pygridsynth provides O(log(1/ε)) ancilla-free Clifford+T synthesis with a new partial-decomposition technique for n≥3 reducing T-count constants to (21/8·4^n - 9/2·2^n + 9)log₂(1/ε) + o(log(1/ε)) and a mixed-synthesis approach empirically lowering error to ε²/(2n).
Synthesis of quantum-logic circuits
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
quant-ph 4representative citing papers
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.
D-VQLS with FWHT Pauli decomposition and 1% thresholding reduces circuit evaluations by 256x for 10-qubit tridiagonal systems while achieving over 99.99% fidelity and near-ideal scaling on up to 96 GPUs.
Parametrized quantum circuit anomaly detector trained on classical hardware and tested on IBM devices for handwritten digits and simulated long-lived particle signals in HEP, but does not outperform classical deep neural networks due to noise and amplitude encoding requirements.
citing papers explorer
-
pygridsynth: A fast numerical tool for ancilla-free Clifford+T synthesis
pygridsynth provides O(log(1/ε)) ancilla-free Clifford+T synthesis with a new partial-decomposition technique for n≥3 reducing T-count constants to (21/8·4^n - 9/2·2^n + 9)log₂(1/ε) + o(log(1/ε)) and a mixed-synthesis approach empirically lowering error to ε²/(2n).
-
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.
-
Distributed Variational Quantum Linear Solver
D-VQLS with FWHT Pauli decomposition and 1% thresholding reduces circuit evaluations by 256x for 10-qubit tridiagonal systems while achieving over 99.99% fidelity and near-ideal scaling on up to 96 GPUs.
-
Long-lived Particles Anomaly Detection with Parametrized Quantum Circuits
Parametrized quantum circuit anomaly detector trained on classical hardware and tested on IBM devices for handwritten digits and simulated long-lived particle signals in HEP, but does not outperform classical deep neural networks due to noise and amplitude encoding requirements.