IBM and Daimler: Quantum Chemistry for Battery Materials

The Problem

Designing better batteries requires understanding how lithium-ion molecules interact at the quantum mechanical level. Classical computers simulate molecular Hamiltonians by approximating the wavefunction - but the exact simulation of electron interactions scales exponentially with system size. Even for moderately large molecules, classical methods like DFT (Density Functional Theory) rely on approximations that can miss important correlation effects.

Quantum computers, by contrast, can represent quantum states natively. The Variational Quantum Eigensolver (VQE) uses a parameterized quantum circuit as a wave function ansatz and classically optimizes the parameters to minimize the energy expectation value.

VQE in Qiskit

The goal is to find the ground state energy of a molecular Hamiltonian H. VQE works as follows:

1. Map the molecular Hamiltonian to a qubit operator (Jordan-Wigner or Bravyi-Kitaev transform)
2. Prepare a parameterized trial state (ansatz) on the quantum hardware
3. Measure the energy expectation value
4. Classically optimize the parameters to minimize energy
5. Repeat until convergence

from qiskit_nature.second_q.drivers import PySCFDriver
from qiskit_nature.second_q.mappers import JordanWignerMapper
from qiskit_nature.second_q.algorithms import VQEUCCFactory
from qiskit_algorithms.optimizers import COBYLA
from qiskit_aer import AerSimulator

# Define the molecule
driver = PySCFDriver(atom='Li .0 .0 .0; H .0 .0 1.6', basis='sto3g')
problem = driver.run()

# Map to qubit operators
mapper = JordanWignerMapper()
qubit_op = mapper.map(problem.second_q_ops()[0])

# Set up VQE with UCCSD ansatz
vqe_factory = VQEUCCFactory(
    quantum_instance=AerSimulator(),
    optimizer=COBYLA(maxiter=200),
)

# Run VQE
result = vqe_factory.get_solver(problem, mapper).solve(problem)
print(f"Ground state energy: {result.total_energies[0]:.4f} Hartree")

The IBM-Daimler Collaboration

Daimler’s interest was in lithium sulfur batteries - a technology with higher theoretical energy density than lithium ion, but difficult to commercialize due to complex electrochemistry. Understanding the quantum mechanical behavior of polysulfide chains (Li2S, Li2S2, …) was the target.

IBM’s team simulated smaller proxy molecules (LiH, BeH2) to validate the methodology on current hardware, with the goal of scaling to larger molecules as hardware improves.

Key findings:

• VQE produced ground state energies matching classical coupled-cluster (CCSD) for LiH at equilibrium geometry
• Error mitigation techniques (zero-noise extrapolation) were essential on real hardware
• Noise in NISQ devices introduces systematic energy overestimation that mitigation can partially correct

Error Mitigation

Running on noisy hardware, the raw VQE results had significant errors. The team applied zero-noise extrapolation: run the same circuit at artificially increased noise levels, then extrapolate back to zero noise.

from qiskit_ibm_runtime import EstimatorV2, Options

options = Options()
options.resilience_level = 1   # zero-noise extrapolation
options.optimization_level = 3

estimator = EstimatorV2(backend=backend, options=options)
job = estimator.run([(ansatz, hamiltonian, params)])
result = job.result()
energy = result[0].data.evs

Status and Outlook

The IBM-Daimler work was explicitly pre-commercial - a research pilot demonstrating what is possible. Chemically accurate simulation of molecules relevant to battery design (with 50-100 heavy atoms) requires fault-tolerant hardware not yet available.

Current NISQ hardware can meaningfully simulate molecules up to about 10-12 qubits worth of electronic structure. The path to practical quantum chemistry advantage runs through error correction, likely 5-10 years away.

Why This Matters

Battery technology underpins electric vehicles, grid storage, and consumer electronics. A 10-20% improvement in battery energy density or cycle life would be commercially transformative. Quantum chemistry is one credible path to discovering new materials that achieve this.

Learn more: Qiskit Reference