Efficient quantum amplitude encoding of polynomial functions

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Detalles Bibliográficos
Publicado en:arXiv.org (Mar 14, 2024), p. n/a
Autor principal: Gonzalez-Conde, Javier
Otros Autores: Watts, Thomas W, Rodriguez-Grasa, Pablo, Sanz, Mikel
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Cornell University Library, arXiv.org
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Acceso en línea:Citation/Abstract
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022 |a 2331-8422 
024 7 |a 10.22331/q-2024-03-21-1297  |2 doi 
035 |a 2840419477 
045 0 |b d20240314 
100 1 |a Gonzalez-Conde, Javier 
245 1 |a Efficient quantum amplitude encoding of polynomial functions 
260 |b Cornell University Library, arXiv.org  |c Mar 14, 2024 
513 |a Working Paper 
520 3 |a Loading functions into quantum computers represents an essential step in several quantum algorithms, such as quantum partial differential equation solvers. Therefore, the inefficiency of this process leads to a major bottleneck for the application of these algorithms. Here, we present and compare two efficient methods for the amplitude encoding of real polynomial functions on \(n\) qubits. This case holds special relevance, as any continuous function on a closed interval can be uniformly approximated with arbitrary precision by a polynomial function. The first approach relies on the matrix product state representation. We study and benchmark the approximations of the target state when the bond dimension is assumed to be small. The second algorithm combines two subroutines. Initially we encode the linear function into the quantum registers with a shallow sequence of multi-controlled gates that loads the linear function's Hadamard-Walsh series, exploring how truncating the Hadamard-Walsh series of the linear function affects the final fidelity. Applying the inverse discrete Hadamard-Walsh transform transforms the series coefficients into an amplitude encoding of the linear function. Then, we use this construction as a building block to achieve a block encoding of the amplitudes corresponding to the linear function on \(k_0\) qubits and apply the quantum singular value transformation that implements a polynomial transformation to the block encoding of the amplitudes. This unitary together with the Amplitude Amplification algorithm will enable us to prepare the quantum state that encodes the polynomial function on \(k_0\) qubits. Finally we pad \(n-k_0\) qubits to generate an approximated encoding of the polynomial on \(n\) qubits, analyzing the error depending on \(k_0\). In this regard, our methodology proposes a method to improve the state-of-the-art complexity by introducing controllable errors. 
653 |a Quantum computing 
653 |a Algorithms 
653 |a Amplitudes 
653 |a Series expansion 
653 |a Quantum computers 
653 |a Walsh transforms 
653 |a Coding 
653 |a Polynomials 
653 |a Linear functions 
700 1 |a Watts, Thomas W 
700 1 |a Rodriguez-Grasa, Pablo 
700 1 |a Sanz, Mikel 
773 0 |t arXiv.org  |g (Mar 14, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2840419477/abstract/embedded/CH9WPLCLQHQD1J4S?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2307.10917