Toggle light / dark theme

While they wrestle with the immediate danger posed by hackers today, US government officials are preparing for another, longer-term threat: attackers who are collecting sensitive, encrypted data now in the hope that they’ll be able to unlock it at some point in the future.

The threat comes from quantum computers, which work very differently from the classical computers we use today. Instead of the traditional bits made of 1s and 0s, they use quantum bits that can represent different values at the same time. The complexity of quantum computers could make them much faster at certain tasks, allowing them to solve problems that remain practically impossible for modern machines—including breaking many of the encryption algorithms currently used to protect sensitive data such as personal, trade, and state secrets.

While quantum computers are still in their infancy, incredibly expensive and fraught with problems, officials say efforts to protect the country from this long-term danger need to begin right now.

These longstanding challenges are both related to how functionals behave when presented with a system that exhibits “fractional electron character.” By using a neural network to represent the functional and tailoring our training dataset to capture the fractional electron behaviour expected for the exact functional, we found that we could solve the problems of delocalization and spin symmetry-breaking. Our functional also showed itself to be highly accurate on broad, large-scale benchmarks, suggesting that this data-driven approach can capture aspects of the exact functional that have thus far been elusive.

For years, computer simulations have played a central role in modern engineering, making it possible to provide reliable answers to questions like “will this bridge stay up?” to “will this rocket make it into space?” As technology increasingly turns to the quantum scale to explore questions about materials, medicines, and catalysts, including those we’ve never seen or even imagined, deep learning shows promise to accurately simulate matter at this quantum mechanical level.

Researchers at Lawrence Berkeley National Laboratory’s Advanced Quantum Testbed (AQT) demonstrated that an experimental method known as randomized compiling (RC) can dramatically reduce error rates in quantum algorithms and lead to more accurate and stable quantum computations. No longer just a theoretical concept for quantum computing, the multidisciplinary team’s breakthrough experimental results are published in Physical Review X.

The experiments at AQT were performed on a four-qubit superconducting quantum processor. The researchers demonstrated that RC can suppress one of the most severe types of errors in quantum computers: coherent errors.

Akel Hashim, AQT researcher, involved in the experimental breakthrough and a graduate student at the University of California, Berkeley explained: “We can perform quantum computations in this era of noisy intermediate-scale quantum (NISQ) computing, but these are very noisy, prone to errors from many different sources, and don’t last very long due to the decoherence—that is, information loss—of our qubits.”

By Stina Andersson and Ellinor Wanzambi

Researchers have been working on quantum algorithms since physicists first proposed using principles of quantum physics to simulate nature decades. One important component in many quantum algorithms is quantum walks, which are the quantum equivalent of the classical Markov chain, i.e., a random walk without memory. Quantum walks are used in algorithms in areas such as searching, node ranking in networks, and element distinctness.

Consider the graph in Figure 1 and imagine that we randomly want to move between nodes A, B, C, and D in the graph. We can only move between nodes that are connected by an edge, and each edge has an associated probability that decides how likely we are to move to the connected node. This is a random walk. In this article, we are working only with Markov chains, also called the memory-less random walks, meaning that the probabilities are independent of the previous steps. For example, the probabilities of arriving at node A are the same no matter if we got there from node B or node D.

Quantum computers have the potential to solve important problems that are beyond reach even for the most powerful supercomputers, but they require an entirely new way of programming and creating algorithms.

Universities and major tech companies are spearheading research on how to develop these new algorithms. In a recent collaboration between University of Helsinki, Aalto University, University of Turku, and IBM Research Europe-Zurich, a team of researchers have developed a new method to speed up calculations on quantum computers. The results are published in the journal PRX Quantum of the American Physical Society.

“Unlike classical computers, which use bits to store ones and zeros, information is stored in the qubits of a quantum processor in the form of a , or a wavefunction,” says postdoctoral researcher Guillermo García-Pérez from the Department of Physics at the University of Helsinki, first author of the paper.

Recent theoretical breakthroughs have settled two long-standing questions about the viability of simulating quantum systems on future quantum computers, overcoming challenges from complexity analyses to enable more advanced algorithms. Featured in two publications, the work by a quantum team at Los Alamos National Laboratory shows that physical properties of quantum systems allow for faster simulation techniques.

“Algorithms based on this work will be needed for the first full-scale demonstration of quantum simulations on quantum computers,” said Rolando Somma, a quantum theorist at Los Alamos and coauthor on the two papers.

As the development of quantum computers increases, “use cases will grow exponentially. We’re at a turning point,” Uttley told Investor’s Business Daily.

Big Developers Of Quantum Computing

Quantum computing is on target to be one of the greatest scientific breakthroughs of the 21st Century. Businesses, governments, institutions and universities have made it a high priority, with billions of dollars invested globally.

Most physicists and philosophers now agree that time is emergent while Digital Presentism denotes: Time emerges from complex qualia computing at the level of observer experiential reality. Time emerges from experiential data, it’s an epiphenomenon of consciousness. From moment to moment, you are co-writing your own story, co-producing your own “participatory reality” — your stream of consciousness is not subject to some kind of deterministic “script.” You are entitled to degrees of freedom. If we are to create high fidelity first-person simulated realities that also may be part of intersubjectivity-based Metaverse, then D-Theory of Time gives us a clear-cut guiding principle for doing just that.

Here’s Consciousness: Evolution of the Mind (2021) documentary, Part III: CONSCIOUSNESS & TIME #consciousness #evolution #mind #time #DTheoryofTime #DigitalPresentism #CyberneticTheoryofMind


Watch the full documentary on Vimeo on demand: https://vimeo.com/ondemand/339083

*Based on recent book The Syntellect Hypothesis: Five Paradigms of the Mind’s Evolution (2020) by evolutionary cyberneticist Alex M. Vikoulov, available as eBook, paperback, hardcover, and audiobook on Amazon: https://www.amazon.com/Syntellect-Hypothesis-Paradigms-Minds…atfound-20

To us humans, to be alive is to perceive the flow of time. Our perception of time is linear – we remember the past, we live in the present and we look forward to the future.

A new study suggests that systems governed by quantum mechanics do not show an exclusively linear evolution in time: in this way, they are sometimes able to unfold into the past and into the future simultaneously.

An international group of physicists concludes in a recent research published in the journal Communications Physics that quantum systems that evolve in one direction or another in time can also be found evolving in unison along both directions. This property shown by quantum systems in certain contexts breaks with the classical temporal conception, in which it is only possible to move forward or backward in time.

The work, carried out by scientists from the universities of Bristol (United Kingdom), Vienna (Austria), the Balearic Islands (Spain) and the Institute of Quantum Optics and Quantum Information (IQOQI-Vienna), shows that the limit between the time that going back and forth can be blurred in quantum mechanics. According to a press release from the University of Bristol, the new study forces us to rethink how the flow of time manifests itself in contexts in which quantum laws play a fundamental role.