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# 🍒 QuantumFlow: Quantum Tensorflow Machine Learning deployment in K8s ## ⚛️🔋 QuantumFlow: Quantum Computin Tensorflow in K8s
<br> <br>
## Theoretical Introduction #### 👉 this repository contains my work deploying a quantum computing version of tensorflow in kubernets.
<br>
---
### Theoretical Introduction
<br>
* With the recent progress in the development of quantum computing, the development of new quantum ML models could have a profound impact on the world’s biggest problems, leading to breakthroughs in the areas of medicine, materials, sensing, and communications. * With the recent progress in the development of quantum computing, the development of new quantum ML models could have a profound impact on the world’s biggest problems, leading to breakthroughs in the areas of medicine, materials, sensing, and communications.
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* A key feature of TensorFlow Quantum is the ability to simultaneously train and execute many quantum circuits. This is achieved by TensorFlow’s ability to parallelize computation across a cluster of computers, and the ability to simulate relatively large quantum circuits on multi-core computers. * A key feature of TensorFlow Quantum is the ability to simultaneously train and execute many quantum circuits. This is achieved by TensorFlow’s ability to parallelize computation across a cluster of computers, and the ability to simulate relatively large quantum circuits on multi-core computers.
<br>
--- ---
### Steps:
<br> <br>
## Steps: #### Prepare a quantum dataset
### Prepare a quantum dataset
- Quantum data is loaded as tensors (a multi-dimensional array of numbers). - Quantum data is loaded as tensors (a multi-dimensional array of numbers).
- Each quantum data tensor is specified as a quantum circuit written in Cirq that generates quantum data on the fly. - Each quantum data tensor is specified as a quantum circuit written in Cirq that generates quantum data on the fly.
- The tensor is executed by TensorFlow on the quantum computer to generate a quantum dataset. - The tensor is executed by TensorFlow on the quantum computer to generate a quantum dataset.
### Evaluate a quantum neural network model #### Evaluate a quantum neural network model
- The researcher can prototype a quantum neural network using Cirq that they will later embed inside of a TensorFlow compute graph. - The researcher can prototype a quantum neural network using Cirq that they will later embed inside of a TensorFlow compute graph.
- Parameterized quantum models can be selected from several broad categories based on knowledge of the quantum data's structure. - Parameterized quantum models can be selected from several broad categories based on knowledge of the quantum data's structure.
- The goal of the model is to perform quantum processing in order to extract information hidden in a typically entangled state. - The goal of the model is to perform quantum processing in order to extract information hidden in a typically entangled state.
### Sample or Average #### Sample or Average
- Measurement of quantum states extracts classical information in the form of samples from a classical random variable. - Measurement of quantum states extracts classical information in the form of samples from a classical random variable.
- The distribution of values from this random variable generally depends on the quantum state itself and on the measured observable. - The distribution of values from this random variable generally depends on the quantum state itself and on the measured observable.
- As many variational algorithms depend on mean values of measurements, also known as expectation values, TFQ provides methods for averaging over several runs involving steps (1) and (2). - As many variational algorithms depend on mean values of measurements, also known as expectation values, TFQ provides methods for averaging over several runs involving steps (1) and (2).
### Evaluate a classical neural networks model #### Evaluate a classical neural networks model
- Once classical information has been extracted, it is in a format amenable to further classical post-processing. - Once classical information has been extracted, it is in a format amenable to further classical post-processing.
- As the extracted information may still be encoded in classical correlations between measured expectations, classical deep neural networks can be applied to distill such correlations. - As the extracted information may still be encoded in classical correlations between measured expectations, classical deep neural networks can be applied to distill such correlations.
### Evaluate Cost Function #### Evaluate Cost Function
- Given the results of classical post-processing, a cost function is evaluated. - Given the results of classical post-processing, a cost function is evaluated.
- This could be based on how accurately the model performs the classification task if the quantum data was labeled, or other criteria if the task is unsupervised. - This could be based on how accurately the model performs the classification task if the quantum data was labeled, or other criteria if the task is unsupervised.
### Evaluate Gradients & Update Parameters #### Evaluate Gradients & Update Parameters
- After evaluating the cost function, the free parameters in the pipeline should be updated in a direction expected to decrease the cost. - After evaluating the cost function, the free parameters in the pipeline should be updated in a direction expected to decrease the cost.
- This is most commonly performed via gradient descent. - This is most commonly performed via gradient descent.
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### Pre-requisites ### Pre-requisites
<br>
* [Vagrant](https://www.vagrantup.com/). * [Vagrant](https://www.vagrantup.com/).
* [Virtual Box](https://www.virtualbox.org/). * [Virtual Box](https://www.virtualbox.org/).
* [Kubeflow and MiniKF](https://www.kubeflow.org/docs/other-guides/virtual-dev/getting-started-minikf/). * [Kubeflow and MiniKF](https://www.kubeflow.org/docs/other-guides/virtual-dev/getting-started-minikf/).
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### References ### References
<br>
* [Announcing TensorFlow Quantum: An Open Source Library for Quantum Machine Learning](https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html). * [Announcing TensorFlow Quantum: An Open Source Library for Quantum Machine Learning](https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html).