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.
[0:00:00]– Introduction
[0:00:40]– What problems can you solve with quantum-inspired optimization?
[0:05:35]– A concrete example: Secret Santa
[0:08:52]– Demo, Part I: Solving Secret Santa with QIO
[0:17:58]– Demo, Part II: Running the code
[0:21:12]– Quantum-inspired algorithms
[0:24:33]– Wrap-up
- Solve optimization problems by using quantum-inspired optimization
- What are quantum-inspired algorithms?
- Ising formulations of many NP problems (Cornell University)
- A Tutorial on Formulating and Using QUBO Models (Cornell University)
- Sample code: delbert/secret-santa (GitHub)
- Azure Quantum optimization service samples (GitHub)
- Create a free account (Azure)
