Classical computers can sometimes outperform quantum computers thanks to new algorithms, challenging the idea that quantum always prevails.
NYU researchers have developed a new method that allows classical computers to perform certain tasks faster and more efficiently than quantum computers.
The researchers applied the higher resonant radio frequency, which prompted any normal, “hot” fermions in the liquid to ring in response. The researchers then could zero in on the resonating fermions and track them over time to create “movies” that revealed heat’s pure motion — a sloshing back and forth, similar to sound waves.
“For the first time, we can take pictures of this substance as we cool it through the critical temperature of superfluidity, and directly see how it transitions from being a normal fluid, where heat equilibrates boringly, to a superfluid where heat sloshes back and forth,” Zwierlein says.
The experiments mark the first time scientists have been able to image second sound directly and the pure motion of heat in a superfluid quantum gas. The researchers plan to extend their work to map heat’s behavior more precisely in other ultracold gases. Then, they say their findings can be scaled up to predict how heat flows in other strongly interacting materials, such as high-temperature superconductors and neutron stars.
Nuclear physicists have discovered gravity’s profound influence on the quantum scale, revealing the strong force’s distribution within protons for the first time. This groundbreaking research, combining historical theoretical insights with modern experimental data, offers unprecedented understanding of the proton’s internal dynamics and sets the stage for future discoveries in nuclear science.
Gravity’s influence is unmistakably evident throughout the observable universe. Its effects are observed in the synchronized orbits of moons around planets, in comets that deviate from their paths due to the gravitational pull of large stars, and in the majestic spirals of enormous galaxies. These magnificent phenomena highlight the role of gravity on the grandest scales of matter. Meanwhile, nuclear physicists are uncovering the significant contributions of gravity at the very smallest scales of matter.
New research conducted by nuclear physicists at the U.S. Department of Energy’s Thomas Jefferson National Accelerator Facility is using a method that connects theories of gravitation to interactions among the smallest particles of matter to reveal new details at this smaller scale. The research has now revealed, for the first time, a snapshot of the distribution of the strong force inside the proton. This snapshot details the shear stress the force may exert on the quark particles that make up the proton. The result was recently published in Reviews of Modern Physics.
Matching quantum computing with Tensor networks, and varying then to get the data you need. It’s a good read, about 4 minutes and goes into more detail. Apparently there’s no errors like there is in quantum computing with some adjustments.
Quantum computing has long been celebrated for its potential to surpass traditional computing in terms of speed and memory efficiency. This innovative technology promises to revolutionize our ability to predict physical phenomena that were once deemed impossible to forecast.
The essence of quantum computing lies in its use of quantum bits, or qubits, which, unlike the binary digits of classical computers, can represent values anywhere between 0 and 1.
This fundamental difference allows quantum computers to process and store information in a way that could vastly outpace their classical counterparts under certain conditions.
Quantum computing has been hailed as a technology that can outperform classical computing in both speed and memory usage, potentially opening the way to making predictions of physical phenomena not previously possible.
Many see quantum computing’s advent as marking a paradigm shift from classical, or conventional, computing. Conventional computers process information in the form of digital bits (0s and 1s), while quantum computers deploy quantum bits (qubits) to store quantum information in values between 0 and 1.
Under certain conditions, this ability to process and store information in qubits can be used to design quantum algorithms that drastically outperform their classical counterparts. Notably, quantum’s ability to store information in values between 0 and 1 makes it difficult for classical computers to perfectly emulate quantum ones.
Reliable quantum gates are the fundamental component of quantum information processing. However, achieving high-dimensional unitary transformations in a scalable and compact manner with ultrahigh fidelities remains a great challenge.
To address this issue, scientists in China showcase the use of deep diffractive neural networks (D2NNs) to construct a series of high-dimensional quantum gates, which are encoded by the spatial modes of photons. This work, published in Light: Science & Applications, offers a new paradigm for quantum gate design using deep learning.
Quantum computing holds the promise of transforming our information processing methodologies, and at its core, reliable quantum logic gates play an essential role in quantum information processing.
A new fusion of materials, each with special electrical properties, has all the components required for a unique type of superconductivity that could provide the basis for more robust quantum computing. The new combination of materials, created by a team led by researchers at Penn State, could also provide a platform to explore physical behaviors similar to those of mysterious, theoretical particles known as chiral Majoranas, which could be another promising component for quantum computing.
The new study appears in the journal Science. The work describes how the researchers combined the two magnetic materials in what they called a critical step toward realizing the emergent interfacial superconductivity, which they are currently working toward.
Superconductors—materials with no electrical resistance —are widely used in digital circuits, the powerful magnets in magnetic resonance imaging (MRI) and particle accelerators, and other technology where maximizing the flow of electricity is crucial.
Qubits are the building block for quantum technology, and finding or building qubits that are stable and easily manipulated is one of the central goals of quantum technology research. Scientists have found that an atom of erbium—a rare-earth metal sometimes used in lasers or to color glass—can be a very effective qubit.
To make erbium qubits, erbium atoms are placed in “host materials,” where the erbium atoms replace some of the material’s original atoms. Two research groups—one at quantum startup memQ, a Chicago Quantum Exchange corporate partner, and one at the US Department of Energy’s Argonne National Laboratory, a CQE member—have used different host materials for erbium to advance quantum technology, demonstrating the versatility of this kind of qubit and highlighting the importance of materials science to quantum computing and quantum communication.
The two projects address challenges that quantum computing researchers have been trying to solve: engineering multi-qubit devices and extending the amount of time qubits can hold information.
The Quantum Insider (TQI) is the leading online resource dedicated exclusively to Quantum Computing.
Making a strange state of matter called a time crystal inside a quantum computer helped researchers stabilise a fragile quantum state inspired by Schrödinger’s cat.
