Quantum Computing Applications in Competitive StarCraft

Dr. Qubert Spins1, and Kim Song2

1 Department of Quantum Possibilities, Cranberry-Lemon University, Pittsburgh, PA, USA

2 Undergraduate Student and E-Sports Player, University of California Los Angeles 

Abstract

The high tech extreme speed and immense computational complexity of Quantum Computing has found modern applications for cybersecurity, previously impossible optimization problems, weather forecasting, and even modeling of the human genome. There could not be a more perfect application than the world of competitive StarCraft 2. No professional eSport has needed an overpriced $25M computer rig for a small advantage over another player more than this Blizzard Entertainment classic. In this paper, the specs and capabilities of a cheap and effective Noisy Intermediate-Scale Quantum (NISQ) Computing rig will be developed that will be ideal for professional StarCraft Zerg players in high stakes tournaments. At an incredibly low build price of $20M, this computer is the world’s best dollar for qubit deal for a video game class quantum computer. Additionally, new StarCraft strategies and user capabilities will be developed. With this new hardware and resulting new strategies, the United States StarCraft team just might finally stand a chance against the Koreans.   

Keywords: Quantum Computing, StarCraft 2, Computer Cooling, Quantum Entanglement

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Published by B McGraw

B McGraw has lived a long and successful professional life as a software developer and researcher. After completing his BS in spaghetti coding at the department of the dark arts at Cranberry Lemon in 2005 he wasted no time in getting a masters in debugging by print statement in 2008 and obtaining his PhD with research in screwing up repos on Github in 2014. That's when he could finally get paid. In 2018 B McGraw finally made the big step of defaulting on his student loans and began advancing his career by adding his name on other people's research papers after finding one grammatical mistake in the Peer Review process.

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