Introduction

Quantum computers have the potential to tackle significant scientific and commercial problems. To make them commercially viable, quantum computers need to be large-scale, fault-tolerant machines with many qubits in superposition and low physical error rates. Achieving fault tolerance also requires quantum error correction (QEC), which is both time and space intensive, leading to increased execution time for operations and the need for more physical qubits.

Given these requirements, estimating resources is crucial in developing quantum computers and solutions for commercial use. It's essential to understand how architectural design choices and QEC schemes impact your quantum computing plans.

The Azure Quantum Resource Estimator helps you analyze how different architectural parameters, like physical qubit and QEC models, affect overall physical-resource estimates. In this module, you'll learn how to use the Azure Quantum Resource Estimator to estimate the resources needed to run a real quantum algorithm.

Learning objectives

By the end of this module, you'll be able to:

• Explain what the Azure Quantum Resource Estimator is.
• Define the target parameters of the Azure Quantum Resource Estimator.
• Estimate the resources of a quantum algorithm using the Azure Quantum Resource Estimator.

Prerequisites

• The latest version of Visual Studio Code.
• The latest version of the Quantum Development Kit extension.
• A Python environment with Python and Pip installed.
• VS Code with Python and Jupyter extensions installed.