Resource-Efficient Digitized Adiabatic Quantum Factorization

TL;DR

This paper introduces a resource-efficient digitized adiabatic quantum algorithm for integer factorization, utilizing a novel encoding in the kernel subspace to reduce circuit complexity and improve solution fidelity.

Contribution

It proposes a new QUBO-based adiabatic factorization method that outperforms standard PUBO approaches in efficiency and accuracy.

Findings

Successful factorization of 8-bit integers demonstrating improved fidelity.

Significant reduction in circuit complexity compared to PUBO methods.

Enhanced solution accuracy with the new encoding approach.

Abstract

Digitized adiabatic quantum factorization is a hybrid algorithm that exploits the advantage of digitized quantum computers to implement efficient adiabatic algorithms for factorization through gate decompositions of analog evolutions. In this paper, we harness the flexibility of digitized computers to derive a digitized adiabatic algorithm able to reduce the gate-demanding costs of implementing factorization. To this end, we propose a new approach for adiabatic factorization by encoding the solution of the problem in the kernel subspace of the problem Hamiltonian, instead of using ground-state encoding considered in the standard adiabatic factorization proposed by Peng $et$ $al$. [Phys. Rev. Lett. 101, 220405 (2008)]. Our encoding enables the design of adiabatic factorization algorithms belonging to the class of Quadratic Unconstrained Binary Optimization (QUBO) methods, instead the Polinomial Unconstrained Binary Optimization (PUBO) used by standard adiabatic factorization. We illustrate the performance of our QUBO algorithm by implementing the factorization of integers $N$ up to 8 bits. The results demonstrate a substantial improvement over the PUBO formulation, both in terms of reduced circuit complexity and increased fidelity in identifying the correct solution.