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arXiv Machine Learning By Ruchira V Bhat, Rahul Bhowmick, Avinash Singh, Krishna Kumar Sabapathy

Meta-learning of Gibbs states for many-body Hamiltonians with applications to Quantum Boltzmann Machines

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The paper introduces two meta‑learning algorithms—Meta‑Variational Quantum Thermalizer (Meta‑VQT) and Neural Network Meta‑VQT (NN‑Meta VQT)—to efficiently prepare quantum Gibbs states for parametrized many‑body Hamiltonians on NISQ devices. Meta‑VQT employs a fully quantum ansatz, while NN‑Meta VQT uses a quantum‑classical hybrid architecture, both optimizing over a finite training set to generate accurate thermal states at unseen parameter values. The methods are validated on Transverse Field Ising Models up to eight qubits, a two‑qubit Heisenberg model, a 3‑qubit Kitaev ring, and are applied to train a Quantum Boltzmann Machine, yielding significant speedups and improved accuracy over existing techniques.

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