scikit-quant is an aggregator package to improve interoperability between quantum computing software packages. Our first focus in on classical optimizers, making the state-of-the art from the Applied Math community available in Python for use in quantum computing.
Website: http://scikit-quant.org
Installation
pip install sckit-quant
Usage
Basic example:
# create a numpy array of bounds, one (low, high) for each parameter
bounds = np.array([[-1, 1], [-1, 1]], dtype=float)
# budget (number of calls, assuming 1 count per call)
budget = 40
# initial values for all parameters
x0 = np.array([0.5, 0.5])
# method can be ImFil, SnobFit, Orbit, or Bobyqa
result, history = \
minimize(objective_function, x0, bounds, budget, method='imfil')
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
scikit-quant-0.3.0.tar.gz
(16.7 kB
view details)
File details
Details for the file scikit-quant-0.3.0.tar.gz.
File metadata
- Download URL: scikit-quant-0.3.0.tar.gz
- Upload date:
- Size: 16.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/1.12.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.6.3 requests-toolbelt/0.8.0 tqdm/4.29.1 CPython/2.7.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
d671cafdd61328c20889a41519afe936a26341e3c72e0ad4a394ac149afc50ea
|
|
| MD5 |
b145d408ab12bd425cc82235088b3d89
|
|
| BLAKE2b-256 |
c4c8cd3a97a1d5cf965120c8a2f4edb348950d60bb1255fa41a0e4b29933d1fe
|
