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IQM and Volkswagen Advance Quantum-Classical AFQMC to Simulate Next-Generation Battery Chemistry

European quantum hardware manufacturer IQM Quantum Computers (Nasdaq: IQMX) and Volkswagen Group Innovation have demonstrated algorithmic and experimental advances in Quantum-Classical Auxiliary-Field Quantum Monte Carlo (QC-AFQMC),…

Quantum Computing Report

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Nov 6, 2026 at 3:48 AM UTC · 3 min read

IQM and Volkswagen Advance Quantum-Classical AFQMC to Simulate Next-Generation Battery Chemistry
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European quantum hardware manufacturer IQM Quantum Computers (Nasdaq: IQMX) and Volkswagen Group Innovation have demonstrated algorithmic and experimental advances in Quantum-Classical Auxiliary-Field Quantum Monte Carlo (QC-AFQMC), reducing the classical post-processing runtime scaling of the hybrid electronic structure algorithm by an order of magnitude. Detailed in a technical study titled Quantum-Classical Auxiliary-Field Quantum Monte Carlo at the Edge of Practicability, the collaboration achieved up to a 124× reduction in classical execution time for a 50-orbital benchmark and validated the workflow on IQM’s commercial Emerald superconducting processor.

QC-AFQMC Algorithmic & Experimental Benchmarks
Computational DimensionPrevious Implementation BaselineIQM & Volkswagen Demonstration
Per-Step Classical ScalingÕ(N5.5) via standard shadow interpolationÕ(N4.5) using Aitken’s block transformation on singular Pfaffians and automatic differentiation for force bias.
Classical Time-to-Solution SpeedupYears on large clusters for ≥ 50 orbitalsUp to 124× speedup at 50 orbitals; up to 248× estimated speedup at 100 orbitals.
Experimental Hardware ValidationUnmitigated small toy modelsGround-state calculation of stretched H8 on IQM Emerald using MPS-based tensor-network error filtering.
Industrial Materials Benchmark≤ 20 qubits for active-space models40-qubit simulation of Li2O4 lithium superoxide dimer rearrangement (26 electrons in 20 orbitals).

Simulating complex chemical reactions—such as the intermediate stages of lithium-air battery cycles—presents severe challenges for classical electronic structure methods due to the exponential growth of Slater determinants needed to capture strong electron correlation. Standard classical phaseless AFQMC (ph-AFQMC) mitigates sign problems by projecting random walks against a trial wavefunction; however, the method incurs systematic bias if that trial state cannot accurately represent multi-reference character. While QC-AFQMC leverages a quantum computer to prepare high-fidelity multi-determinant trial states, calculating the overlaps between the quantum trial state and classical Monte Carlo walkers has historically created a prohibitive computational bottleneck.