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Researchers propose a way to relieve the material requirements needed to realize topological quantum computers.

Quantum information is a powerful technology for increasing the amount of information that can be processed and communicated securely. Using quantum entanglement to securely distribute a secret quantum state among multiple parties is known as “quantum state sharing.”

An important protocol in quantum networks and cryptography, quantum state sharing works like this (in simple terms): a secret quantum state is divided into n shares and given to n players. The secret state can only be reconstructed if k (where kn/2) players cooperate, while the remaining n-k players cannot access the information. This protocol can also be used for quantum error correction, allowing the reconstruction of the secret state even if some of the information is lost.

In quantum information, there are two types of systems: discrete variable and continuous variable systems. Discrete variable systems are good because they don’t lose information easily, while continuous variable systems are good because the generation and processing of quantum states are deterministic rather than probabilistic, which enables a high degree of precision.

A team of researchers at Kyoto University is exploring the use of higher dimensions in de Sitter space to explain gravity in the early universe. By developing a method to compute correlation functions among fluctuations, they aim to bridge the gap between Einstein’s theory of general relativity and quantum mechanics. This could potentially validate superstring theory and enable practical calculations about the early universe’s subtle changes. Although initially tested in a three-dimensional universe, the analysis may be extended to a four-dimensional universe for real-world applications.

Having more tools helps; having the right tools is better. Utilizing multiple dimensions may simplify difficult problems — not only in science fiction but also in physics — and tie together conflicting theories.

For example, Einstein’s theory of general relativity — which resides in the fabric of space-time warped by planetary or other massive objects — explains how gravity works in most cases. However, the theory breaks down under extreme conditions such as those existing in black holes and cosmic primordial soups.

Open-standard computer chip instructions could help speed up futuristic technology like autonomous cars and quantum computers.

This could lead to chat gpt infinite ♾️ ✨️

Machine learning is a fascinating and exciting field within computer science. Recently, this excitement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the finite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practical, infinite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that can lead to exponential speedups in situations where classical algorithms scale polynomially. Finally, we also map out an experimental implementation which can be used as a blueprint for future photonic demonstrations.

Developing new quantum devices relies on controlling how electrons behave. A material called graphene, a single layer of carbon atoms, has fascinated researchers in recent years because its electrons behave as if they have no mass. For decades, scientists have also been interested in high-temperature superconductors: ceramic materials where electron interactions yield a macroscopic quantum state where electrons pair with each other. They do so at a temperature above the usual superconducting temperature of metals, which approaches absolute zero.

In a recent study published in Physical Review Letters, researchers from the SUNY Polytechnic Institute, Stony Brook University and the Brookhaven National Laboratory in the US, along with Aalto University in Finland, demonstrated a new electronic device that employs the unique ways in which electrons behave in these two materials— graphene and high-temperature superconductors.

The experiment, led by Sharadh Jois and Ji Ung Lee from SUNY with the support of theoretical work done by Jose Lado, assistant professor at Aalto, demonstrated a new quantum device that combines both graphene and an unconventional high-temperature superconductor.

Quantum computing and simulations are creating transformative opportunities by exploiting the principles of quantum mechanics in new ways to generate and process information. It is expected that a variety of areas ranging from day-to-day activities to making advanced scientific discoveries are going to benefit from such computations. Several early stage applications of quantum computing and simulation have already been demonstrated, and these preliminary results show that quantum computing and simulations could significantly accelerate the deployment of new technologies urgently needed to meet the growing demand for energy while safeguarding the environment.

The discovery of new quantum materials with magnetic properties could pave the way for ultra-fast and considerably more energy-efficient computers and mobile devices. So far, these types of materials have been shown to work only in extremely cold temperatures. Now, a research team at Chalmers University of Technology in Sweden are the first to make a device made of a two-dimensional magnetic quantum material work in room temperature.

Today’s rapid IT expansion generates enormous amounts of digital data that needs to be stored, processed, and communicated. This comes with an ever-increasing need for energy—projected to consume more than 30% of the world’s total energy consumption by 2050. To combat the problem, the research community has entered a new paradigm in materials science. The research and development of two-dimensional quantum materials, that form in sheets and are only a few atoms thick, have opened new doors for sustainable, faster and more energy-efficient data storage and processing in computers and mobiles.

The first atomically thin material to be isolated in a laboratory was graphene, a single atom-thick plane of graphite, that resulted in the 2010 Nobel Prize in Physics. And in 2017, two-dimensional materials with magnetic properties were discovered for the first time. Magnets play a fundamental role in our everyday lives, from sensors in our cars and home appliances to computer data storage and memory technologies, and the discovery opened for new and more sustainable solutions for a wide range of technology devices.

This paper presents a relevant contribution towards an effective and convenient “Science 2.0” universal computational framework to achieve deeper cognitive intelligence at your fingertips and beyond. Computational information conservation theory CICT can help us to develop competitive applications and even advanced quantum cognitive computational application and systems towards deep computational cognitive intelligence. CICT new awareness of a discrete HG hyperbolic geometry subspace reciprocal space, RS of coded heterogeneous hyperbolic structures, underlying the familiar Q Euclidean direct space, DS system surface representation can open the way to holographic information geometry HIG to recover lost coherence information in system description and to develop advanced quantum cognitive systems. This paper is a relevant contribution towards an effective and convenient “Science 2.0” unive.