qml/__init__.py,sha256=6FYhmtlb4Kyd8C8Rmo7MLwxPVbz9gsb0mT4e5Mb6vhI,923
qml/__main__.py,sha256=OuI4Lxl0PXKL_w70420IAnGHTyVj3ZxmwhXyTNdHyuQ,11149
qml/ansatz.py,sha256=FzzZ8ryDkllxD5svnwHdlsyA3zJfDeANRVPt6Dgzt2I,2197
qml/benchmarks.py,sha256=r1RYAIROCRjcGCT7W5NT5rqBbtTR8nDWrpZobtFoi0Q,9748
qml/classical_baselines.py,sha256=kN3JqUdO9X24Yxh7z65L4i4HPWQVSIXdkKCca-bt0xM,14549
qml/classifiers.py,sha256=Ax6MBdqDo3R4NCpyCSt0ZsNpS_ilgCWRnl-Bw5BVsVw,8042
qml/data.py,sha256=W5ho2UWQT5O0m6HrwsxP1T2NtRoHuohkPoMpzyOMhIk,3566
qml/datasets.py,sha256=W8XqLJZUbOM8EVG4WlmAEH264k-LDlpZ3xPHRG2YSbc,2297
qml/embeddings.py,sha256=pY5IPNJa6Y7_Tc57YbYj-U33byKjzELgiz-2DKykgXI,3593
qml/io_utils.py,sha256=BMZXBmGMk-DQ62V27YenWRTyD3Ef08wmKJk1fj7Lo-s,2377
qml/kernel_methods.py,sha256=0cIvlLimO_ZtgoLncMVBbazekAe7shUBCXaZOHO825o,5763
qml/losses.py,sha256=k8iYsCeZJEgbYmKuEE7VQf-U3DjBOP1h1ReTKOjM0yY,382
qml/metrics.py,sha256=felJohlsZ0qZkilKCgXeMaFBlCeV51qeA6mgAq635vc,1131
qml/regression.py,sha256=TU6y9qrwf82yVQZ32SbGLlTrK4YI97_ELunnwuYScjw,5699
qml/training.py,sha256=1vYfxNjUPnjRe5Wm5ZR6jrINJEQriNF1Fk_Lj0B_Qsc,673
qml/utils.py,sha256=g9ujbtdg-AKq9vmem18NuIXZglh8606HGe-8UaN2Rus,273
qml/visualize.py,sha256=mP5z4sP0TJ23yGMZ2Luf_N1WawO-zfkWLWOhtNepDf8,6632
qml_pennylane-0.1.1.dist-info/licenses/LICENSE,sha256=8yjLrHRw7qIlfoogu8raXR4DVceIzWyMgJJ9veAAoKo,1069
qml_pennylane-0.1.1.dist-info/METADATA,sha256=9dyN94Ul75iRQCu7vRp_1FDhaSqFI9zjRw8j9H7gsz4,6918
qml_pennylane-0.1.1.dist-info/WHEEL,sha256=aeYiig01lYGDzBgS8HxWXOg3uV61G9ijOsup-k9o1sk,91
qml_pennylane-0.1.1.dist-info/top_level.txt,sha256=NPT3piXWWmFbwUfI_iKxDMnDCKNtTYRrd7PRmwR0nRI,4
qml_pennylane-0.1.1.dist-info/RECORD,,
