Abrupt transitions in variational quantum circuit training

نویسندگان

چکیده

Variational quantum algorithms dominate gate-based applications of modern processors. The so-called layerwise trainability conjecture appears in various works throughout the variational computing literature. asserts that a circuit can be trained piecewise, e.g., few layers sequence to minimize an objective function. Here, we prove this false. Counterexamples are found by considering functions exponentially close (in number qubits) identity matrix. In finite setting, abrupt transitions ability circuits these functions. Specifically, below critical (target-gate-dependent) threshold, training terminates and remains near for subsequently added blocks piecewise. A layer depth will abruptly train arbitrarily target, thereby minimizing These findings shed light on divide-and-conquer apply wide collection contemporary

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ژورنال

عنوان ژورنال: Physical Review A

سال: 2021

ISSN: ['1538-4446', '1050-2947', '1094-1622']

DOI: https://doi.org/10.1103/physreva.103.032607