Local-Observable-Guided Generative Quantum Circuits for Degenerate Ground Spaces

• URL: http://arxiv.org/abs/2605.23300v1
• Date: Fri, 22 May 2026 07:17:50 GMT
• Title: Local-Observable-Guided Generative Quantum Circuits for Degenerate Ground Spaces
• Abstract summary: We develop a hybrid generative quantum circuit to search for degenerate ground spaces in quantum many-body systems.<n>We benchmark the framework on the Majumdar-Ghosh model, the Affleck-Kennedy-Lieb-Tasaki model, and the spin-1 XXZ chain.<n>The framework remains robust under shot-based estimation and can still recover the degenerate ground space with a reduced measurement budget.
• Score: 4.309386084494431
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Searching for degenerate ground spaces in quantum many-body systems is central to understanding spontaneous symmetry breaking and topological order. Although existing numerical methods can approximate individual ground states with high accuracy, recovering the full degenerate space remains a substantial challenge. Here we tackle this problem using a hybrid generative quantum circuit that combines a classical generative model with an expressive parameterized quantum circuit (PQC). The classical model learns a distribution over PQC parameters, enabling the sampling of an ensemble of ground states, while the PQC ensures compatibility with quantum hardware. To promote both low energy and state diversity, we define an energy-diversity objective composed of an energy-minimization term and cosine-similarity penalties derived from local observable correlators. These local descriptors provide a scalable, measurement-efficient means of distinguishing distinct ground states. We benchmark the framework on the Majumdar-Ghosh model, the Affleck-Kennedy-Lieb-Tasaki model, and the spin-1 XXZ chain, which realize distinct mechanisms of degeneracy. In all cases, the method produces a diverse ensemble whose linear span accurately reproduces the target ground space, in some instances, it identifies an approximately orthogonal basis within the learned ensemble. We further show that the framework remains robust under shot-based estimation and can still recover the degenerate ground space with a reduced measurement budget.

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