Hyundai is now partnering with startup IonQ to see how quantum computers can design advanced batteries for electric vehicles, with the aim of creating the largest battery-chemistry model yet to be run on a quantum computer, the companies announced yesterday.
A quantum computer with high enough complexity—for instance, enough components known as quantum bits or “qubits”—could theoretically achieve a quantum advantage where it can find the answers to problems no classical computer could ever solve. In theory, a quantum computer with 300 qubits fully devoted to computing could perform more calculations in an instant than there are atoms in the visible universe.
The nearest-term app for quantum computers may be chemistry—for instance, simulating molecules to see which ones might prove useful drugs. “Quantum computers are naturally suited for modeling molecular behavior because both are systems governed by quantum mechanics,” says Peter Chapman, CEO and president of Maryland-based IonQ. Since quantum computers can model chemistry more accurately than classical computing, “it becomes possible to ensure that one is extracting maximum efficiency and eliminating sources of potential waste.”
Now IonQ aims to use quantum computing to analyze and simulate the structure and energy of lithium compounds for Hyundai’s batteries, including lithium oxide in lithium-air batteries. “Lithium-air batteries have a higher energy density than lithium-sulfur batteries and thus have more potential power and capability,” Chapman says.
In the new partnership, IonQ will develop new variational quantum eigensolver algorithms optimized for investigating lithium chemistry. These kinds of algorithms are often used in quantum chemistry to, for instance, model a molecule’s ground state, the one in which it has the least amount of energy. Variational quantum eigensolvers are actually hybrid algorithms, where classical computers do much of the work while quantum processors solve the part of the problem that would prove difficult for conventional machines to handle.
IonQ will join the classical and quantum computers in this work over the cloud. This includes IonQ’s latest quantum computer, “which recently outperformed all other devices tested in a series of benchmarking tests run by industry consortium QED-C and which is currently in private beta,” Chapman says.
IonQ's glass chip can hold 64 ions in four groups for a total of 32 usable qubits. WALKER STEERE/IONQ
Whereas Google, IBM, Amazon and others often use qubits based on superconducting loops, IonQ uses qubits based on electromagnetically trapped ions. Superconducting loops are compatible with conventional microchip technology, but trapped ions may offer advantages such as resistance to errors.
The partnership aims to create the most advanced battery chemistry model yet developed on quantum computers, measured by the number of qubits and quantum gates, the quantum computing version of the logic gates that conventional computers use to perform computations.
“The team plans to harness at least 12 qubits and over 100 gate operations for the project,” Chapman says. In comparison, a Daimler-IBM partnership using quantum computing to develop next-generation lithium-sulfur batteries used only four qubits, and no other commercial projects have published results yet, he notes.
Carmakers are increasingly looking into quantum computing “because it is naturally applied to a revolution taking place across the industry—the development of electric vehicles,” Chapman says. “There is enormous incentive to make a better, less expensive battery, and so it makes sense that forward-thinking companies like Hyundai are putting quantum in their tool kit.”
The companies noted this work aims to improve the cost, durability, capacity, safety, and charging behavior of lithium batteries, which are often the most expensive components of electric vehicles. “Electric vehicles are an important part of the global movement toward reducing our collective carbon footprint,” Chapman says. “It’s very meaningful to be partnering with Hyundai to advance science that will help make them more commonplace.”
Beyond chemical analysis for battery materials, quantum computing could also help explore fuel-cell technologies and material durability, Chapman notes. In addition, “quantum machine learning applications could be used to improve training time for autonomous vehicles and solve simple problems in predictive maintenance, warehousing, and more,” he says.
“Longer-term, more complex optimization problems such as multichannel logistics and routing are on automakers’ R&D slates,” Chapman adds. “For instance, Volkswagen has been exploring quantum computing in a variety of applications for several years, first looking at how best to optimize the routing of buses and vans in traffic using quantum hardware and quantum-inspired techniques. More recently, they’ve been looking at optimizing the distribution network of charging stations.”
All in all, “I hope this partnership makes clear that quantum is not some far-off concept with no present practical application,” Chapman says. “If you choose the right partner and pick the right problems, there is impactful work that can be done right now.”
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