over the past few years my role at Quantinuum has evolved a bit, and about a year ago they changed my title to “evangelist.” Technically, I’m now the “senior evangelist” because we recently added several other people to the team, which will help us do an even better job of spreading the word.
How will the use of quantum computers benefit society?
We anticipate we’re only 3–5 years away from being able to do things on a quantum computer that are truly valuable to society. That time will pass very quickly, which is why we’re encouraging companies to work with us right now to develop projects so that in a few years, when technology catches up, they’ll be in a good position to take advantage of opportunities.
The two nearest-term commercial applications for quantum computers are in chemistry and optimization, such as supply chain and logistics.
In chemistry, we have known the equations for 100 years. If you give me a molecule, I know exactly what the molecule is made of — I know how many electrons, protons and neutrons are in it, and I know the equations governing all their interactions. But, solving those equations and actually figuring out the behavior of the molecule is very difficult because, as a molecule gets bigger, there are so many interactions that tracking them quickly overwhelms a conventional computer. Quantum computers are expected to one day solve these chemical equations easier and faster.
For example, pharmaceutical companies could use this technology to design medicine. Right now, there is a lot of guesswork in developing a drug. Scientists can do a little preliminary work on a computer, but then they must synthesize a lot of trial drugs followed by testing on humans.
Developing drugs this way is expensive, time consuming, and risky. In general, it takes about 10 years and $1 billion dollars to bring a drug to market. It would be ideal if scientists could do more work on a computer up front, which will save time and money and be less risky for patients.
Additionally, quantum computing will be invaluable for the machine-learning industry. Artificial intelligence is used everywhere. Your Netflix recommendations use AI machine-learning, and while this may not be lifechanging, advanced autopilot technology on an airplane or in a driverless car will be. Quantum computers one day could have the power, speed, and capacity to take machine-learning to a whole new level.
How did you end up working for Quantinuum?
I started hearing about quantum computing in 2017 and thought it sounded amazing. This field of study didn’t even exist when I was a student.
My background is in theoretical physics. For 15 years I worked in string theory and cosmology. Several years ago, I decided to leave academia and pursue other interests. I was very fortunate to be introduced to Ilyas Khan, founder of Cambridge Quantum and now CEO of Quantinuum, and he asked me to join the team about five years ago.
I was the first American hire at Cambridge Quantum, which was then a small start-up company with only about 30 people. The organization was comprised of all scientists until I joined. I was the first person to be hired whose main objective was business development.
Why is your job as an evangelist important to Quantinuum?
We can have the most amazing technology in the world, but if no one knows about it, then it doesn’t do anyone much good. There is a lot of misunderstanding and unfamiliarity that surrounds this industry currently, which is why my job of creating awareness is so important.
I get to talk to university students and researchers and let them know we have software they can use for free to help them code better. I am very lucky to have an academic background in physics because when I speak at these universities, the professors sometimes let me take over the class for a day. I don’t think they would grant the same access to a salesperson. I love to talk about the cool things we have done and are doing with these students and share ways we can partner and collaborate both now and in the future.
We want to build our hiring pipeline with the smartest and most creative young minds available. Hiring is a top priority, and job candidates may not know there are such amazing job opportunities at Quantinuum and throughout this exciting industry.
How has the industry changed in the last five years?
When I started, there were 8–10 credible quantum computing startups, including us. We were all pretty small with just a few dozen employees at the time.
Now, it seems like there’s a new company forming, a new investment, or a technical breakthrough in hardware or software every week. There are quantum information sciences degrees and programs in college now including quantum computing and closely related sciences. It’s dizzying to keep up with everything.
Today, there are roughly 400 quantum companies, building quantum products all over the world. Companies are also increasing in size. Our company currently has 400 employees, but we’re hiring like crazy and anticipate adding 200 people in 2022.
The U.S. government also is investing. During the last administration, they had a Quantum Initiative Act (QIA) where $1.2 billion was allocated for quantum funding. Other countries also are investing. China, for example, has spent at least $30 billion in quantum technology over the last few years.
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.
In a follow-up to our recent work with Hiverge using AI to discover algorithms for quantum chemistry, we’ve teamed up with Hiverge, Amazon Web Services (AWS) and NVIDIA to explore using AI to improve algorithms for combinatorial optimization.
With the rapid rise of Large Language Models (LLMs), people started asking “what if AI agents can serve as on-demand algorithm factories?” We have been working with Hiverge, an algorithm discovery company, AWS, and NVIDIA, to explore how LLMs can accelerate quantum computing research.
Hiverge – named for Hive, an AI that can develop algorithms – aims to make quantum algorithm design more accessible to researchers by translating high-level problem descriptions in mostly natural language into executable quantum circuits. The Hive takes the researcher’s initial sketch of an algorithm, as well as special constraints the researcher enumerates, and evolves it to a new algorithm that better meets the researcher’s needs. The output is expressed in terms of a familiar programming language, like Guppy or NVIDIA CUDA-Q, making it particularly easy to implement.
The AI is called a “Hive” because it is a collective of LLM agents, all of whom are editing the same codebase. In this work, the Hive was made up of LLM powerhouses such as Gemini, ChatGPT, Claude, Llama, as well as NVIDIA Nemotron, which was accessed through AWS’ Amazon Bedrock service. Many models are included because researchers know that diversity is a strength – just like a team of human researchers working in a group, a variety of perspectives often leads to the strongest result.
Once the LLMs are assembled, the Hive calls on them to do the work writing the desired algorithm; no new training is required. The algorithms are then executed and their ‘fitness’ (how well they solve the problem) is measured. Unfit programs do not survive, while the fittest ones evolve to the next generation. This process repeats, much like the evolutionary process of nature itself.
After evolution, the fittest algorithm is selected by the researchers and tested on other instances of the problem. This is a crucial step as the researchers want to understand how well it can generalize.
In this most recent work, the joint team explored how AI can assist in the discovery of heuristic quantum optimization algorithms, a class of algorithms aimed at improving efficiency across critical workstreams. These span challenges like optimal power grid dispatch and storage placement, arranging fuel inside nuclear reactors, and molecular design and reaction pathway optimization in drug, material, and chemical discovery—where solutions could translate into maximizing operational efficiency, dramatic reduction in costs, and rapid acceleration in innovation.
In other AI approaches, such as reinforcement learning, models are trained to solve a problem, but the resulting "algorithm" is effectively ‘hidden’ within a neural network. Here, the algorithm is written in Guppy or CUDA-Q (or Python), making it human-interpretable and easier to deploy on new problem instances.
This work leveraged the NVIDIA CUDA-Q platform, running on powerful NVIDIA GPUs made accessible by AWS. It’s state-of-the art accelerated computing was crucial; the research explored highly complex problems, challenges that lie at the edge of classical computing capacity. Before running anything on Quantinuum’s quantum computer, the researchers first used NVIDIA accelerated computing to simulate the quantum algorithms and assess their fitness. Once a promising algorithm is discovered, it could then be deployed on quantum hardware, creating an exciting new approach for scaling quantum algorithm design.
More broadly, this work points to one of many ways in which classical compute, AI, and quantum computing are most powerful in symbiosis. AI can be used to improve quantum, as demonstrated here, just as quantum can be used to extend AI. Looking ahead, we envision AI evolving programs that express a combination of algorithmic primitives, much like human mathematicians, such as Peter Shor and Lov Grover, have done. After all, both humans and AI can learn from each other.
As quantum computing power grows, so does the difficulty of error correction. Meeting that demand requires tight integration with high-performance classical computing, which is why we’ve partnered with NVIDIA to push the boundaries of real-time decoding performance.
Realizing the full power of quantum computing requires more than just qubits, it requires error rates low enough to run meaningful algorithms at scale. Physical qubits are sensitive to noise, which limits their capacity to handle calculations beyond a certain scale. To move beyond these limits, physical qubits must be combined into logical qubits, with errors continuously detected and corrected in real time before they can propagate and corrupt the calculation. This approach, known as fault tolerance, is a foundational requirement for any quantum computer intended to solve problems of real-world significance.
Part of the challenge of fault tolerance is the computational complexity of correcting errors in real time. Doing so involves sending the error syndrome data to a classical co-processor, solving a complex mathematical problem on that processor, then sending the resulting correction back to the quantum processor - all fast enough that it doesn’t slow down the quantum computation. For this reason, Quantum Error Correction (QEC) is currently one of the most demanding use-cases for tight coupling between classical and quantum computing.
Given the difficulty of the task, we have partnered with NVIDIA, leaders in accelerated computing. With the help of NVIDIA’s ultra-fast GPUs (and the GPU-accelerated BP-OSD decoder developed by NVIDIA as part of NVIDIA CUDA-Q QEC library), we were able to demonstrate real-time decoding of Helios’ qubits, all in a system that can be connected directly to our quantum processors using NVIDIA NVQLink.
While real-time decoding has been demonstrated before (notably, by our own scientists in this study), previous demonstrations were limited in their scalability and complexity.
In this demonstration, we used Brings’ code, a high-rate code that is possible with our all-to-all connectivity, to encode our physical qubits into noise-resilient logical qubits. Once we had them encoded, we ran gates as well as let them idle to see if we could catch and correct errors quickly and efficiently. We submitted the circuits via both NVIDIA CUDA-Q as well as our own Guppy language, underlining our commitment to accessible, ecosystem-friendly quantum computing.
The results were excellent: we were able to perform low-latency decoding that returned results in the time we needed, even for the faster clock cycles that we expect in future generation machines.
A key part of the achievement here is that we performed something called “correlated” decoding. In correlated decoding, you offload work that would normally be performed on the QPU onto the classical decoder. This is because, in ‘standard’ decoding, as you improve your error correction capabilities, it takes more and more time on the QPU. Correlated decoding elides this cost, saving QPU time for the tasks that only the quantum computer can do.
Stay tuned for our forthcoming paper with all the details.
Every year, APS Global Physics Summit brings together scientific community members from around the world across all disciplines of physics.
Join Quantinuum at this year’s conference, taking place in our backyard, Denver, Colorado, from March 15th – 20th, where we will showcase how our quantum hardware, software, and partnerships are helping define the next era of high-performance and quantum computing.
Find our team at booth #1020 and join our sessions below to discover how we’re advancing quantum technologies and building the bridge between HPC and quantum.
Monday, March 16th
Programmable quantum matter at the frontier of classical computation
Speaker: Andrew Potter
Time: 10:12 – 10:48 am
Benchmarking a 98-qubit trapped-ion quantum computer
Speaker: Charles Baldwin
Time: 12:36 – 12:48 pm
High-Fidelity Quantum operations in the Helios Barium-Ion Processor
Speaker: Anthony Ransford
Time: 4:18 – 4:30 pm
Generative AI Model for Quantum State Preparation
Speaker: Jem Guhit
Time: 4:42 – 4:54 pm
Quantum digital simulations of holographic models using Quantinuum Systems
Speaker: Enrico Rinaldi
Time: 5:54 – 6:30 pm
Tuesday, March 17th
Software-Enabled Innovations that Drive Robust Commercial Operation on Quantinuum Helios
Speaker: Caroline Figgatt
Time: 8:00 – 8:12 am
Improving Clock Speed in the Quantinuum Helios Quantum Computer
Speaker: Adam Reed
Time: 8:12 – 8:24 am
Less Quantum, More Advantage: An End-to-End Quantum Algorithm for the Jones Polynomial
Speaker: Konstantinos Meichanetzidis
Time: 8:48 – 9:00 am
Quantum Operation Pipelining in the Quantinuum Helios Processor
Speaker: Colin Kennedy
Time: 9:00 - 9:12 am
Directly estimating the fidelity of measurement-based quantum computation
Speaker: David Stephen
Time: 9:12 - 9:24 am
Logical algorithms in a quantum error-detecting code on a trapped-ion quantum processor
Speaker: Matthew DeCross
Time: 9:36 - 9:48 am
Separate and efficient characterization of SPAM errors in the presence of leakage
Speaker: Leigh Norris
Time: 10:00 - 10:12 am
Logical benchmarking on a trapped-ion quantum processor
Speaker: Andrew Guo
Time: 12:00 - 12:12 pm
Modelling Actinides Chemistry with Trapped Ion Quantum Computers
Speaker: Carlo Alberto Gaggioli
Time: 3:30 - 3:42 pm
Wednesday, March 18th
Digital quantum magnetism at the frontier of classical simulation
Speaker: Michael Foss-Feig
Time: 8:36 - 9:12 am
Shorter width truncated Taylor series for Hamiltonian dynamics simulations
Speaker: Michelle Wynne Sze
Time: 9:24 - 9:36 am
Quantum-Accelerated DFT+DMFT for Correlated Subspaces in Hemoglobin
Speaker: Juan Pedersen
Time: 9:48 - 10:00 am
Simple logical quantum computation with concatenated symplectic double codes
Speaker: Noah Berthusen
Time: 12:48 - 1:00 pm
When is enough enough? Efficient estimation of quantum properties by stopping early
Speaker: Oliver Hart
Time: 12:48 - 1:00 pm
High-Level Programming of the Quantinuum Helios Processor
Speaker: John Campora
Time: 1:48 - 2:24 pm
Error detection without post-selection in adaptive quantum circuits
Speaker: Eli Chertkov
Time: 4:42 - 4:54 pm
Thursday, March 19th
Below Threshold Logical Quantum Computation at Quantinuum
Speaker: Shival Dasu
Time: 8:00 - 8:36 am
Performing optimal phase measurements with a universal quantum processor
Speaker: Ross Hutson
Time: 8:36 - 8:48 am
Benchmarking with leakage heralded measurements on the Quantinuum Helios processor
Speaker: Victor Colussi
Time: 10:00 am
High-throughput bidirectional microwave-to-optical transduction assessed with a practical quantum capacity
Speaker: Maxwell Urmey
Time: 12:00 - 12:36 pm
Fast quantum state preparation via AI-based Graph Decimation
Speaker: Matteo Puviani
Time: 5:54 - 6:06 pm
Friday, March 20th
2D Tensor Network Methods for Simulation of Spin Models on Quantum Computers
Speaker: Reza Haghshenas
Time: 8:36 - 8:48 am
High-Performance Computing Simulations for Optical Multidimensional Coherent Spectroscopy Studies of Strained Silicon-Vacancy Centers in Diamond
Speaker: Imran Bashir
Time: 10:36 - 10:48 am
High-Performance Statevector Simulation for TKET and Selene with NVIDIA cuStateVec
Speaker: Fabian Finger
Time: 12:36 - 12:48 pm
Part 1: Logic gates on High-rate Quantum LDPC codes using ion trap devices
Speaker: Elijah Durso-Sabina
Time: 12:48 - 1:00 pm
Driving Quantum Computing Forward: QEC, Hardware, and Applications with Quantinuum
Speaker: Natalie Brown
Time: 1:12 - 1:48 pm
A new QCCD computer and new applications
Speaker: Anthony Ransford
Time: 2:24 - 3:00 pm
*All times in MT