A Hardware-Trained Generative Quantum Eigensolver for Sample-Based Quantum Diagonalization of Organotin Hydroxides up to 70 Qubits

This dataset accompanies the manuscript "A Hardware-Trained Generative Quantum Eigensolver for Sample-Based Quantum Diagonalization of Organotin Hydroxides up to 70 Qubits". We trained amortized generative quantum eigensolver (GQE) policies—transformer models conditioned on molecular descriptors—to design the state-preparation circuits for sample-based quantum diagonalization (SQD). Training ran first on a noiseless simulator and then on the IBM Quantum processor ibm_kobe at 46 and 70 qubits. This record contains the zero-shot evaluation of the trained policies on the eleven active spaces of the organotin Sn(OH)₃-Me family and its mono- and di-hydrate adducts (32 to 70 qubits), together with the training behavior of the policies and the data needed to verify every result of the evaluation.

Two training configurations. The policies differ only in the reward structure of the hardware training stage: a Hartree–Fock penalty (which prevents the policy from producing only the reference determinant—the main configuration of the manuscript) and a reward whose weights were tuned jointly on a noisy simulator. Each configuration consists of four policies (two conditioning schemes, each trained for 46 and 70 qubits), evaluated across eleven active spaces, yielding 44 runs per configuration.

What a run is. For each run, the policy generated ten gate sequences for an unseen molecule. The ten circuits were transpiled and measured on ibm_kobe with 100,000 shots each, without error mitigation. Each circuit was ranked by a one-shot diagonalization of its valid shots, and the best, second-best, merged, and two further selections were recovered with a 20,000-dimensional subspace across 25 iterations. Energies are compared against density-matrix renormalization group (DMRG) reference energies.

What each report contains. Each report includes interactive HTML summaries of the training metrics, zero-shot evaluation benchmarks, and individual per-run details.

Related material. Companion code repository: https://github.com/kunalroy332/sqd-gqe. The molecular active-space integrals and DMRG energies were provided by Mitsubishi Chemical Corporation.

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