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Volkswagen Group and Xanadu establish quantum simulation program for battery materials

Volkswagen Group and Canadian quantum technology company Xanadu have established a multiyear research program to improve the performance of quantum algorithms for simulating battery materials. The goal is to reduce computational costs and accelerate Volkswagen’s adoption of quantum computers to develop battery materials that are safer, lighter, and more cost-effective.

Accurate and efficient simulation of battery materials is an industry-wide challenge that could benefit from the arrival of fault-tolerant quantum computers. Existing classical methods, such as density-functional theory, have been the cornerstone of computational chemistry for several decades, but despite their many successes, are reaching limitations on research areas critical for building better batteries.

Over the past year, Volkswagen and Xanadu have engaged in multi-domain research across material science, computational chemistry, battery technologies, and quantum algorithms that have set the foundation for the program’s long-term research efforts.

The joint program aims to tackle industry challenges in battery research by focusing on the development of advanced quantum algorithms for simulating battery materials that will be processed on Xanadu’s next generation fault-tolerant quantum computers. The program’s first research article (Delgado et al.) highlights the first estimation of the resources required to implement a quantum algorithm for simulating a realistic cathode material, dilithium iron silicate.


Quantum computing for battery simulations. (a) Sketches depicting three key properties of lithium-ion batteries that can be obtained from calculations of the ground-state energies of cathode materials and isolated molecules. (b) Summary of the main steps of the first-quantized quantum algorithm implemented in the Delgado paper. The ground-state energy E of a given material is obtained by running a qubitization-based quantum phase estimation (QPE) algorithm on a quantum computer. The initial state for the QPE method is obtained by calculating Hartree-Fock orbitals and using the quantum computer to prepare the corresponding antisymmetric Hartree-Fock state. (c) Examples of measurable quantities that can be derived: the cell voltage is given by the difference between the chemical potentials (μ) of the electrodes computed from the energy variation (ΔE) of the cathode material; the activation energy (Ea), which is used to predict the ionic mobility; and the temperature profile that helps to define the battery thermal stability. Delgado et al.

At Xanadu, we are pushing the frontiers of quantum computing hardware, software, and algorithms. Our goal in quantum algorithms research is to make quantum computers truly useful. Focusing on batteries is a strategic choice given the demand from industry and the prospects for quantum computing to aid in understanding the complex chemistry inside a battery cell.

—Juan Miguel Arrazola, Head of Algorithms at Xanadu

The program will also investigate additional computational problems in materials discovery where quantum computing has the strongest prospects for massive impact. The partnership with Xanadu supports Volkswagen’s larger objective of becoming a data and software-driven provider of more sustainable mobility and their ambition to be leaders in both battery development and quantum computing applications.

Earlier this year, Volkswagen AG and the Government of Canada signed a Memorandum of Understanding to promote e-mobility in the country. Both parties agreed to investigate opportunities for Canada to contribute to Volkswagen’s global and regional battery supply chains.


  • Alain Delgado, Pablo A. M. Casares, Roberto dos Reis, Modjtaba Shokrian Zini, Roberto Campos, Norge Cruz-Hernández, Arne-Christian Voigt, Angus Lowe, Soran Jahangiri, M. A. Martin-Delgado, Jonathan E. Mueller, and Juan Miguel Arrazola (2022) “Simulating key properties of lithium-ion batteries with a fault-tolerant quantum computer” Phys. Rev. A 106, 032428 doi: 10.1103/PhysRevA.106.032428– Published 26 September 2022


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