Machine learning provides a powerful framework for predicting ground-state properties across families of quantum many-body problems, enabling amortized inference and reducing the cost of repeated simulations. Variational quantum algorithms (VQAs) are promising candidates for implementing such learnable solvers on quantum computers, yet rigorous guarantees for convergence and generalization remain scarce. Motivated by the fact that standard quantum phase estimation (QPE) provides provable performance when given a guiding state with non-trivial overlap with the ground state, we introduce a variational quantum algorithm with guiding states aiming towards predicting ground-state properties of quantum many-body systems. We then develop a proof technique—the linearization trick—that maps the training dynamics of the algorithm to those of a kernel model. This connection yields theoretical guarantees on both convergence and generalization for the VQA under the guiding state assumption. Our analysis shows that guiding states facilitate convergence, suppress finite-size error terms, and ensure stability across system dimensions. Finally, we validate our findings with numerical experiments on 2D random Heisenberg models.
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