Quantum AI Breakthrough: Theorem Shrinks Appetite for Training Data

Less data for more results. 

Quantum AI Breakthrough: Theorem Shrinks Appetite for Training Data

Los Alamos National Laboratory

August 23, 2022

A proof devised by a multi-institutional team of scientists demonstrates that quantum neural networks can train on minimal data. "The need for large datasets could have been a roadblock to quantum AI [artificial intelligence], but our work removes this roadblock," said Patrick Coles at the U.S. Department of Energy's Los Alamos National Laboratory (LANL). Coles said quantum AI training occurs in a mathematical construct called a Hilbert space, and the theorem shows that navigating this space requires only as many data points as the number of parameters in a given model. The researchers could ensure that a quantum model can be compiled in far fewer computational gates relative to the volume of data. LANL's Marco Cerezo said, "We can compile certain very large quantum operations within minutes with very few training points—something that was not previously possible." ... ' 

A Guide to Quantum Computing from LANL

Worked with LANL, seems useful looking to see how this is linked to, will post. See here:  https://dl.acm.org/doi/10.1145/3517340 

Quantum Computer Programming for Dummies  By Los Alamos National Laboratory News, June 20, 2022

Scientists at the U.S. Department of Energy's Los Alamos National Laboratory have formulated a new beginner-level guide for quantum computer programmers.

The crowdsourced guide considers 20 quantum algorithms, then walks programmers through deploying them on IBM's 5-quantum bit IBMQX4 quantum computer and other quantum systems.

The results of each deployment is detailed, with distinctions between the simulator and the hardware runs specified.

Los Alamos’ Stephan Eidenbenz said the guide was prepared to get the Los Alamos workforce ready for quantum computing to assist those with little or no quantum computing experience; it also can help more experienced staffers in implementing a quantum algorithm on an actual quantum computer.

From Los Alamos National Laboratory News

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Printing Circuits on Nanomagnets Yields New Breed of AI

Considering this, out of LANL, impressed by their work, another of our enterprise connections.  Will we able to print networks with for specific need?

Printing Circuits on Nanomagnets Yields New Breed of AI

IEEE Spectrum, Dexter Johnson, April 5, 2022

Los Alamos National Laboratory (LANL) researchers have developed an artificial spin glass made of nanomagnets that is configured to mimic a neural network, paving the way for artificial intelligence algorithms to be printed as physical hardware. The artificial spin glass is comprised of thin layers of iron-nickel alloy, and the positions and orientations of the microscopic bar magnets align with the interaction structure of an artificial neural network. LANL's Michael Saccone said their artificial spin glass, a proof-of-principle Hopfield neural network, is akin to a slide rule in which "the rules of the geometry encode simple arithmetic." The architecture of a Hopfield network and nanomagnetic system involves information flowing constantly between all nanomagnets in all directions. Saccone explained, "This takes a while for a sequential algorithm to simulate, but in a physical system there is no cost to the inherent parallelization. The universe just does its thing."

Understanding High-Mutating Viruses

Tool Helps to Better Understand High-Mutating Viruses, Including COVID-19

Los Alamos Reporter, March 8, 2022

Scientists at the U.S. Department of Energy's Los Alamos National Laboratory (LANL) have developed FEVER, or Fast Evaluation of Viral Emerging Risks, a computational tool for detecting and investigating high-mutating viruses. Researchers use FEVER to design flexible measurement assays that concurrently identify whole classes of viruses for bio-surveillance, accurately diagnose an outbreak strain, and type mutations to find variants impacting public health. "We applied FEVER to COVID-19 and showed that we can indeed perform both highly specific SARS-CoV-2 diagnostics in 100 clinical samples while performing mutation typing for spike variants all at the same time," said LANL's Jessica Kubicek-Sutherland. ... '