Quantum Computer Capabilities will be critical to cracking Machine Learning and other Artificial Intelligence problems and the ultimate physics and capabilities in Quantum computing will determine what would be possible in any Technological Singularity

Google believes quantum computing may help solve some of the most challenging computer science problems, particularly in machine learning. Machine learning is all about building better models of the world to make more accurate predictions. If we want to cure diseases, we need better models of how they develop. If we want to create effective environmental policies, we need better models of what’s happening to our climate. And if we want to build a more useful search engine, we need to better understand spoken questions and what’s on the web so you get the best answer.

Machine learning is highly difficult. It’s what mathematicians call an “NP-hard” problem. That’s because building a good model is really a creative act. As an analogy, consider what it takes to architect a house. You’re balancing lots of constraints -- budget, usage requirements, space limitations, etc. -- but still trying to create the most beautiful house you can.

Cracking machine learning is needed for Artificial General Intelligence (AGI). AGI is needed for a technological singularity. Quantum computing seems to be needed to crack NP-Hard problems. Quantum computing may also be unable to solve all NP-Hard problems but could be needed to help get better answers for as many NP-Hard problems as possible.

Google has been trying to apply Dwave Systems quantum annealing systems to help solve machine learning problems.

Quantum computers of the future will have the potential to give artificial intelligence a major boost, a series of studies in the journal Nature suggests

Quantum AI techniques could dramatically speed up tasks such as image recognition for comparing photos on the web or for enabling cars to drive themselves

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