Machine learning optimizes quantum circuits, reducing gates by up to 95%

Researchers have developed an automated method for optimizing quantum circuits using machine learning models. By analyzing thousands of circuits from the MQT Bench suite, they trained a predictive model to select the most effective Qiskit transpiler passes. This approach significantly reduces two-qubit gates, achieving an average reduction of 19.1% to 32.4% beyond Qiskit's default configurations, and in some cases, up to 95.8% improvement. AI

IMPACT This research could lead to more efficient quantum algorithms and software development by automating complex optimization tasks.

RANK_REASON Academic paper detailing a new method for quantum circuit optimization using machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Domenik Eichhorn
• MQT Bench
• Qiskit

• paper
• infra

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