Delbert Murphy joins Scott Hanselman to show how quantum-inspired algorithms mimic quantum physics to solve difficult optimization problems. Quantum-Inspired optimization (QIO) takes state-of-the-art algorithmic techniques from quantum physics and makes these capabilities available in Azure on conventional hardware, and callable from a Python client. You can use QIO to solve problems with hundreds of thousands of variables, combined into millions of terms, in a few minutes, with this easy-to-consume Azure service.
0:00 – Introduction
0:40 – What problems can you solve with quantum-inspired optimization?
5:35 – A concrete example: Secret Santa
8:52 – Demo, Part I: Solving Secret Santa with QIO
17:58 – Demo, Part II: Running the code
21:12 – Quantum-inspired algorithms
24:33 – Wrap-up
🔗 Solve optimization problems by using quantum-inspired optimization – https://aka.ms/azfr/693/01
🔗 What are quantum-inspired algorithms? – https://aka.ms/azfr/693/02
🔗 Ising formulations of many NP problems (Cornell University) – https://aka.ms/azfr/693/03
🔗 A Tutorial on Formulating and Using QUBO Models (Cornell University) – https://aka.ms/azfr/693/04
🔗 Sample code: delbert/secret-santa (GitHub) – https://aka.ms/azfr/693/05
🔗 Azure Quantum optimization service samples (GitHub) – https://aka.ms/azfr/693/06
🔗 Create a free account (Azure) – https://aka.ms/azfr/693/free
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Publisher: Microsoft Azure
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