semvec/pypi-packages huggingface dataset:
picker 3.0.0
A Django sports picker app
Requires Python: >=3.11
Keywords: django, sports, football

django-picker
=============

.. image:: https://github.com/dakrauth/picker/workflows/Test/badge.svg
    :target: https://github.com/dakrauth/picker/actions

A Django-based sports picker app for various leagues (NFL, English Premier, etc).

Templates
---------

The included templates are all `Bootstrap 5 <http://getbootstrap.com/>`_ based.

Demo
----

For Linux/Mac OS X:

.. code-block:: bash

    $ git clone https://github.com/dakrauth/django-picker.git
    $ cd django-picker
    $ docker-compose up --build

Browse to: http://127.0.0.1:8008

User ``demo``, password ``demo`` has management rights. Users [``user1``, ``user2``, ..., ``user9``]
all share password ``password``.

Development
-----------

For convenience, please install `just <https://github.com/casey/just>`_.

.. code-block:: bash

    $ git clone https://github.com/dakrauth/django-picker.git
    $ cd django-picker

    # Show all available recipes
    $ just

Build and execute the demo in a Docker container:

.. code-block:: bash

    $ just docker-build
    $ just docker-run

To test:

.. code-block:: bash

    $ just test

    # or

    $ just retest

    # or, for all tox tests

    $ just tox

Meta
----

:Version: 3.0.0
:Requirements: Python >= 3.11, Django >= 5.1
:License: MIT (see ``LICENSE`` file for more information)
:Source: https://github.com/dakrauth/django-picker

Database Schema
~~~~~~~~~~~~~~~

.. image:: resources/models.png
extraferm 0.1.0
Quantum circuit simulator for particle number conserving extended matchgate circuits
Requires Python: >=3.11

# ExtraFerm

## Overview

This library can be used to estimate Born rule probabilities of measurement outcomes from particle-number conserving extended matchgate circuits:

- **Extended matchgate**: the universal gate set consisting of matchgates + controlled‑phase gates  
- **Particle-number conserving**: the Hamming weight of initial state is preserved at all points by the circuit

## Circuit Extent

Our simulator runs most efficiently when the circuit’s **extent** is close to one. The extent is defined as:

$$
\xi^* = \prod_{j=1}^k \bigl(\cos(\theta_j/4) + \sin(\theta_j/4)\bigr)^2
$$

where $\theta_j$ is the angle of the $j$-th controlled-phase gate.

## Use

Users should interact with the simulator through the [`outcome_probabilities`](python/extended_matchgate_simulator/interface.py) function in `interface.py`. 

## Example

```python
import numpy as np
from qiskit import QuantumCircuit
from qiskit.circuit.library import XXPlusYYGate

# Create a small circuit

seed=123
rng = np.random.default_rng(seed=seed)
angles = rng.uniform(0, 2*np.pi, size=12)
qc = QuantumCircuit(4)
qc.x([0, 2])
qc.append(XXPlusYYGate(theta=angles[0], beta=angles[1]), [0, 1])
qc.cp(theta=angles[3], control_qubit=1, target_qubit=2)
qc.append(XXPlusYYGate(theta=angles[4], beta=angles[5]), [1, 2])
qc.cp(theta=angles[6], control_qubit=0, target_qubit=1)
qc.p(theta=angles[7], qubit=0)
qc.append(XXPlusYYGate(theta=angles[8], beta=angles[9]), [2, 3])
qc.append(XXPlusYYGate(theta=angles[10], beta=angles[11]), [0, 1])

# Estimate a few bitstring probabilitites

from extraferm import outcome_probabilities
bitstrings = [0b0101, 0b1100, 0b0011]
probs = outcome_probabilities(circuit=qc, 
                              outcome_states=bitstrings, 
                              trajectory_count=100_000, 
                              seed=seed)

# Show results

print(probs)
qc.draw("mpl", scale=0.8, fold=-1)
```

[5.09355260e-01 1.56195696e-33 8.07714813e-02]

![Quantum Circuit](tutorials/circuit.png)


## Setup

Install this repository

```bash
git clone https://github.com/zhassman/Extended-Matchgate-Simulator.git
cd Extended-Matchgate-Simulator
pip install -e .
```