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Quantum-inspired algorithms and the Azure Quantum optimization service
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Manage episode 301473888 series 78917
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)
98 tập
Series đã xóa ("Feed không hoạt động" status)
When? This feed was archived on March 08, 2022 18:08 (). Last successful fetch was on December 10, 2021 02:56 ()
Why? Feed không hoạt động status. Server của chúng tôi không thể lấy được feed hoạt động của podcast trong một khoảng thời gian.
What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.
Manage episode 301473888 series 78917
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)
98 tập
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