Optimizing VQE workflow for a IBM quantum system

[HwajungKang]

Description

The goal of this project is for you to gain the knowledge of the full workflow of running VQE on a IBM Quantum system.

You will learn the VQE basics and several techniques that can be implemented to improve the fidelity of the outcomes when running the circuits on a near-term quantum computer by building your own VQE example to execute on an IBM Quantum system.

You will understand the purpose of the variational algorithm for a near-term quantum computer, how to construct a parametrized circuit to measure energy expectation value for a given Hamiltonian, apply Mapomatic (https://github.com/Qiskit-Partners/mapomatic) to find a good compiled circuit with the lowest noise, understand the need for a particular optimization algorithm like SPSA when running on a real quantum computer. We will also utilize measurement mitigation techniques in several versions, full, tensored and finally M3 (https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.2.040326), (https://github.ibm.com/IBM-Q-Software/mthree) and understand the reasons for these different approaches.

Deliverables

A jupyter notebook that can be a tutorial

Mentors details

• Mentor 1
  • Name: Hwajung Kang
  • GitHub ID: HwajungKang
  • What they do: Education Delivery Program Manager

Number of mentees

1

Type of mentees

• Required:
  • Basics of quantum computing and Qiskit
  • Python basics
  • Github experience
• Nice to have:
  • Experience of using IBM Quantum systems

[HuangJunye]

Closing this issue as it was not paired with any mentee. Thank you for suggesting the project idea!