Developing “Killer Apps” for Quantum Computing
Logistics, Supply Chain and Routing
By Kevin Jackson for ҹɫֱ
The world is a lot smaller than it was in the previous century – or even in the previous decade.
Customers are now accustomed to a wide variety of products that can be delivered from distributors all over the globe. While this is a great opportunity for suppliers, it also presents a challenge in the form of supply chain, logistics, routing, and optimization.
How can distribution companies continue to serve the needs of their customers in the most efficient and effective way possible? This may seem like a simple question, but it becomes a complex computational problem when trying to account for all the variables that can occur within a distribution network.
What’s more, classical computers simply cannot adequately perform this optimization calculation in real-world scenarios. Because of the number of variables, the math just runs too slow.
That said, new work in quantum computing has shown promise in applications within the optimization field. To that end, we interviewed ҹɫֱ’s and to better understand how quantum computing could to optimized logistics and supply chains.
Kohagen and Fiorentini are participating in a panel about quantum computing at this week in Las Vegas, Nevada.
Beyond classical computing
When it comes to optimization it is all about maximizing or minimizing an objective. A good example is a company that delivers goods and products but owns a limited number of trucks. To improve efficiency and minimize costs, the company needs to maximize the number of objects its trucks carry and identify the shortest routes between deliveries.
“You have all these constraints, you have your objective, and you’ve got to make decisions,” said Kohagen, an optimization researcher. “The decisions end up being things like how many goods you are going to send between your distribution centers and your stores? Each of these optimization problems, even if you consider them separately, are hard problems. The technical term is that they’re (non-deterministic polynomial)-hard because you’re dealing with discrete things. For example, I can’t send half a T-shirt to my customer. I can only operate with whole integers.”
Fiorentini expands on this: “In logistics, we cannot leave anyone behind. If we need to deliver medicine, we cannot decide ‘the villages with less than 1,000 people – we don’t supply them. There are too many, and not enough people live there’. That’s not an option in today’s world.”
Today’s computers struggle to solve these NP-hard optimization problems because of the number of ever-changing variables. Consider the much-studied Traveling Salesperson Problem, which is often used to illustrate the complexity of managing logistics, routing, and supply chains.
This is a theoretical problem where a machine is tasked with finding the shortest route between an identified list of cities that a “salesperson” must visit before returning to the point of origin. This problem is simple enough with only a few cities, but it becomes exponentially harder as more locations are added, and other factors such as multiple salespeople, weather conditions, and unforeseen events arise.
Classical computers can solve this theoretical problem for a single salesperson traveling to thousands of cities. But this scenario is not realistic, and this is where classical computers begin to struggle.
“The Traveling Salesperson Problem is not very representative of what happens in the real world,” Kohagen said. “For example, with online ordering so prevalent, a retailer has orders coming in constantly. They must determine how to efficiently retrieve those items from the warehouse, pack them into the trucks, and then transport them to the customers.”
Today, the reality of an extended supply chain or distribution network is beyond what the best classical computer can solve. Quantum computers harness unique properties of quantum physics that enable them to examine all possible answers simultaneously and then concentrate the probable output of the computation onto the best option.
“Classical is a great technology, but it doesn’t cut it here,” said Fiorentini, who develops and tests quantum algorithms for optimization. “Quantum is the best alternative to classical computing that we have.“
The quantum computing opportunity
Optimization problems have long been viewed as “killer applications” for quantum computing and research conducted by Fiorentini, Kohagen and others has begun to prove that.
Fiorentini believes it is time for decision makers to explore and invest in quantum-enabled solutions for optimization problems. “There are two decisions here for decision makers,” he said. “We either give up on the problem and say, ‘we’ll just do the best we can with a classical solution, or we start allocating a budget for really developing quantum technology.”
Quantum computing is expanding rapidly and is poised to disrupt markets such as optimization. A similar situation is the power sector, which is experiencing major disruptions due to innovations in renewable energy resources, energy storage, and regulatory reform.
Every technology has a tipping point, and all signs point to a current trend in quantum computing moving rapidly to real-world applications in optimization.
“There are a lot of algorithms being developed for optimization right now,” said Kohagen. “If you really want to advance your business with quantum methods for logistics or supply chain, this is the moment to start. Decision makers must act quickly. Those that seize the opportunity before others will have a major advantage over those who lag.”
“As quantum computers continue to scale in computational power, they’ll be able to handle increasingly complex calculations to deliver more robust and optimized supply chain solutions,” said Tony Uttley, President and COO of ҹɫֱ.
“We’re excited by the acceleration of our System Model H1 technologies, Powered by Honeywell. Measured in terms of qubit number as well as quantum volume, we’re meeting our commitment to increase performance by a factor of 10X each year,” he said. “Alongside other revolutionary advances such as real-time error correction, we look forward to supporting the commercialization of quantum applications that will change the way logistical challenges are met. In fact, within the coming few months we’ll be sharing more exciting news regarding our latest technological achievements.”
Want to learn about our work to develop quantum-enabled optimization solutions for companies? Contact our experts
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.