semvec/pypi-packages huggingface dataset:
avogadr-py 1.0.1
Simple avogadr.io batch downloader python script.
Requires Python: >=3.8

# avogadr-py

Simple avogadr.io batch downloader python script

## Installation

avogadr-py is avalaible through Python Package Index ([PyPI](https://pypi.python.org/pypi)) using [pip](https://pip.pypa.io):

```console
foo@bar:~$ python3 -m pip install --upgrade avogadr_py
```

To uninstall using [pip](https://pip.pypa.io):

```console
foo@bar:~$ python3 -m pip uninstall avogadr_py
```

## Command reference

```console
foo@bar:~$ avogadr-py -h
usage: avogadr-py [-h] [-i INPUT] [-o OUTPUT_FOLDER] [-b BACKGROUND] [-f FOREGROUND] [-W WIDTH] [-H HEIGHT] [-n]

options:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        JSON formatted compound list. Defaults to 'compounds.json'.
  -o OUTPUT_FOLDER, --output-folder OUTPUT_FOLDER
                        Folder to output files to. Defaults to 'output/'.
  -b BACKGROUND, --background BACKGROUND
                        Background colour in HEX format. Defaults to '24283b'.
  -f FOREGROUND, --foreground FOREGROUND
                        Foreground colour in HEX format. Defaults to '7aa2f7'.
  -W WIDTH, --width WIDTH
                        Image width in pixels. Defaults to '1920'.
  -H HEIGHT, --height HEIGHT
                        Image height in pixels. Defaults to '1080'.
  -n, --no-include-name
                        Don't include compound names. Defaults to 'False'.
```

## Example

```console
foo@bar:~$ avogadr-py -i compounds.json -W 3840 -H 2160 -o output/2160p/blue -f 7aa2f7
```

## Development

Source code repository is available on [GitHub](https://github.com/Czechbol/avogadr-py). Feel free to contribute. [Bug reports](https://github.com/Czechbol/avogadr-py/issues) and suggestions are welcome.

## License

avogadr-py is licensed under the [MIT License](https://github.com/Czechbol/avogadr-py/blob/main/LICENSE).

## Acknowledgement

- I would like to thank [Saul Johnson](https://github.com/lambdacasserole)
  for the amazing [avogadr.io](https://avogadr.io) website that is used as basis for this script.
tnqmetro 1.1.0
Tensor-network based package for efficient quantum metrology computations.
Requires Python: >=3.6

# TNQMetro

TNQMetro is a numerical package written in Python 3 for calculations of fundamental quantum bounds on measurement precision. Thanks to the usage of the tensor-network formalism it can beat the curse of dimensionality and provides an efficient framework to calculate bounds for finite size system as well as determine the asymptotic scaling of precision in systems where quantum enhancement amounts to a constant factor improvement over the Standard Quantum Limit. It is written in a user-friendly way so that the basic functions do not require any knowledge of tensor networks.

Introduction to the package alongside simple examples can be found in the paper [Computer Physics Communications **274**, 108282 (2022)](https://doi.org/10.1016/j.cpc.2021.108282).  
Documentation to the package can be found on the [GitHub wiki](https://github.com/kchabuda/TNQMetro/wiki).  
In-depth explanation of the tensor-network based approach to calculations of fundamental quantum bounds on measurement precision can be found in the paper [Nature Communications **11**, 250 (2020)](https://doi.org/10.1038/s41467-019-13735-9).

## Dependencies

TNQMetro requires [NumPy](https://github.com/numpy/numpy) and [ncon](https://github.com/mhauru/ncon) package.

## Installation

`pip install tnqmetro`

## Example of usage

Example of optimization of QFI using TNQMetro for N=1000 qubits with OBC and in the asymptotic regime for the problem of phase estimation with uncorrelated dephasing noise.

```
import numpy as np
import scipy.linalg
import tnqmetro

N = 1000 # number of sites in tensor-network (in this example one site = one qubit)
d = 2 # dimension of local Hilbert space (dimension of physical index)
h = np.arange(d)
h = np.diag(h) # local generator ("Hamiltonian")
c1 = 1. # uncorrelated noise strength parameter
aux = np.kron(h, np.eye(d)) - np.kron(np.eye(d), h)
Y = scipy.linalg.expm(-c1 * aux @ aux / 2) # local superoperator for uncorrelated dephasing noise

F_f, F_m_f, L_MPO_f, psi_MPS_f = tnqmetro.fin(N, [], h, [Y]) # finite appraoch
F_i, F_m_i, L_MPO_i, psi_MPS_i = tnqmetro.inf([], h, [Y]) # infinite appraoch
```