AI News, Quantum Computing artificial intelligence

AI Could Make Quantum Computers a Reality

Even though quantum processors based on superconducting circuits already exist in labs today, they don't compare in speed or processing power to today's typical desktop, laptop, and tablet computers.

Even if you can settle on materials, a physical architecture, and a form factor for your quantum device, you're still faced with the very real difficulties of actually measuring quantum signals so you can take advantage of the processing and storage enhancements offered by quantum computing.

Ultimately, as Siddiqi explained, the question at hand is, “How can you measure a quantum signal's phases when as soon as you start to measure it you disturb it?” Siddiqi has co-authored a recent study, “Using a Recurrent Neural Network to Reconstruct Quantum Dynamics of a Superconducting Qubit from Physical Observations” in which he and fellow researchers from UC Berkeley, the Israel Institute of Technology, and Université Paris-Saclay trained a neural network to perform the predictions necessary to measure quantum states.

According to the paper, “This method has potential to greatly simplify and enhance tasks in quantum systems such as noise characterization, parameter estimation, feedback, and optimization of quantum control.” Neural networks are a particular subset of artificial intelligence algorithms designed to mimic the biology of the human brain.

In Siddiqi's research the team used a specific type of neural network called a recurrent neural network (RNN), which is typically applied to speech-related tasks such as language translation and voice recognition.

2016 paper co-authored by researchers from the Swiss Federal Institute of Technology in Zurich and Microsoft Research, examined the use of neural networks to address the many-body problems of quantum physics (essentially helping to understand how quantum particles interact with one another, or entangle).

Should quantum computers achieve the level of performance it is theorized they can achieve, many predict artificial intelligence will undergo a similar transformation and see an exponential increase in performance and “intelligence.” Researchers at Google, for example, are already experimenting with “quantum neural networks” to model how neural networks may function and perform on quantum processors.

London Quantum Computing Meetup

The details of these challenges vary depending upon which model of quantum computation is being implemented (with two notable models being quantum annealing and gate-based quantum computation) and upon which device platform is being exploited (with superconducting devices and ion traps currently leading the field). Here

Google recently used this machine to demonstrate a 10^8 pre-factor speed-up (by comparison with quantum Monte Carlo methods on a classical computer) for a carefully designed class of optimization problems.

Paul will describe both the strengths and limitations of the D-Wave machine, and discuss his own experimental research on how future implementations of a quantum annealer could overcome these limitations. This

Evolution Continues with Quantum Biology and Artificial Intelligence

As I’m working on a product that will make heavy use of encryption, I’ve found myself trying to explain public-key cryptography to friends more than once lately.

This is what we call public key encryption: Everyone who has Anna’s public key (and it’s easy to find a copy of it, she’s been giving them away, remember?), can put documents in her box, lock it, and know that the only person who can unlock it is Anna.

Computers capable of explaining their decisions to military commanders The Defense Department’s cutting-edge research arm has promised to make the military’s largest investment to date in artificial intelligence (AI) systems for U.S. weaponry, committing to spend up to $2 billion over the next five years in what it depicted as a new effort to make such systems more trusted and accepted by military commanders.

The agency sees its primary role as pushing forward new technological solutions to military problems, and the Trump administration’s technical chieftains have strongly backed injecting artificial intelligence into more of America’s weaponry as a means of competing better with Russian and Chinese military forces.

But it is larger than AI programs have historically been funded and roughly what the United States spent on the Manhattan Project that produced nuclear weapons in the 1940’s, although that figure would be worth about $28 billion today due to inflation.

Google had been leading the Project Maven project for the department, but after an organized protest by Google employees who didn’t want to work on software that could help pick out targets for the military to kill, the company said in June it would discontinue its work after its current contract expires.

While Maven and other AI initiatives have helped Pentagon weapons systems become better at recognizing targets and doing things like flying drones more effectively, fielding computer-driven systems that take lethal action on their own hasn’t been approved to date.

The report noted that while AI systems are already technically capable of choosing targets and firing weapons, commanders have been hesitant about surrendering control to weapons platforms partly because of a lack of confidence in machine reasoning, especially on the battlefield where variables could emerge that a machine and its designers haven’t previously encountered [8].

Right now, for example, if a soldier asks an AI system like a target identification platform to explain its selection, it can only provide the confidence estimate for its decision, DARPA’s director Steven Walker told reporters after a speech announcing the new investment –

DARPA officials have been opaque about exactly how its newlyfinanced research will result in computers being able to explain key decisions to humans on the battlefield [9], amidst all the clamor and urgency of a conflict, but the officials said that being able to do so is critical to AI’s future in the military.

“We’ve had expert systems in the past, we’ve had very robust robotic systems to a degree, we know how to recognize images in giant databases of photographs, but the aggregate, including what people have called common sense from time to time, it’s still quite elusive in the field”

DARPA currently has about 25 programs focused on AI research [12], DARPA currently has about 25 programs focused on AI research, according to the agency, but plans to funnel some of the new money through its new Artificial Intelligence Exploration Program.

That program, announced in July, will give grants up to $1 million each for research into how AI systems can be taught to understand context, allowing them to more effectively operate in complex environments.

Quantum computers change the world In the ancient world, they used cubits as an important data unit, but the new data unit of the future is the qubit –

Quantum bits are the basic units of information in quantum computing, a new type of computer in which particles like electrons or photons can be utilize to process information with both sides (polarizations) acting as a positive or negative, alternatively or at the same time.

According to experts, quantum computers will be able to create breakthroughs in many of the most complicated data processing problems, leading to the development of new medicines, building molecular structures and doing analysis going far beyond the capabilities of today’s binary computers [13-15].

Announced in January 2018, the D-Wave 2000Q can solve larger problems than was previously possible, with faster performance, providing a big step toward production of applications in optimization, cybersecurity, machine learning and sampling.

Taking advantage of the physical spin of quantum elements, a quantum computer will be able to process simultaneously the same data in different ways (HYBRID), enabling it to make projections and analyses much more quickly and efficiently than now is possible.

There are significant physical issues that must be worked out, such as the fact that quantum computers can only operate at cryogenic temperatures (at 250 times colder than deep space) –

Their quantum research has progressed to the point where partner QuTech is simulating quantum algorithm workloads, and Intel is fabricating new qubit test chips on a regular basis in their leadingedge manufacturing facilities.

The MIT/Innsbruck team is not the only one to have developed cybersecurity-breaking schemes, even on these early machines, the problem is significant enough that representatives of NIST, Toshiba, Amazon, Cisco, Microsoft, Intel and some of the top academics in the cybersecurity and mathematics worlds met in Toronto for the yearly Workshop on Quantum-Safe Cryptography last year.

And given the leaps of progress that are being made on almost a daily process, a commercially viable quantum computer offering cloud services could happen even more quickly, the D-Wave 2000Q is called that because it can process 2,000 qubits.

The solution lies in the development of quantum-safe cryptography, consisting of information theoretically secure schemes, hash-based cryptography, code-based cryptography and exotic-sounding technologies like lattice-based cryptography, multivariate cryptography (like the Unbalanced Oil and Vinegar scheme), and even super singular elliptic curve isogeny cryptography.

Artificial intelligence as key to future geopolitical power Russian president Vladimir Putin had in September 2017 addressed 16 000 Russian schools with following statement: “Artificial Intelligence is the future, not only for Russia but for all humankind”, he said via live video beamed to 16 000 selected schools.

For example, existing machine learning technology could enable high degrees of automation in labor-intensive activities such as satellite imagery analysis and cyber defense.

For example, commercially available AI-enabled technology (such as long-range drone package delivery) may give weak states and nonstate actors Access to a type of long-range precision strike capability.

Former U.S. Treasury Secretary Larry Summers has predicted that advances in AI and related technologies will lead to a dramatic decline in demand for labour such that the USA may have a third of men between the ages of 25 and 54 not working by the end of this half century.

Panel: What can Quantum do for AI?

Inaugural AI Research Week, hosted by the MIT-IBM Watson AI Lab. Panel discussion on research directions at the intersection of AI and Quantum Computing.

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