{ "info": { "author": "", "author_email": "", "bugtrack_url": null, "classifiers": [ "Development Status :: 2 - Pre-Alpha", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Natural Language :: English", "Programming Language :: C++", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Chemistry", "Topic :: Scientific/Engineering :: Mathematics", "Topic :: Scientific/Engineering :: Physics", "Topic :: Software Development" ], "description": "scikit-quant\n============\n\nscikit-quant is an aggregator package to improve interoperability between\nquantum computing software packages.\nOur first focus in on classical optimizers, making the state-of-the art from\nthe Applied Math community available in Python for use in quantum computing.\n\nWebsite: http://scikit-quant.org\n\n\nInstallation\n------------\n\n pip install sckit-quant\n\n\nUsage\n-----\n\nBasic example::\n\n # create a numpy array of bounds, one (low, high) for each parameter\n bounds = np.array([[-1, 1], [-1, 1]], dtype=float)\n\n # budget (number of calls, assuming 1 count per call)\n budget = 40\n\n # initial values for all parameters\n x0 = np.array([0.5, 0.5])\n\n # method can be ImFil, SnobFit, Orbit, or Bobyqa\n result, history = \\\n minimize(objective_function, x0, bounds, budget, method='imfil')", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "http://scikit-quant.org", "keywords": "quantum computing optimizers", "license": "LBNL BSD", "maintainer": "Wim Lavrijsen", "maintainer_email": "WLavrijsen@lbl.gov", "name": "scikit-quant", "package_url": "https://pypi.org/project/scikit-quant/", "platform": "", "project_url": "https://pypi.org/project/scikit-quant/", "project_urls": { "Homepage": "http://scikit-quant.org" }, "release_url": "https://pypi.org/project/scikit-quant/0.3.0/", "requires_dist": null, "requires_python": "", "summary": "Integrator for Python-based quantum computing software", "version": "0.3.0" }, "last_serial": 4900646, "releases": { "0.1.1": [ { "comment_text": "", "digests": { "md5": "43e363a07c11afcf074fd3f0ddba83d8", "sha256": "add91cdf28121b3da5240c2e7b65f7e9e15fc3f843a1a1cac14416a27920ac78" }, "downloads": -1, "filename": "scikit-quant-0.1.1.tar.gz", "has_sig": true, "md5_digest": "43e363a07c11afcf074fd3f0ddba83d8", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 2697, "upload_time": "2019-01-31T16:10:29", "url": "https://files.pythonhosted.org/packages/b9/c0/d2eb160df4b68f9610324afc0dcb242f689dba8e6630605f8ca833337694/scikit-quant-0.1.1.tar.gz" } ], "0.2.0": [ { "comment_text": "", "digests": { "md5": "581f789e32013070609a59912f3b5fce", "sha256": "5fa6f4bd4cb5b12c4878c0186fc685955c5e644cfbb7f35d7b6b5890343d501d" }, "downloads": -1, "filename": "scikit-quant-0.2.0.tar.gz", "has_sig": true, "md5_digest": "581f789e32013070609a59912f3b5fce", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 2781, "upload_time": "2019-02-24T19:33:53", "url": "https://files.pythonhosted.org/packages/e8/97/d5f2de30804a90ad21dac8a472648a5861eb4b6689fe4af9933f33e1276e/scikit-quant-0.2.0.tar.gz" } ], "0.3.0": [ { "comment_text": "", "digests": { "md5": "b145d408ab12bd425cc82235088b3d89", "sha256": "d671cafdd61328c20889a41519afe936a26341e3c72e0ad4a394ac149afc50ea" }, "downloads": -1, "filename": "scikit-quant-0.3.0.tar.gz", "has_sig": true, "md5_digest": "b145d408ab12bd425cc82235088b3d89", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 16731, "upload_time": "2019-03-05T16:38:55", "url": "https://files.pythonhosted.org/packages/c4/c8/cd3a97a1d5cf965120c8a2f4edb348950d60bb1255fa41a0e4b29933d1fe/scikit-quant-0.3.0.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "b145d408ab12bd425cc82235088b3d89", "sha256": "d671cafdd61328c20889a41519afe936a26341e3c72e0ad4a394ac149afc50ea" }, "downloads": -1, "filename": "scikit-quant-0.3.0.tar.gz", "has_sig": true, "md5_digest": "b145d408ab12bd425cc82235088b3d89", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 16731, "upload_time": "2019-03-05T16:38:55", "url": "https://files.pythonhosted.org/packages/c4/c8/cd3a97a1d5cf965120c8a2f4edb348950d60bb1255fa41a0e4b29933d1fe/scikit-quant-0.3.0.tar.gz" } ] }