New partnership aims to improve the durability, capacity and safety of the car company’s batteries.
IonQ and Hyundai Motors announced a new partnership designed to use the power of quantum computing to build better electric car batteries. The companies expect the research to benefit Hyundai’s automotive lithium batteries by making improvements to the charge and discharge cycles of the devices as well as their durability, capacity and safety.
The team will develop new variational quantum eigensolver (VQE) algorithms to study lithium compounds and their chemical reactions. The IonQ/Hyundai project plans to run a battery chemistry model to simulate 14 electrons of dilithium oxide.
A VQE algorithm runs partly on a classical computer and partly on a quantum computer. As the IBM Q team explained: “A classical computer varies some experimental parameters that control the preparation of a quantum state, and then a quantum computer prepares that state and calculates its properties.”
The project team will have access to all quantum computers commercially available at IonQ, including the latest system which is currently in private beta. This version of the hardware recently outperformed all other devices tested in a series benchmarking tests run by industry consortium QED-C, according to Peter Chapman, president and CEO of IonQ.
“In terms of recent technological milestones, the system uses the advanced double-individual gate laser system, where each ion is being hit by two individual laser beams,” he said.
TaeWon Lim, executive vice president and head of the Fundamental Material Research Center at Hyundai Motor Group, said in a press release that the company is stepping into the quantum era and developing more effective battery power.
“This creative collaboration with IonQ is expected to provide innovation in the development of basic materials in virtual space for various parts of the future mobility,” he said.
Chapman said in a press release that quantum chemistry solutions can address climate change.
“Battery efficiency is one of the most promising emerging areas where quantum computing can make a difference,” he said.
Chapman sees many use cases for quantum computing in the auto industry as Hyundai and others shift their focus from being car manufacturers to being transport and mobility providers.
“Volkswagen, Daimler, Bosch, BMW, and more have already invested in research across a variety of application areas,” he said. “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, and more recently, they’re looking at optimizing the distribution network of charging stations.”
The research from the battery project also could be used to improve fuel cell technologies and material durability, Chapman said, as well as optimization challenges.
“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 said. “Longer-term, more complex optimization problems such as multichannel logistics and routing are on automakers’ R&D slates.”
This quantum partnership supports Hyundai’s Strategy 2025 goals, which are designed to increase sales of electric vehicles and address climate change. The plan has four focus areas: electric vehicles, urban air mobility, autonomous driving technology and hydrogen fuel cells. The Strategy 2025 goals include selling an all-electric lineup by 2040 and developing a hydrogen fuel cell ecosystem.
Hyundai started selling electric vehicles in 2008 and brings expertise in lithium batteries to the new partnership, while IonQ brings quantum skills and hardware. IonQ has worked with other partners to apply quantum computing to chemistry problems. In March 2021, IonQ described research with Dow and 1QBit to simulate complex molecules and chemical compounds. The researchers used the principle of problem decomposition to “reduce the number of qubits required to simulate the electronic structure of a large model chemical system.” This means fewer qubits–up to a factor of 10–are needed to achieve the same level of accuracy compared with solving the problem without breaking it down into smaller parts. The paper, “Optimizing Electronic Structure Simulations on a Trapped-ion Quantum Computer using Problem Decomposition,” was published in February 2021.
IonQ uses a trapped-ion design in its quantum machines. IonQ’s quantum systems are available through direct API access, Amazon Braket, Microsoft Azure and Google Cloud.