Universal approximation of continuous functions with minimal quantum circuits
arXiv:2411.19152 · doi:10.1103/t49h-jmty
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, the input becomes continuous and the output represents real-valued functions. While the universality of discrete quantum computers is well understood, basic questions remained open in the continuous case. We focus on universality on multivariate functions. Current approaches require either a number of qubits scaling linearly with the dimension of the input for fixed encodings, or a tunable encoding procedure in single-qubit circuits. The question of whether universality can be reached with a fixed encoding and sub-linearly many qubits remained open for the last five years. In this paper, we answer this question in the affirmative for arbitrary multivariate functions. We provide two methods: (i) a single-qubit circuit where each coordinate of the arguments to the function to represent is input independently, and (ii) a multi-qubit approach where all coordinates are input in one step, with number of qubits scaling logarithmically with the dimension of the argument of the function of interest. We view the first result of inherent and fundamental interest, whereas the second result opens the path towards representing functions whose arguments are densely encoded in a unitary operation, possibly encoding for instance quantum processes.
5 pages, 1 page bibliography, 10 pages appendices; 3 figures, 1 table. Accepted version in PRR
References in corpus (12)
- A variational eigenvalue solver on a quantum processor
- Supervised learning with quantum enhanced feature spaces
- Quantum machine learning in feature Hilbert spaces
- Quantum Circuit Learning
- Optimal Hamiltonian Simulation by Quantum Signal Processing
- The effect of data encoding on the expressive power of variational quantum machine learning models
- Data re-uploading for a universal quantum classifier
- Generalized Euler Angle Paramterization for SU(N)
- The Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation
- One qubit as a Universal Approximant
- Multidimensional Fourier series with quantum circuits
- Approximation and Generalization Capacities of Parametrized Quantum Circuits for Functions in Sobolev Spaces