A Comprehensive Review of Quantum Circuit Optimization: Current Trends and Future Directions

TL;DR

This survey reviews recent advancements in quantum circuit optimization, covering various techniques and highlighting future research opportunities to improve quantum computation efficiency and error mitigation.

Contribution

It provides a comprehensive overview of current methods, analyzing their strengths, limitations, and potential future directions in quantum circuit optimization.

Findings

Analyzes hardware-independent and dependent optimization techniques

Identifies key challenges and limitations of current methods

Suggests promising future research directions

Abstract

Optimizing quantum circuits is critical for enhancing computational speed and mitigating errors caused by quantum noise. Effective optimization must be achieved without compromising the correctness of the computations. This survey explores re-cent advancements in quantum circuit optimization, encompassing both hardware-independent and hardware-dependent techniques. It reviews state-of-the-art approaches, including analytical algorithms, heuristic strategies, machine learning based methods, and hybrid quantum-classical frameworks. The paper highlights the strengths and limitations of each method, along with the challenges they pose. Furthermore, it identifies potential research opportunities in this evolving field, offering insights into the future directions of quantum circuit optimization.