RT Conference Proceedings
T1 An Efficient QAOA via a Polynomial QPU-Needless Approach.
A1 Chicano-García, José-Francisco
A1 Dahi, Zakaria Abdelmoiz
A1 Luque-Polo, Gabriel Jesús
K1 Matemáticas computacionales
K1 Optimización combinatoria
K1 Computación cuántica
AB The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum algorithm described as ansatzes that represent both the problem and the mixer Hamiltonians. Both are parameterizable unitary transformations executed on a quantum machine/simulator and whose parameters are iteratively optimized using a classical device to optimize the problem’s expectation value. To do so, in each QAOA iteration, most of the literature uses a quantum machine/simulator to measure the QAOA outcomes. However, this poses a severe bottleneck considering that quantum machines are hardly constrained (e.g. long queuing, limited qubits, etc.), likewise, quantum simulation also induces exponentially-increasing memory usage when dealing with large problems requiring more qubits. These limitations make today’s QAOA implementation impractical since it is hard to obtain good solutions with a reasonably-acceptable time/resources. Considering these facts, this work presents a new approach with two main contributions, including (I) removing the need for accessing quantum devices or large-sized classical machines during the QAOA optimization phase, and (II) ensuring that when dealing with some 𝑘-bounded pseudo-Boolean problems, optimizing the exact problem’s expectation value can be done in polynomial time using a classical computer.
YR 2023
FD 2023
LK https://hdl.handle.net/10630/27752
UL https://hdl.handle.net/10630/27752
LA eng
NO This work is partially funded by Universidad de Málaga, Ministerio de Ciencia, Innovación y Universidades del Gobierno de España under grants PID 2020-116727RB-I00 (funded by MCIN/AEI/ 10.13039/501100011033) and PRX21/00669; and TAILOR ICT-48 Net- work (No 952215) funded by EU Horizon 2020 research and innovation programme.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 24 ene 2026