Mastering Quantum Circuit Learning with Qiskit 0.42 and Cirq 1.1: A Comparative Study

Introduction to Quantum Circuit Learning

Last quarter, our team discovered that quantum circuit learning was a crucial aspect of our quantum computing project. We were working with Qiskit 0.42 and Cirq 1.1 to implement Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA). In this post, I'll share our experience with these two powerful libraries and provide a step-by-step guide on how to use them for quantum circuit learning.

Background on Qiskit and Cirq

Qiskit and Cirq are two popular open-source libraries for quantum computing. Qiskit, developed by IBM, provides a comprehensive framework for quantum computing, including quantum circuit synthesis, simulation, and optimization. Cirq, developed by Google, focuses on near-term quantum computing and provides a software framework for quantum circuits.

Variational Quantum Eigensolver (VQE)

VQE is a hybrid quantum-classical algorithm used to find the ground state of a quantum system. It's an essential tool for quantum chemistry and materials science applications. In our project, we used VQE to simulate the behavior of a molecule.

Implementing VQE with Qiskit

To implement VQE with Qiskit, we first defined the quantum circuit using the QuantumCircuit class. Then, we created a VQE object and passed the circuit, the Hamiltonian, and the optimizer to it.

from qiskit import QuantumCircuit, Aer, execute
from qiskit.aqua.