Quantum Algorithms, Quantum Simulation

IQM and Volkswagen speed up battery research with quantum-classical methods

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November 6, 2026 by Ivy Delaney

IQM and Volkswagen Group Innovation achieved a speedup of up to 124 times in computational time for simulating battery materials, using improvements to Quantum-Classical Auxiliary-Field Quantum Monte Carlo, the company says. The collaboration combined Volkswagen’s materials challenges with IQM’s superconducting quantum hardware, specifically the Emerald processor, to validate ground-state energy calculations. Researchers then scaled the method to a 40-qubit study of a lithium superoxide dimer rearrangement, relevant to lithium-air battery cycles, though substantial runtime challenges remain. The team asks, “Are you ready to change the future?” highlighting the potential of this hybrid quantum-classical approach.

QC-AFQMC Method Reduces Classical Post-Processing Costs

This advance moves QC-AFQMC beyond prior performance for simulating complex chemical systems, specifically targeting the limitations of classical methods when modeling strongly correlated electron interactions. The team validated these improvements using error-mitigated data from IQM’s Emerald superconducting quantum hardware, confirming results against established phaseless-AFQMC and full configuration-interaction references for stretched hydrogen chains. While substantial runtime challenges persist, the successful scaling indicates a viable path for quantum-classical approaches to tackle complex materials problems. Volkswagen Group Innovation contributed an industrially relevant materials challenge to the collaboration, providing a practical focus for the algorithm development.

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Avatar of Ivy Delaney

Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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