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Implementing_QSVM_on_Real_Quantum_Device

Introduction

This project aims to implement Quantum Support Vector Machines (QSVM) on actual quantum devices and simulators using the Qiskit library in conjunction with Amazon Braket (AWS). The core objective is to evaluate and compare the performance efficiency of QSVM implementations across various quantum hardware and simulation environments. The comparison will focus on key statistical metrics including recall, precision, F1 score, and accuracy, alongside execution time.

Objectives

• Implement QSVM using Qiskit on real quantum devices and simulators via Amazon Braket.

• Evaluate and compare the performance of QSVM across different quantum environments.

• Measure key statistical metrics: recall, precision, F1 score, and accuracy.

• Analyze execution time for QSVM on both quantum hardware and simulators.

Prerequisites

• Python 3.10

• AWS account with access to Amazon Braket

• Basic knowledge of quantum computing and machine learning

• Qiskit

• QSVM

• Iris Dataset (150x4)

• Quantum Hardwares (IonQ)

• Aria-2

• Simulators

Usuage

  1. Set up your AWS credentials and configure AWS CLI.
  2. Ensure you have access to the quantum devices available in Amazon Braket.
  3. Execute the code to run QSVM on quantum hardware and simulators.
  4. Compare the output metrics and execution times from both scripts.
  5. Use the provided tools to visualize and compare the results.

Results

  1. The results of the comparison including recall, precision, F1 score, accuracy, and execution time will be stored in the output/ directory.
  2. Visualization and comparison of the results can be done using the provided Jupyter notebooks in the QSVM/ directory.