Variational quantum computing for quantum simulation: principles, implementations, and challenges

TL;DR

This paper provides a comprehensive overview of variational quantum computing for quantum simulation, discussing foundational principles, implementations, challenges, and recent advancements within the NISQ era.

Contribution

It systematically reviews the principles, applications, and challenges of variational quantum algorithms in quantum simulation, highlighting recent progress and future opportunities.

Findings

Variational quantum algorithms are promising for quantum simulation in the NISQ era.

Challenges include noise, trainability, and barren-plateau issues.

Recent advancements have extended the understanding of variational quantum computing applications.

Abstract

This work presents a comprehensive overview of variational quantum computing and their key role in advancing quantum simulation. This work explores the simulation of quantum systems and sets itself apart from approaches centered on classical data processing, by focusing on the critical role of quantum data in Variational Quantum Algorithms (VQA) and Quantum Machine Learning (QML). We systematically delineate the foundational principles of variational quantum computing, establish their motivational and challenges context within the noisy intermediate-scale quantum (NISQ) era, and critically examine their application across a range of prototypical quantum simulation problems. Operating within a hybrid quantum-classical framework, these algorithms represent a promising yet problem-dependent pathway whose practicality remains contingent on trainability and scalability under noise and barren-plateau constraints.This review serves to complement and extend existing literature by synthesizing the most recent advancements in the field and providing a focused perspective on the persistent challenges and emerging opportunities that define the current landscape of variational quantum computing for quantum simulation.