Lifeboat Foundation

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.

Continue reading “Quantum computing is outperformed by new type of traditional computing” »

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.

By Karmela Padavic-Callaghan

Cooper-Pair Splitting on Demand.

A proposed device can repeatedly grab pairs of electrons from a superconductor and separate them while preserving their entangled state.

By adiabatically changing the energy levels of two quantum dots, theoreticians predict that it should be possible to control the splitting of Cooper pairs from a superconductor. Such an adiabatic Cooper pair splitter could serve as an on-demand source of entangled electrons in future solid-state quantum technologies.

Researchers have successfully extended the lifespan of time crystals, confirming a theoretical concept proposed by Frank Wilczek. This marks a significant step forward in quantum physics.

A team from TU Dortmund University recently succeeded in producing a highly durable time crystal that lived millions of times longer than could be shown in previous experiments. By doing so, they have corroborated an extremely interesting phenomenon that Nobel Prize laureate Frank Wilczek postulated around ten years ago and which had already found its way into science fiction movies. The results have now been published in Nature Physics.

Groundbreaking achievement in time crystal research.