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# System prepended metadata

title: Quantum Image Classification

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# Quantum Image Classification 
## Problem Statement
Your task is to benchmark a Quantum algorithm for image classification. Your goal will be to go through multiple datasets and study the perfomance of various quantum classification algorithms. Upload your code and a report describing your method and results on github.

## How to go about it?
1) The first thing you should think about is how will you encode an image? Usually the way an image is encoded is as a matrix where each entity represents intensity at that pixel. But doing something like this for a quantum algorithm is not so easy. Look for methods for Quantum Image Encoding. Some examples for the same can be found [here](https://learn.qiskit.org/course/ch-applications/flexible-representation-of-quantum-images-frqi). (If you are finding this hard you can use predefined encodings in module.)
2) Now you have the image loaded in the qubit, What will be the next step? Well it depends on the algorithm but for most algorithms it will be passing the qubit to a circuit implementation of the algorithm, so at this step you will have to look for some classification algorithm. One such algorithm is Quantum Support Vector Machine which you can read about [here](https://qiskit.org/documentation/stable/0.24/tutorials/machine_learning/01_qsvm_classification.html). Read about such algorithms and try to implement what you understand the best.
3) You do the measurement and voila! you have classified the image but is your algorithm any good? The quality of an algorithm can be measured using various metrics. For eg. Accuracy which is defined as- Number of Correct Predictions/ Total Predictions. 

## Tips
Pennylane and Qiskit has some great demos, you can refer to them to get inspiration. This is an open ended project and you are encouraged to explore as much as possible. 

## Bonus task
1. Compare the use of various image encodings and it's effect on perfomance of the algorithm.
2. Compare the algorithm that you have implemented with it's classical counterpart. 
