Variational quantum architectures. Applications for noisy intermediate-scale quantum computers
[eng] Quantum algorithms showing promising speedups with respect to their classical counterparts already exist. However, noise limits the quantum circuit depth, making the practical implementation of many such quantum algorithms impossible nowadays. In this sense, variational quantum algorithms offe...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/189500 |
| Acceso en línea: | https://hdl.handle.net/2445/189500 http://hdl.handle.net/10803/675533 |
| Access Level: | acceso abierto |
| Palabra clave: | Algorismes computacionals Ordinadors quàntics Computer algorithms Quantum computers |
| Sumario: | [eng] Quantum algorithms showing promising speedups with respect to their classical counterparts already exist. However, noise limits the quantum circuit depth, making the practical implementation of many such quantum algorithms impossible nowadays. In this sense, variational quantum algorithms offer a new approach, reducing the requisites of quantum computational resources at the expense of classical optimization. Disciplines in which variational quantum algorithms may have practical applications include simulation of quantum systems, solving large systems of linear equations, combinatorial optimization, data compression, quantum state diagonalization, among others. This thesis studies different variational quantum algorithm applications. In Chapter 1, we introduce the main building blocks of variational quantum algorithms. In Chapter 2, we benchmark the seminal variational quantum eigensolver algorithm for condensed matter systems. In Chapter 3, we explore how the task of compressing quantum information is affected by data encoding in variational quantum circuits. In Chapter 4, we propose a novel variational quantum algorithm to compute the singular values of pure bipartite states. In Chapter 5, we develop a new variational quantum algorithm to solve linear systems of equations. Finally, in Chapter 6, we implement quantum generative adversarial networks for generative modeling tasks. The conclusions of this thesis are exposed in Chapter 7. Furthermore, supplementary material can be found in the appendices. Appendix A provides an introduction to Qibo, a framework for quantum simulation. Appendix B presents some results related to the Solovay-Kitaev theorem. Extra results from Chapter 5 and Chapter 6 can be found in Appendix C and Appendix D, respectively. |
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