Adrián Pérez-Salinas, Mahtab Yaghubi Rad, Alice Barthe, Vedran Dunjko (Dec 02 2024).
Abstract: The conventional paradigm of quantum computing is discrete: it utilizes discrete sets of gates to realize bitstring-to-bitstring mappings, some of them arguably intractable for classical computers. In parameterized quantum approaches, widely used in quantum optimization and quantum machine learning, the input becomes continuous and the output represents real-valued functions. Various strategies exist to encode the input into a quantum circuit. While the bitstring-to-bitstring universality of quantum computers is quite well understood, basic questions remained open in the continuous case. For example, it was proven that full multivariate function universality requires either (i) a fixed encoding procedure with a number of qubits scaling as the dimension of the input or (ii) a tunable encoding procedure in single-qubit circuits. This reveals a trade-off between the complexity of the data encoding and the qubit requirements. The question of whether universality can be reached with a fixed encoding and constantly many qubits has been open for the last five years. In this paper, we answer this remaining fundamental question in the affirmative. We provide a constructive method to approximate arbitrary multivariate functions using just a single qubit and a fixed-generator parametrization, at the expense of increasing the depth. We also prove universality for a few of alternative fixed encoding strategies which may have independent interest. Our results rely on a combination of techniques from harmonic analysis and quantum signal processing.
