[https://challenges.quantum-computing.ibm.com/fall-2022]
- Completed IBM Quantum Challenge Fall 22 #challenge-fall-2022 , solving all 4 Labs:
- Lab 1: Introduction to Primitives on Qiskit Runtime
Do you know that there is a way to maximize the quantum workflow in Qiskit? To execute quantum circuits, all of them are going through the cloud environment. Especially, there is an obvious delay when you are using an algorithm which requires feedback loops between classical and quantum hardware. In this kind of situation, we can expect a substantial speed-up on primitives with Qiskit Runtime. In exercise 1 we are going to learn what Qiskit Runtime and primitives are and how to use them in a proper way. We also comprehend the exact meaning of error mitigation and simple prologues of its techniques. As this exercise is including the primitives and the error mitigation techniques, it will be a basement for solving all the following exercises. Please free to explore as much as possible along with your own spaceship!
- Lab 2: Quantum Kernel Learning with Qiskit Runtime
Throwing of machine learning into quantum computing creates interest areas of research that use the principles of quantum mechanics to enhance machine learning or vice versa. This lab will focus on Qiskit Runtime Sampler and applying the error mitigation technique, matrix-free measurement mitigation (M3) to machine learning application problems, including the quantum kernel learning. Your mission is to test whether your repairs of quantum computer with limited resources is successful.
- Lab 3: Optimization with Qiskit Primitives
Optimization is required everywhere in fields such as manufacturing, economics, engineering and more. Solving higher complexity problems combined with
potential massive amounts of data requires more efficiency and scalability of this data-driven technology. NP-hard problems including the travelling
salesman problem, the max-cut problem and the set cover problem are intractable on the classical computer to be solved. This lab, we shall utilise
the newly refactored VQE class on Estimator using Qiskit runtime to approach a model. Travelling salesman problem. For this problem, we will build
some problem specific PQC’s considering each TSP constraints in an attempt to find a better convergence.
We shall also be looking into Digital ZNE as our Error Mitigation strategy currently available on Qiskit Runtime as a service.
- Lab 4: Quantum chemistry with Qiskit Primitives
One of the most natural use cases of quantum computing is in chemistry applications; quantum computing offers the ability to simulate the behaviour of molecules without the need for large approximations that classical computers currently require. In this lab, you will learn how to apply Qiskit Primitives to construct and build routines using VQE to compute the ground state energies of molecules, total reaction energy for a given reaction and calculate the excited energy wavelength spectrum using VQD for multiple excited states. You will also be introduced to the prototype-zne module to explore building custom Digital ZNE routines for Error mitigation.
