https://github.com/dpbm/bayesian-networks

Testing Quantum bayesian networks with pennnylane
https://github.com/dpbm/bayesian-networks

bayesian bayesian-networks pennylane quantum quantum-computing quantum-machine-learning

Last synced: about 2 months ago
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Testing Quantum bayesian networks with pennnylane

• Host: GitHub
• URL: https://github.com/dpbm/bayesian-networks
• Owner: Dpbm
• Created: 2024-11-29T14:17:11.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-01T16:49:09.000Z (about 2 months ago)
• Last Synced: 2024-12-01T17:39:19.129Z (about 2 months ago)
• Topics: bayesian, bayesian-networks, pennylane, quantum, quantum-computing, quantum-machine-learning
• Language: Jupyter Notebook
• Homepage: https://dpbm.github.io/bayesian-networks/
• Size: 147 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

        
# Bayesian Networks

This project was meant to be an example to be given during my quantum machine learning [mini-course](https://github.com/Dpbm/qml-course).

However, due to the amount of time I was given, I decided to remove these example about Bayesian Networks and focus specially on Quantum Convolutional Neural Networks.

Although it was a small snippet of code, I think that it deserved some attention. So, here I am, check out what I did.

## How to Run

All this code was done using Python and PennyLane, so you may need to have [Python3.10](https://www.python.org/) installed as well as all the dependencies required for this project. I recommend you to use `mamba/conda-lock`, but feel free to use whatever you want.

```bash

#mamba setup

mamba env create -f environment.yml

#conda-lock setup

conda-lock install -n bays conda-lock.yml

#pip setup

pip install -r requirements.txt

```

With all configured, you can run:

```bash

jupyter lab bayesian-network.ipynb

```

## Explanation

Is pretty common to see out there, some explanations about Bayesian Theorem using a disease and a test that can give either the correct or the wrong answer (like false positives and negatives). For this case, it won't be different.

A similar probability case was mapped into a quantum circuit with all probabilities embedded as rotations. This way, each qubit has some probability to be in a certain classical state based on the odds pre-modeled.

This kind of network can be seen as a graph, where node is a state and every weight on the edges represents the probability to go to a certain state (much like a automaton).

| qubit | Meaning                      |

|-------|----------------------------------|

|   0   | positive(1) negative(0) |

|   1   | false psotive/negative |

|   2   | chance of having the disease |

| odds | to |

|---------------|-------|

|  0.2          | false positive |

|  0.4         | false negative |

|  0.01         | have the disease given a false positive/negative |

![graph](./assets/probabilities_graph.png)

Following this structure, it's very straightforward to create the circuit.

![circuit 1](./assets/first_example.png)

In this case, the first qubit will act as an input, telling what was the test result (positive/negative), then the probability chain will be in charge to compute the probabilities based on the previous state.

After measuring the last two qubits, we have the following distribution:

![probabilities circuit 1](./assets/probs_example_1.png)

The same can be experienced when the result value is `1`.

![circuit 2](./assets/circuit_example_2.png)

![probabilities circuit 2](./assets/probs_example_2.png)