Quantum Chebyshev Transform: Mapping, Embedding, Learning and Sampling Distributions

• URL: http://arxiv.org/abs/2306.17026v1
• Date: Thu, 29 Jun 2023 15:19:32 GMT
• Title: Quantum Chebyshev Transform: Mapping, Embedding, Learning and Sampling Distributions
• Authors: Chelsea A. Williams, Annie E. Paine, Hsin-Yu Wu, Vincent E. Elfving, Oleksandr Kyriienko
• Abstract summary: We show how to encode data into quantum states with amplitudes growing exponentially in the system size. We propose an embedding circuit for generating the orthonormal Chebyshev basis of exponential capacity. This enables automatic model differentiation, and opens a route to solving differential equations.
• Score: 18.124351208075062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop a paradigm for building quantum models in the orthonormal space of Chebyshev polynomials. We show how to encode data into quantum states with amplitudes being Chebyshev polynomials with degree growing exponentially in the system size. Similar to the quantum Fourier transform which maps computational basis space into the phase (Fourier) basis, we describe the quantum circuit for the mapping between computational and Chebyshev spaces. We propose an embedding circuit for generating the orthonormal Chebyshev basis of exponential capacity, represented by a continuously-parameterized shallow isometry. This enables automatic quantum model differentiation, and opens a route to solving stochastic differential equations. We apply the developed paradigm to generative modeling from physically- and financially-motivated distributions, and use the quantum Chebyshev transform for efficient sampling of these distributions in extended computational basis.

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