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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 quantum state, 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

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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.

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To handle this, people have trained neural networks on regions where we have more complete weather data. Once trained, the system could be fed partial data and infer what the rest was likely to be. For example, the trained system can create a likely weather radar map using things like satellite cloud images and data on lightning strikes.

This is exactly the sort of thing that neural networks do well with: recognizing patterns and inferring correlations.

What drew the Rigetti team’s attention is the fact that neural networks also map well onto quantum processors. In a typical neural network, a layer of “neurons” performs operations before forwarding its results to the next layer. The network “learns” by altering the strength of the connections among units in different layers. On a quantum processor, each qubit can perform the equivalent of an operation. The qubits also share connections among themselves, and the strength of the connection can be adjusted. So, it’s possible to implement and train a neural network on a quantum processor.