AI Insight
Researchers successfully trained a quantum machine learning system called a generative quantum eigensolver (GQE) using IBM's 70-qubit quantum processor to solve complex chemistry problems involving organotin compounds. The system uses transformer neural networks to generate efficient quantum circuits for calculating molecular energies, first training on simulations and then directly on quantum hardware with over 120 million quantum measurements. When applied to organotin hydroxide molecules (models for advanced computer chip manufacturing materials), the method achieved results within 4.5 milliHartree median accuracy compared to classical reference calculations, while using 34-72 times fewer quantum gate operations than existing approaches.
Why it matters
This work demonstrates that quantum computers can now participate in their own training process at scales beyond classical simulation capabilities, potentially accelerating the development of practical quantum algorithms for chemistry and materials science. The specific application to photoresist materials used in extreme-ultraviolet lithography suggests potential future impacts on semiconductor manufacturing optimization.
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⚠️ Preprint – Noch nicht peer-reviewed
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Abstract: Quantum hardware can provide feedback for training circuit generators beyond the reach of exact classical simulation. We train generative quantum eigensolver (GQE) policies, transformers conditioned on molecular descriptors, to generate the state-preparation circuits for sample-based quantum diagonalization (SQD). Training runs first on a noiseless simulator and then with the IBM Heron processor ibm_kobe inside the loop, at 46 and 70 qubits ($1.2 times 10^8$ shots for the four main training runs). A classical CCSD(T) reference energy of the training molecules enters only an admission gate and two auxiliary reward terms. During hardware training, a reward penalty prevents collapse to circuits that sample only the Hartree-Fock determinant. Policies trained on transition-metal complexes are applied without retraining to Sn(OH)$_3$-Me and its hydrates, minimal models of organotin photoresists for extreme-ultraviolet lithography, from 32 to 70 qubits. After one round of hardware sampling, followed by classical ranking and recovery of the cached counts with the molecular Hamiltonian and no further QPU calls, 27 of 28 runs at 46-70 qubits are within 15 mHa of the density-matrix renormalization group (DMRG) reference (median 4.5 mHa), and in all eight runs with an untrained control the trained policy is more accurate. At 56-70 qubits the selected circuits use 34-72 times fewer two-qubit gates than the local unitary cluster Jastrow (LUCJ) circuit transpiled for the same device. These results demonstrate that circuit generators can be trained with quantum hardware at 70 qubits.