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A Comprehensive Review of Quantum Circuit Optimization: Current Trends and Future Directions
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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.
Forward citations
Cited by 2 Pith papers
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Witnessing the architecture of quantum circuits
A witness framework certifies when a unitary cannot be realized by a prescribed quantum circuit architecture, with SDP and LP relaxations and analytical Clifford bounds.
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Optimization of Hybrid Quantum-Classical Algorithms
The authors introduce metrics and seven optimization routines for Quil hybrid programs, showing improvements on magic state distillation and a modified iterative phase estimation program.
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