Work

Connection between single-layer quantum approximate optimization algorithm interferometry and thermal distribution sampling

Year: 2024

Type: article

Abstract: The quantum approximate optimization algorithm (QAOA) was originally proposed to find approximate solutions to combinatorial optimization problems on quantum computers. However, the algorithm has also attracted interest for sampling purposes since it was theoretically demonstrated under reasonable complexity assumptions that one layer of the algorithm already engineers a probability distribution beyond what can be simulated by classical computers. In this regard, a recent study has also shown that, in universal Ising models, this global probability distribution resembles pure but thermal-like distributions at a temperature that depends on the internal correlations of the spin model. In this work, through an interferometric interpretation of the algorithm, we extend the theoretical derivation of the amplitudes of the eigenstates and the Boltzmann distributions generated by a single-layer QAOA. We also review the implications of this behavior from practical and fundamental perspectives.

Source: Frontiers in Quantum Science and Technology

DOI: 10.3389/frqst.2024.1321264

Authors: Pablo Díez-Valle, Diego Porras, Juan José García-Ripoll

Institutions: Consejo Superior de Investigaciones Científicas, Instituto de Física Fundamental

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,125

Funders: European Commission, Centro para el Desarrollo Tecnológico Industrial