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

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Cited by in corpus (4)

• Quantum Computing for Discrete Optimization: A Highlight of Three Technologies
• Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search
• Controlled Gate Networks: Theory and Application to Eigenvalue Estimation
• Unleashing Optimizations in Dynamic Circuits through Branch Expansion