Lifeboat Foundation

Hmmm; my verdict is out for now.

IBM Quantum Computing Scientist Jay Gambetta uses a tablet to interact with the IBM Quantum Experience, the world’s first quantum computing platform delivered via the IBM Cloud at IBM’s T. J. Watson Research Center in Yorktown, NY.

On Wednesday, May 4, for the first time ever, IBM is making quantum computing available via the cloud to anyone interested in hands-on access to an IBM quantum processor, making it easier for researchers and the scientific community to accelerate innovations, and help discover new applications for this technology. This is the beginning of the quantum age of computing and the latest advance from IBM towards building a universal quantum computer. A universal quantum computer, once built, will represent one of the greatest milestones in the history of information technology and has the potential to solve certain problems we couldn’t solve, and will never be able to solve, with today’s classical computers. (Jon Simon/Feature Photo Service for IBM)

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I am totally jealous right now!

Australia opened a new quantum computing lab at the University of New South Wales (UNSW).

This follows the government’s $26-million investment in the Centre of Excellence for Quantum Computation & Communication Technology (CQC2T) as part of the National Innovation and Science Agenda. The government’s investment is supported by $10 million each from Telstra and the Commonwealth Bank of Australia (CBA).

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Interesting insight on Aluminum Nitride used to create Qubits.

http:///articles/could-aluminum-nitride-be-engineered-to-pro…nteresting insight.

Newswise — Quantum computers have the potential to break common cryptography techniques, search huge datasets and simulate quantum systems in a fraction of the time it would take today’s computers. But before this can happen, engineers need to be able to harness the properties of quantum bits or qubits.

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I do love Nvidia!

During the past nine months, an Nvidia engineering team built a self-driving car with one camera, one Drive-PX embedded computer and only 72 hours of training data. Nvidia published an academic preprint of the results of the DAVE2 project entitled End to End Learning for Self-Driving Cars on arXiv.org hosted by the Cornell Research Library.

The Nvidia project called DAVE2 is named after a 10-year-old Defense Advanced Research Projects Agency (DARPA) project known as DARPA Autonomous Vehicle (DAVE). Although neural networks and autonomous vehicles seem like a just-invented-now technology, researchers such as Google’s Geoffrey Hinton, Facebook’s Yann Lecune and the University of Montreal’s Yoshua Bengio have collaboratively researched this branch of artificial intelligence for more than two decades. And the DARPA DAVE project application of neural network-based autonomous vehicles was preceded by the ALVINN project developed at Carnegie Mellon in 1989. What has changed is GPUs have made building on their research economically feasible.

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Post-quantum cryptography discussion in Tacoma WA on May 5th discussing hacking by QC hackers and leveraging Cryptography algorithms to offset the attacks; may be of interest to sit in and even join in the debates. I will try attend if I can because it would be interesting to see the arguments raised and see the responses.

The University of Washington Tacoma Institute of Technology will present a discussion about the esoteric field of post-quantum cryptography at the Northwest Cybersecurity Symposium on May 5.

“I’ve been researching post-quantum cryptography for years, finding ways to protect against a threat that doesn’t yet exist,” said Anderson Nascimento, assistant professor of computer science at the institute, in a release.

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RPI’s new material takes semiconducting transistors to new levels.

Two-dimensional phosphane, a material known as phosphorene, has potential application as a material for semiconducting transistors in ever faster and more powerful computers. But there’s a hitch. Many of the useful properties of this material, like its ability to conduct electrons, are anisotropic, meaning they vary depending on the orientation of the crystal. Now, a team including researchers at Rensselaer Polytechnic Institute (RPI) has developed a new method to quickly and accurately determine that orientation using the interactions between light and electrons within phosphorene and other atoms-thick crystals of black phosphorus. Phosphorene—a single layer of phosphorous atoms—was isolated for the first time in 2014, allowing physicists to begin exploring its properties experimentally and theoretically. Vincent Meunier, head of the Rensselaer Department of Physics, Applied Physics, and Astronomy and a leader of the team that developed the new method, published his first paper on the material—confirming the structure of phosphorene—in that same year.

“This is a really interesting material because, depending on which direction you do things, you have completely different properties,” said Meunier, a member of the Rensselaer Center for Materials, Devices, and Integrated Systems (cMDIS). “But because it’s such a new material, it’s essential that we begin to understand and predict its intrinsic properties.”

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Supercomputer facing problems?

In the world of High Performance Computing (HPC), supercomputers represent the peak of capability, with performance measured in petaFLOPs (10¹⁵ operations per second). They play a key role in climate research, drug research, oil and gas exploration, cryptanalysis, and nuclear weapons development. But after decades of steady improvement, changes are coming as old technologies start to run into fundamental problems.

When you’re talking about supercomputers, a good place to start is the TOP500 list. Published twice a year, it ranks the world’s fastest machines based on their performance on the Linpack benchmark, which solves a dense system of linear equations using double precision (64 bit) arithmetic.

Looking down the list, you soon run into some numbers that boggle the mind. The Tianhe-2 (Milky Way-2), a system deployed at the National Supercomputer Center in Guangzho, China, is the number one system as of November 2015, a position it’s held since 2013. Running Linpack, it clocks in at 33.86 × 10¹⁵ floating point operations per second (33.86 PFLOPS).

Now, I have been hearing folks are planning to experiment with block chaining on the new Nvidia DGX-1. I do know Nvidia’s CEO mentioned that DGX-1 could be used in conjunction with block chaining as an interim step to Quatum Computing to help secure information. We’ll see.

Sterling Heights, MI (PRWEB) April 24, 2016.

Rave Computer, an Elite Solution Provider in the NVIDIA Partner Network program, today announced that it has been selected to offer the new NVIDIA® DGX-1™ deep learning system, the world’s first deep learning supercomputer designed to meet the unlimited computing demands of artificial intelligence.

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When nature knows best.