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

Arxiv – Quantum algorithms for supervised and unsupervised machine learning

Machine-learning tasks frequently involve problems of manipulating and classifying large numbers of vectors in high-dimensional spaces. Classical algorithms for solving such problems typically take time polynomial in the number of vectors and the dimension of the space. Quantum computers are good at manipulating high-dimensional vectors in large tensor product spaces. This paper provides supervised and unsupervised quantum machine learning algorithms for cluster assignment and cluster finding. Quantum machine learning can take time logarithmic in both the number of vectors and their dimension, an exponential speed-up over classical algorithms.

Last year, it seemed that Dwave had already achieved some speed up for some useful problems over classical systems, but when compared against the best classical algorithms this turned out not to be the case. The speedup was only against off the shelf solver software.

Wired also had a discussion of the impact and usefulness of quantum computing to Machine Learning

Late one night, during a swanky Napa Valley conference last year, MIT physicist Seth Lloyd found himself soaking in a hot tub across from Google’s Sergey Brin and Larry Page — any aspiring technology entrepreneur’s dream scenario. Lloyd made his pitch, proposing a quantum version of Google’s search engine whereby users could make queries and receive results without Google knowing which questions were asked. The men were intrigued. But after conferring with their business manager the next day, Brin and Page informed Lloyd that his scheme went against their business plan. “They want to know everything about everybody who uses their products and services,” he joked.

It is easy to grasp why Google might be interested in a quantum computer capable of rapidly searching enormous data sets. A quantum computer, in principle, could offer enormous increases in processing power, running algorithms significantly faster than a classical (non-quantum) machine for certain problems.

Quantum computing might be able to assist big data: by searching very large, unsorted data sets.


quantum RAM (Q-RAM), and Lloyd has developed a conceptual prototype, along with an accompanying program he calls a Q-App (pronounced “quapp”) targeted to machine learning. He thinks his system could find patterns within data without actually looking at any individual records, thereby preserving the quantum superposition (and the users’ privacy). “You can effectively access all billion items in your database at the same time,” he explained, adding that “you’re not accessing any one of them, you’re accessing common features of all of them.”

For example, if there is ever a giant database storing the genome of every human being on Earth, “you could search for common patterns among different genes” using Lloyd’s quantum algorithm, with Q-RAM and a small 70-qubit quantum processor while still protecting the privacy of the population, Lloyd said.

What will be possible with the Physics of different Quantum Computing Approaches ?

The question will be how well Dwave Systems performance scales for its 1024 qubit system this year and 2048 qubit system next year. It will also be important to see how well architectural improvements such as increased connections and possibly error correction improve the Dwave systems performance. Architectural improvements are made monthly.

There are other quantum computing systems based on entirely different technology which could succeed or do better than Dwave if Dwave comes up short.

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