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Category: quantum physics – Page 342

A new quantum algorithm for classical mechanics with an exponential speedup

Quantum computers promise to solve some problems exponentially faster than classical computers, but there are only a handful of examples with such a dramatic speedup, such as Shor’s factoring algorithm and quantum simulation. Of those few examples, the majority of them involve simulating physical systems that are inherently quantum mechanical — a natural application for quantum computers. But what about simulating systems that are not inherently quantum? Can quantum computers offer an exponential advantage for this?

In “Exponential quantum speedup in simulating coupled classical oscillators”, published in Physical Review X (PRX) and presented at the Symposium on Foundations of Computer Science (FOCS 2023), we report on the discovery of a new quantum algorithm that offers an exponential advantage for simulating coupled classical harmonic oscillators. These are some of the most fundamental, ubiquitous systems in nature and can describe the physics of countless natural systems, from electrical circuits to molecular vibrations to the mechanics of bridges. In collaboration with Dominic Berry of Macquarie University and Nathan Wiebe of the University of Toronto, we found a mapping that can transform any system involving coupled oscillators into a problem describing the time evolution of a quantum system. Given certain constraints, this problem can be solved with a quantum computer exponentially faster than it can with a classical computer.

AI Can Recreate Images From Human Brain Waves With ‘Over 75% Accuracy’

Scientists were reportedly able to use artificial intelligence (AI) to reconstruct images solely from people’s brain activity with over 75% accuracy for the first time ever.

According to Japanese newspaper The Mainichi, recreating images from brain activity is usually only possible when a subject is actually seeing the images with their own eyes, or when the type of images, such as faces, letters or simple figures, were specified.

However, a team of researchers at the National Institutes for Quantum Science and Technology (QST) in Japan have now demonstrated that it’s possible to accurately reconstruct complex images with AI — based almost solely from a person’s thoughts.

Quantum computers could solve problems in minutes that would take today’s supercomputers millions of years

“We’re looking at a race, a race between China, between IBM, Google, Microsoft, Honeywell,” Kaku said. “All the big boys are in this race to create a workable, operationally efficient quantum computer. Because the nation or company that does this will rule the world economy.”

It’s not just the economy quantum computing could impact. A quantum computer is set up at Cleveland Clinic, where Chief Research Officer Dr. Serpil Erzurum believes the technology could revolutionize the world of health care.

Quantum computers can potentially model the behavior of proteins, the molecules that regulate all life, Erzurum said. Proteins change their shape to change their function in ways that are too complex to follow, but quantum computing could change that understanding.

With a quantum “squeeze,” clocks could keep even more precise time, MIT researchers propose

More stable clocks could measure quantum phenomena, including the presence of dark matter.

A new MIT study finds that even if all noise from the outside world is eliminated, the stability of clocks, laser beams, and other oscillators would still be vulnerable to quantum mechanical effects.

Clocks, lasers, and other oscillators could be tuned to super-quantum precision, allowing researchers to track infinitesimally small differences in time, according to a new MIT study.

Superconducting nanowires detect single protein ions

An international research team led by quantum physicist Markus Arndt (University of Vienna) has achieved a breakthrough in the detection of protein ions: Due to their high energy sensitivity, superconducting nanowire detectors achieve almost 100% quantum efficiency and exceed the detection efficiency of conventional ion detectors at low energies by a factor of up to a 1,000.

In contrast to conventional detectors, they can also distinguish macromolecules by their impact energy. This allows for more sensitive detection of proteins and it provides additional information in mass spectrometry.

  • Breakthrough in protein ion detection using superconducting nanowire detectors, significantly outperforming conventional methods.

    • Physicists May Have Found a Hard Limit on The Performance of Large Quantum Computers

    A newly discovered trade-off in the way time-keeping devices operate on a fundamental level could set a hard limit on the performance of large-scale quantum computers, according to researchers from the Vienna University of Technology.

    While the issue isn’t exactly pressing, our ability to grow systems based on quantum operations from backroom prototypes into practical number-crunching behemoths will depend on how well we can reliably dissect the days into ever finer portions. This is a feat the researchers say will become increasingly more challenging.

    Whether you’re counting the seconds with whispers of Mississippi or dividing them up with the pendulum-swing of an electron in atomic confinement, the measure of time is bound by the limits of physics itself.

    Model Correctly Predicts High-Temperature Superconducting Properties

    A first-principles model accounts for the wide range of critical temperatures (Tc’s) for four materials and suggests a parameter that determines Tc in any high-temperature superconductor.

    Since the first high-temperature superconducting materials, known as the cuprates, were discovered in 1986, researchers have struggled to explain their properties and to find materials with even higher superconducting transition temperatures (Tc’s). One puzzle has been the cuprates’ wide variation in Tc, ranging from below 10 K to above 130 K. Now Masatoshi Imada of Waseda University in Japan and his colleagues have used first-principles calculations to determine the order parameters—which measure the density of superconducting electrons—for four cuprate materials and have predicted the Tc’s based on those order parameters [1]. The researchers have also found what they believe is the fundamental parameter that determines Tc in a given material, which they hope will lead to the development of higher-temperature superconductors.

    For each material, Imada and his colleagues applied the basic principles of quantum mechanics, focusing on the planes of copper and oxygen atoms that are known to host the superconducting electrons. They used a combination of numerical techniques, including one supplemented by machine learning, and did not require any adjustable parameters.