Work

Quantum-driven sampling of the almost-uniform distribution via quantum walks

Year: 2026

Type: article

Abstract: We investigate the use of discrete-time quantum walks to sample from an almost-uniform distribution, in the absence of any external source of randomness. Integers are encoded on the vertices of a cycle graph, and a quantum walker evolves for a fixed number of steps before its position is measured and recorded. The walker is then reset to the measured site, and the procedure is iterated to generate the sequence of outcomes. We show that when the number of nodes of the graph is odd, i.e. the condition of the ergodic theorem for classical random walks on finite groups are satisfied, the marginal distributions converges to the uniform distribution. Although correlations between successive outcomes are unavoidable, they can be significantly reduced by a suitable choice of the evolution time.

Source: New Journal of Physics

DOI: 10.1088/1367-2630/ae4820

Authors: Marco Radaelli, Claudia Benedetti, Stefano Olivares

Institutions: Trinity College Dublin, University of Milan

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,610

Funders: Università degli Studi di Milano, Science Foundation Ireland, Trinity College Dublin, Irish Research Council