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A research team, led by Professor Jung-Woo Yoo from the Department of Materials Science and Engineering at UNIST has unveiled a new type of magnetic memory device, designed to reduce power consumption and heat generation in MRAM semiconductors. The work was published in Nature Communications on October 10, 2024.

Magnetic random access memory (MRAM) represents the next generation of memory technology, combining the strengths of NAND flash and DRAM. It is a non-volatile storage solution, meaning data is preserved even when the device is powered off, while also achieving speeds comparable to DRAM. MRAM has already seen commercialization in sectors requiring fast and reliable data access.

Traditional MRAM devices rely on to write and erase data. In these devices, when the magnetization directions of the two magnetic layers are aligned (parallel), the resistance is low; when they are opposite (antiparallel), the resistance is high. Data is then represented as binary states (0 and 1) based on these configurations. However, changing the magnetization direction necessitates a current exceeding a critical threshold, which leads to significant and heat generation.

Researchers have achieved high gate fidelities up to 99.98% using a new double-transmon coupler. This development enhances quantum computing performance and supports the advancement toward fault-tolerant systems.

Researchers from the RIKEN Center for Quantum Computing and Toshiba have developed a quantum computer gate using a double-transmon coupler (DTC), a device previously proposed in theory to enhance the fidelity of quantum gates significantly. With this innovation, the team achieved a fidelity of 99.92% for a two-qubit device known as a CZ gate and 99.98% for a single-qubit gate.

This milestone, part of the Q-LEAP project, not only improves the performance of noisy intermediate-scale quantum (NISQ) devices but also lays the groundwork for fault-tolerant quantum computation through more effective error correction.

PRESS RELEASE —-Toshiba Corporation (Toshiba) has confirmed a technology that they claimed promises to advance progress toward the development of higher-performance quantum computers through an investigation of a potential advance in quantum computing. Experiments conducted by a joint research group from Toshiba and RIKEN, one of Japan’s largest comprehensive research institutions, have successfully realized a Double-Transmon Coupler, a solution for superconducting quantum computers initially proposed by Toshiba. The researchers achieved a world-class fidelity of 99.90% for a two-qubit gate, which is at the heart of quantum computation. Fidelity is a standard performance indicator for quantum gates, quantifying how close an operation is to the ideal in a range from 0% to 100%, with higher percentages indicating greater accuracy in the quantum gate’s operation.

Originally proposed by Toshiba in a paper from September 2022, the Double-Transmon Coupler is a tunable coupler that holds the key to improving the performance of superconducting quantum computers. In successful experimental realization, Toshiba and RIKEN have confirmed its theoretical superiority over conventional tunable couplers in suppressing the long-standing problem of unnecessary residual coupling and enabling high-speed, high-fidelity two-qubit gates.

To improve the performance of two-qubit gates, the coherence time, the period for which the quantum superposition state can be maintained — critical in quantum computers — must be extended. Gates must also be executed quickly and the strength of residual coupling must be suppressed to reduce the errors it causes. The Toshiba-RIKEN team achieved a world-class coherence time for the transmon qubit, a short gate time of 48 ns, and reduced the residual coupling strength to as low as 6 kHz, thereby achieving a fidelity of 99.90%.

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My name is Artem, I’m a graduate student at NYU Center for Neural Science and researcher at Flatiron Institute (Center for Computational Neuroscience).

In this video, we explore the Nobel Prize-winning Hodgkin-Huxley model, the foundational equation of computational neuroscience that reveals how neurons generate electrical signals. We break down the biophysical principles of neural computation, from membrane voltage to ion channels, showing how mathematical equations capture the elegant dance of charged particles that enables information processing.

It’s hard to tell when you’re catching some rays at the beach, but light packs a punch. Not only does a beam of light carry energy, it can also carry momentum. This includes linear momentum, which is what makes a speeding train hard to stop, and orbital angular momentum, which is what the Earth carries as it revolves around the sun.

In a new paper, scientists seeking better methods for controlling the quantum interactions between light and matter have demonstrated a novel way to use light to give electrons a spinning kick. They reported the results of their experiment, which shows that a light beam can reliably transfer to itinerant electrons in graphene, on Nov. 26, 2024, in the journal Nature Photonics.

Having tight control over the way that light and matter interact is an essential requirement for applications like quantum computing or quantum sensing. In particular, scientists have been interested in coaxing electrons to respond to some of the more exotic shapes that light beams can assume.

Researchers at INRS have developed a synthetic photonic lattice capable of generating and manipulating quantum states of light, paving the way for promising advancements in applications ranging from quantum computing to secure quantum communication protocols.

A study co-directed by Professor Roberto Morandotti of Institut national de la recherche scientifique (INRS) in collaboration with teams from Germany, Italy, and Japan paves the way for innovative solutions that could enable the development of a system to process quantum information with both simplicity and power.

Their work, just published in the journal Nature Photonics, presents a method for manipulating the photonic states of light in a never-before-seen way, offering greater control over the evolution of photon propagation. This control makes it possible to improve the detection and number of photon coincidences, as well as the efficiency of the system.

“Life is incredible.” Here’s how a brain implant changed the life of Jon Nelson, who long suffered from severe depression. Now a patient advocate for startup Motif, he spoke to Emily Chang about the hope of using neurotech to treat mental illnesses.

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Conventional components perform incredibly inefficiently at these sub-freezing temperatures, the scientists said. They’re also very hard to maintain — as more and more qubits are added to a system, the more heat is emitted, which makes it more difficult and expensive to sustain these ultralow temperatures.

Because the new transistor — dubbed the “cryo-CMOS transistor” — is optimized to operate at temperatures under 1 K and emit near-zero heat, it offers plenty of advantages over traditional electronics, representatives of the Finnish company SemiQon, which developed the transistor, said in a statement.

Ed Boyden is a professor at the MIT Media Lab working on the most advanced brain-computer interfacing technology currently available, optogenetics. At Singularity Summit 2009.

https://scirate.com/arxiv/2411.

Researchers present a #quantummachinelearning advantage of families of constant depth local quantum circuits over reasonably constrained log-log-depth classical circuits.

Quantum…


One of the core challenges of research in quantum computing is concerned with the question whether quantum advantages can be found for near-term quantum circuits that have implications for practical applications. Motivated by this mindset, in this work, we prove an unconditional quantum advantage in the probably approximately correct (PAC) distribution learning framework with shallow quantum circuit hypotheses. We identify a meaningful generative distribution learning problem where constant-depth quantum circuits using one and two qubit gates (QNC^0) are superior compared to constant-depth bounded fan-in classical circuits (NC^0) as a choice for hypothesis classes. We hence prove a PAC distribution learning separation for shallow quantum circuits over shallow classical circuits. We do so by building on recent results by Bene Watts and Parham on unconditional quantum advantages for sampling tasks with shallow circuits, which we technically uplift to a hyperplane learning problem, identifying non-local correlations as the origin of the quantum advantage.

Submitted 23 Nov 2024 to Quantum Physics [quant-ph]