BASF: Quantum Chemistry Simulation for Catalyst Design

The Problem

Catalysts sit at the heart of industrial chemistry. They lower the activation energy of reactions, which determines how fast a reaction runs and how much energy it consumes. Finding a better catalyst usually means long cycles of synthesis and lab testing, because computing the relevant activation energies accurately from first principles is hard.

The difficulty is fundamental. Electrons in molecules interact quantum mechanically, and for strongly correlated systems such as transition metal catalysts, classical methods like density functional theory rely on approximations that can miss the chemistry that matters. Exact classical treatment scales exponentially with system size. This is the textbook class of problem where quantum computers are expected to eventually help, because a quantum processor can represent electronic states natively.

What BASF Is Actually Doing

BASF is a founding member of QUTAC, the Quantum Technology and Applications Consortium formed by major German companies in 2021 to develop industrial quantum computing applications. Within QUTAC, BASF participates in the Material Science and the Production and Logistics working groups.

According to QUTAC’s account of the work, BASF’s goal is to accelerate the development of new catalysts, particularly for polymer research, by using quantum computers to shorten the computational steps around activation energies and so reduce experimental cycles. The team is explicit that this is a hybrid approach: the quantum computer would solve only the most complicated and computationally intensive subtasks, with everything else handled classically. A classical computer provides initial estimates, the quantum computer refines them, and the loop iterates.

BASF is equally explicit about timelines. Horst Weiss, who heads knowledge innovation at BASF, notes that today’s quantum computers have only fairly limited capabilities. BASF’s own estimate is that around 100 high-quality qubits with extremely low error rates would be needed just to simulate small molecules, a bar that current hardware does not meet.

The SEEQC Partnership

In February 2023, BASF announced a partnership with SEEQC, a company building digital chip-based quantum computing systems. The research explores quantum computing for homogeneous catalysis, meaning catalysts dissolved in the reaction medium. The target reactions are industrially significant: the partners point to one of the largest homogeneously catalyzed processes in the chemical industry, which produces nearly 10 million metric tons of oxo chemicals per year.

Through this agreement, BASF joined the SEEQC-led QuPharma project, originally launched in 2021 with Merck KGaA to study quantum computing for drug discovery. BASF’s involvement extends the project’s commercial focus to simulations relevant to the chemical industry.

How Quantum Computers Simulate Molecules

The general toolkit BASF and its partners are evaluating is shared across the field.

The Variational Quantum Eigensolver (VQE) is the standard near-term approach: a quantum computer prepares trial molecular states and measures their energy, while a classical optimizer adjusts the circuit to minimize that energy. The lowest energy found approximates the molecular ground state, which governs chemical behavior.

The fault-tolerant alternative is Quantum Phase Estimation (QPE), which relies on the Quantum Fourier Transform (QFT). QPE encodes the molecular energy as the phase of a quantum state and uses the QFT to read that phase out as a bitstring. It offers guaranteed accuracy but demands error-corrected hardware well beyond anything available today, which is why industrial teams treat it as the long-term destination rather than a near-term tool.

To make this concrete, here is a simplified, textbook-style illustration of how a molecular ground state problem can be set up with VQE. This is a standard tutorial example for the hydrogen molecule, not BASF’s code or results:

from qiskit_nature.second_q.drivers import PySCFDriver
from qiskit_nature.second_q.mappers import JordanWignerMapper
from qiskit_nature.second_q.algorithms import GroundStateEigensolver
from qiskit_algorithms import VQE
from qiskit_algorithms.optimizers import SLSQP
from qiskit.primitives import Estimator
from qiskit_nature.second_q.circuit.library import HartreeFock, UCCSD

# Simplified illustration: H2 at equilibrium bond length
driver = PySCFDriver(atom="H 0 0 0; H 0 0 0.735", basis="sto3g")
problem = driver.run()

mapper = JordanWignerMapper()
ansatz = UCCSD(
    num_spatial_orbitals=problem.num_spatial_orbitals,
    num_particles=problem.num_particles,
    mapper=mapper,
    initial_state=HartreeFock(
        num_spatial_orbitals=problem.num_spatial_orbitals,
        num_particles=problem.num_particles,
        mapper=mapper,
    ),
)

vqe = VQE(estimator=Estimator(), ansatz=ansatz, optimizer=SLSQP(maxiter=500))
result = GroundStateEigensolver(mapper, vqe).solve(problem)
print(f"Ground state energy: {result.total_energies[0]:.6f} Hartree")

An industrial catalyst is vastly harder than H2, which is exactly why BASF frames its current work as preparation rather than production use.

Where This Stands

No quantum advantage for catalyst simulation has been demonstrated, by BASF or anyone else, and BASF does not claim otherwise. The documented value of the work today is positioning: building quantum chemistry workflows, understanding which subtasks are worth offloading to a quantum processor, and quantifying the gap between current hardware and the roughly 100 high-quality qubits BASF estimates it needs for even small molecules. The company that has done this groundwork will be best placed when the hardware matures.

Learn more: Qiskit Reference

Sources

• QUTAC: BASF, how quantum computing can help develop chemical catalysts
• SEEQC partners with BASF to explore applications of quantum computing in chemical reactions for industrial use