Search-based approximate diagonalization followed by analytical inversion yields high-precision multi-qubit Clifford+T circuits with 95% fewer non-Clifford gates on real-algorithm benchmarks.
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Quokka# is a Python library that converts quantum circuit analysis tasks into #SAT problems, offering multiple encodings, approximate equivalence checking, and depth-optimal synthesis.
XGBoost models trained on ≤16-qubit data predict eigensolver hyperparameters and reduce error by 0.12% on 28-qubit systems.
citing papers explorer
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High-Precision Multi-Qubit Clifford+T Synthesis by Unitary Diagonalization
Search-based approximate diagonalization followed by analytical inversion yields high-precision multi-qubit Clifford+T circuits with 95% fewer non-Clifford gates on real-algorithm benchmarks.
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Quokka#: Quantum Computing with #SAT
Quokka# is a Python library that converts quantum circuit analysis tasks into #SAT problems, offering multiple encodings, approximate equivalence checking, and depth-optimal synthesis.
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Accelerating Quantum Eigensolver Algorithms With Machine Learning
XGBoost models trained on ≤16-qubit data predict eigensolver hyperparameters and reduce error by 0.12% on 28-qubit systems.