×
Well done. You've clicked the tower. This would actually achieve something if you had logged in first. Use the key for that. The name takes you home. This is where all the applicables sit. And you can't apply any changes to my site unless you are logged in.

Our policy is best summarized as "we don't care about _you_, we care about _them_", no emails, so no forgetting your password. You have no rights. It's like you don't even exist. If you publish material, I reserve the right to remove it, or use it myself.

Don't impersonate. Don't name someone involuntarily. You can lose everything if you cross the line, and no, I won't cancel your automatic payments first, so you'll have to do it the hard way. See how serious this sounds? That's how serious you're meant to take these.

×
Register


Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.
  • Your password can’t be too similar to your other personal information.
  • Your password must contain at least 8 characters.
  • Your password can’t be a commonly used password.
  • Your password can’t be entirely numeric.
Enter the same password as before, for verification.
Login

Grow A Dic
Define A Word
Make Space
Set Task
Mark Post
Apply Votestyle
Create Votes
(From: saved spaces)
Exclude Votes
Apply Dic
Exclude Dic

Harnessing Inferior Solutions For Superior Outcomes: Obtaining Robust Solutions From Quantum Algorithms

Click here to flash read.

arXiv:2404.16784v1 Announce Type: new
Abstract: In the rapidly advancing domain of quantum optimization, the confluence of quantum algorithms such as Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) with robust optimization methodologies presents a cutting-edge frontier. Although it seems natural to apply quantum algorithms when facing uncertainty, this has barely been approached.
In this paper we adapt the aforementioned quantum optimization techniques to tackle robust optimization problems. By leveraging the inherent stochasticity of quantum annealing and adjusting the parameters and evaluation functions within QAOA, we present two innovative methods for obtaining robust optimal solutions. These heuristics are applied on two use cases within the energy sector: the unit commitment problem, which is central to the scheduling of power plant operations, and the optimization of charging electric vehicles (EVs) including electricity from photovoltaic (PV) to minimize costs. These examples highlight not only the potential of quantum optimization methods to enhance decision-making in energy management but also the practical relevance of the young field of quantum computing in general. Through careful adaptation of quantum algorithms, we lay the foundation for exploring ways to achieve more reliable and efficient solutions in complex optimization scenarios that occur in the real-world.

Click here to read this post out
ID: 823528; Unique Viewers: 0
Unique Voters: 0
Total Votes: 0
Votes:
Latest Change: April 26, 2024, 7:32 a.m. Changes:
/u/anonymous
Dictionaries:
Words:
Spaces:
Views: 6
CC:
No creative common's license
Comments: