Circuit Design based on Feature Similarity for Quantum Generative Modeling

• URL: http://arxiv.org/abs/2503.11983v1
• Date: Sat, 15 Mar 2025 03:45:22 GMT
• Title: Circuit Design based on Feature Similarity for Quantum Generative Modeling
• Authors: Mathis Makarski, Jumpei Kato, Yuki Sato, Naoki Yamamoto,
• Abstract summary: We propose a metric-based extension to design an extended circuit based on a similarity measured between the dataset features.<n>Our results show that the metric-based extension offers the means to introduce inductive bias while designing a circuit under limited resources.
• Score: 0.6954287924634025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum generative models may achieve an advantage on quantum devices by their inherent probabilistic nature and efficient sampling strategies. However, current approaches mostly rely on general-purpose circuits, such as the hardware efficient ansatz paired with a random initialization strategy, which are known to suffer from trainability issues such as barren plateaus. To address these issues, a tensor network pretraining framework that initializes a quantum circuit ansatz with a classically computed high-quality solution for a linear entanglement structure has been proposed in literature. In order to improve the classical solution, the quantum circuit needs to be extended, while it is still an open question how the extension affects trainability. In this work, we propose the metric-based extension heuristic to design an extended circuit based on a similarity metric measured between the dataset features. We validate this method on the bars and stripes dataset and carry out experiments on financial data. Our results underline the importance of problem-informed circuit design and show that the metric-based extension heuristic offers the means to introduce inductive bias while designing a circuit under limited resources.

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