arXiv subjects

Marc Maußner

Publications and source records attributed to Marc Maußner.

3 recordsLinked to original sources

arXiv2026

Scaling a CUDA-Q GQE + QSCI pipeline to 40 qubits for EUV photoresist chemistry

We scale a CUDA-Q-native pipeline coupling a generative quantum eigensolver (GQE) to quantum-selected configuration interaction (QSCI) across active spaces of 14 to 44 qubits, applied to the extreme-ultraviolet (EUV) photoresist chemistry of monoalkyltin oxo-hydroxides. A GPT-2 policy emits UCCSD operator sequences; sampled bitstrings become determinants, diagonalised classically, and a cross-circuit generalised-eigenvalue refinement makes every reported GQE+QSCI energy a variational upper bound. Every rung from 14 to 40 qubits carries an exact CASCI or FCI reference, up to 166 million determinants for SnO at 32 qubits. The pipeline is chemically accurate, below 1.6 mHa, through 30 qubits on methyltin trihydroxide and through 32 on SnO, on the best seed at the top rungs. Circuit depth rather than training length is the scaling lever; the refined subspace grows near-linearly with the operator count while staying a vanishing fraction of the determinant space, 0.017% at the 32-qubit SnO rung. It also runs on the 54-qubit IQM Emerald processor, at the shallow depths its routed two-qubit gates allow, reaching +0.330 mHa for SnO at 14 qubits from a CCSD-amplitude-ordered pool prefix and +3.92 mHa for the industrial n-butyltin ligand at 22 qubits from depth-truncated trained circuits under per-circuit readout self-calibration, 81% of the active-space correlation; classical configuration recovery on those counts tightens the 22-qubit result to +0.18 to 0.21 mHa. For the methyl resist, ionisation collapses the classical UCCSD(T) Sn-C bond dissociation energy from 72.6 to 21.2 kcal/mol, the switch that flips solubility on exposure. Against that, the 40-qubit result is support-limited at 22.8 mHa, the full trained ansatz on hardware awaits better fidelities, and classical subspace expansion reaches the 32 to 40-qubit spaces with no quantum sampler, so that boundary is mapped, not beaten.

quant-ph↗

arXiv2026

Additive binding energies in asphalt on a quantum processor via quantum-selected configuration interaction (QSCI)

Quantum-centric supercomputing (in which a quantum processor samples the dominant electronic configurations and classical high-performance computing resources perform the diagonalisation) is emerging as a practical route to correlated electronic-structure calculations. We present QuantumPave, a hybrid quantum-classical workflow for computing additive binding energies in asphalt binder, a quantity central to the oxidative ageing of road infrastructure. Using a 24-atom pyridine-phenol hydrogen-bonded complex as a representative model, we couple machine-learning interatomic potentials (ORB v3) for geometry optimisation with quantum-selected configuration interaction (QSCI), also referred to as sample based quantum diagonalisation (SQD), in a (10e, 10o) active space run on the 54-qubit IQM Emerald processor. On hardware, SQD reproduces the active-space CASCI reference exactly, giving a binding energy of -3.52 kcal/mol (-0.153 eV); the device noise broadens the sampling to span the active space, so no zero-noise extrapolation is required. This active-space value captures the static correlation within the chosen orbitals and underbinds the full hydrogen bond: a counterpoise-corrected CCSD(T) benchmark gives -8.5 to -9.5 kcal/mol, while the calorimetric enthalpy of about -6.25 kcal/mol is consistent with this once zero-point, thermal, and solvent contributions are included. We show that chemically meaningful binding energies for an industrially relevant materials problem are attainable on current quantum hardware within a quantum-centric supercomputing workflow.

quant-ph↗

arXiv2024

A Quantum Computing Approach to Simulating Corrosion Inhibition

This work demonstrates a systematic implementation of hybrid quantum-classical computational methods for investigating corrosion inhibition mechanisms on aluminum surfaces. We present an integrated workflow combining density functional theory (DFT) with quantum algorithms through an active space embedding scheme, specifically applied to studying 1,2,4-Triazole and 1,2,4-Triazole-3-thiol inhibitors on Al111 surfaces. Our implementation leverages the ADAPT-VQE algorithm with benchmarking against classical DFT calculations, achieving binding energies of -0.386 eV and -1.279 eV for 1,2,4-Triazole and 1,2,4-Triazole-3-thiol, respectively. The enhanced binding energy of the thiol derivative aligns with experimental observations regarding sulfur-functionalized inhibitors' improved corrosion protection. The methodology employs the orb-d3-v2 machine learning potential for rapid geometry optimizations, followed by accurate DFT calculations using CP2K with PBE functional and Grimme's D3 dispersion corrections. Our benchmarking on smaller systems reveals that StatefulAdaptVQE implementation achieves a 5-6x computational speedup while maintaining accuracy. This work establishes a workflow for quantum-accelerated materials science studying periodic systems, demonstrating the viability of hybrid quantum-classical approaches for studying surface-adsorbate interactions in corrosion inhibition applications. In which, can be transferable to other applications such as carbon capture and battery materials studies.

cond-mat.mtrl-sci↗