Work

Iterative quantum optimization with an adaptive problem Hamiltonian for the shortest vector problem

Year: 2022

Type: article

Abstract: Quantum optimization algorithms hold the promise of solving classically hard, discrete optimization problems in practice. The requirement of encoding such problems in a Hamiltonian realized with a finite (and currently small) number of qubits, however, poses the risk of finding only the optimum within the restricted space supported by this Hamiltonian. We describe an iterative algorithm in which a solution obtained with such a restricted problem Hamiltonian is used to define a new problem Hamiltonian that is better suited than the previous one. In numerical examples of the shortest vector problem, we show that the algorithm with a sequence of improved problem Hamiltonians converges to the desired solution.

Source: Physical Review A

DOI: 10.1103/physreva.106.022435

Authors: Yifeng Rocky Zhu, David Joseph, Cong Ling, Florian Mintert

Institutions: Imperial College London, SandboxAQ, Helmholtz-Zentrum Dresden-Rossendorf

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Engineering and Physical Sciences Research Council

Awards: G5353371381, Bridging the Gap Between Lattice Coding and Lattice Cryptography - Post-Quantum Cryptography