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Category: quantum physics – Page 789

Physicists at MIT and Harvard University have demonstrated a new way to manipulate quantum bits of matter. In a paper published today in the journal Nature, they report using a system of finely tuned lasers to first trap and then tweak the interactions of 51 individual atoms, or quantum bits.

The team’s results represent one of the largest arrays of quantum bits, known as qubits, that scientists have been able to individually control. In the same issue of Nature, a team from the University of Maryland reports a similarly sized system using trapped ions as quantum bits.

In the MIT-Harvard approach, the researchers generated a chain of 51 atoms and programmed them to undergo a quantum phase transition, in which every other atom in the chain was excited. The pattern resembles a state of magnetism known as an antiferromagnet, in which the spin of every other atom or molecule is aligned.

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(Phys.org)—Physicists have experimentally demonstrated quantum entanglement with 10 qubits on a superconducting circuit, surpassing the previous record of nine entangled superconducting qubits. The 10-qubit state is the largest multiqubit entangled state created in any solid-state system and represents a step toward realizing large-scale quantum computing.

Lead researcher Jian-Wei Pan and co-workers at the University of Science and Technology of China, Zhejiang University, Fuzhou University, and the Institute of Physics, China, have published a paper on their results in a recent issue of Physical Review Letters.

In general, one of the biggest challenges to scaling up multiqubit entanglement is addressing the catastrophic effects of decoherence. One strategy is to use superconducting circuits, which operate at very cold temperatures and consequently have longer coherence times.

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A team at the University of Sydney and Microsoft, in collaboration with Stanford University in the US, has miniaturised a component that is essential for the scale-up of quantum computing. The work constitutes the first practical application of a new phase of matter, first discovered in 2006, the so-called topological insulators.

Beyond the familiar phases of matter — solid, liquid, or gas — are materials that operate as insulators in the bulk of their structures but have surfaces that act as conductors. Manipulation of these materials provide a pathway to construct the circuitry needed for the interaction between and classical systems, vital for building a practical quantum .

Theoretical work underpinning the discovery of this new phase of matter was awarded the 2016 Nobel Prize in Physics.

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Researchers from Google and the University of California Santa Barbara have taken an important step towards the goal of building a large-scale quantum computer.

Writing in the journal Quantum Science and Technology, they present a new process for creating superconducting interconnects, which are compatible with existing superconducting .

The race to develop the first large-scale error-corrected quantum computer is extremely competitive, and the process itself is complex. Whereas classical computers encode data into binary digits (bits) that exist in one of two states, a quantum computer stores information in quantum bits (qubits) that may be entangled with each other and placed in a superposition of both states simultaneously.

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Quantum ghost imaging can achieve unprecedented sensitivity by detecting not just the extremely small amount of light straying off a dim target, but also its interactions with other light in the surrounding environment to obtain more information than traditional methods.

A satellite equipped with the new quantum sensor would be able to identify and track targets that are currently invisible from space, such as stealth bombers taking off at night, according to researchers.

Scientists are developing a probe to track stealth bombers at night.

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Furthermore, with advancements in quantum computing and machine learning, many notable public figures, including Stephen Hawking and Elon Musk, have indicated a growing concern with the imminent threat of AI surpassing human intelligence (Gosset, 2017). For instance, Darrell M. West, a political scientist, has proposed a protectionist framework that appeals to transhumanism, in which he restructures socioeconomic policy to account for changes in technology-induced unemployment. In particular, he posits that “Separating the dispersion of health care, disability, and pension benefits outside of employment offers workers with limited skills social benefits on a universal basis” (West, 2015). Expounding upon this equivocation, a more viable solution to potential unemployment is the realization of a multi-faceted policy which advocates the improvement of STEM-related education on a broad economic base, with habituation programs for the unskilled workforce. That is, with the implementation of appropriate and reformatory policies concerning the future development of AI technologies, this sector provides an economic incentive for new job creation, compatible with industrial development.

Prompt: What are the political implications of artificial intelligence technology and how should policy makers ensure this technology will benefit diverse sectors of society?

In recent years, the rapid development and mass proliferation of artificial intelligence have had various sociopolitical implications. It is a commonly held belief that the emergence of this technology will have an unprecedented impact on policies and political agendas. However, such discourse often lacks a geopolitical and social dimension, which limits the breadth of analysis. Further, little consideration has been given to potential employment and public policy reform. Growing concerns have been raised regarding the potential risk inherent in the evolution of strong AI, which provides the basis for transhumanism, whereby it is conjectured that AI will eventually be able to surpass human intelligence. As such, it is incumbent upon the upcoming generation of policymakers to implement and adopt necessary measures, which will provide a careful, multilateral framework, ultimately achieving market-oriented technological advancement with respect to employment and public policy.

Machine learning, the interplay of computer science and neuroscience, is a rapidly developing field that has been a source of much political controversy in recent years. While emerging technologies have significantly improved production quality and efficiency across industries, they have also raised concerns such as job displacement and other unfavourable socioeconomic implications (Karsten & West, 2015). In particular, the growing shortage of job opportunities has furnished increasing levels of unemployment and has, in various instances, lead to unwanted economic stagnation. On the subject of potential future unemployment, many policymakers have proposed an increase in Earned Income Tax Credit, which provides a collateral basic income and encourages profit-sharing (West, 2015).

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IBM has been offering quantum computing as a cloud service since last year when it came out with a 5 qubit version of the advanced computers. Today, the company announced that it’s releasing 20-qubit quantum computers, quite a leap in just 18 months. A qubit is a single unit of quantum information.

The company also announced that IBM researchers had successfully built a 50 qubit prototype, which is the next milestone for quantum computing, but it’s unclear when we will see this commercially available.

While the earliest versions of IBM’s quantum computers were offered for free to build a community of users, and help educate people on programming and using these machines, today’s announcement is the first commercial offering. It will be available by the end of the year.

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