First, we give the framework of qghnn Then, we introduce an algorithm that is based on qghnn. The algorithm takes inputs such as v, aij, hm, hc, r, n, d The qghnn algorithm dep s in qghnn, the learning rate r, qubits number n, and layers d The output comprises many assessment metrics, including the loss function, mean squared error (mse) Qghnn is presented, which updates the parameters of quantum circuits by minimizing the loss function and employing gradient descent methods, indicating exciting applications in graph analysis on nisq devices.
Wed, 15 jan 2025 (showing 39 of 39 entries ) [93] arxiv:2501.08300 [pdf, html, other] 1brown theoretical physics center, department of physics, brown university, providence, ri 02912, usa 2department of physics & astronomy, university of lethbridge, lethbridge, ab t1k 3m4, canada This work was supported in part by the nsfc under grant nos 12275080 and 12075084, and the innovative research group of hunan province under grant no Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing.
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