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Remote entanglement is crucial for quantum computing, sensing, and communication. Traditional methods for entanglement generation often depend on direct interactions between quantum bits (qubits) or the exchange of entangled photons. In this study, we demonstrate an alternative approach, where we create and preserve entanglement between two noninteracting qubits through dissipation into a shared waveguide.

While dissipation is typically viewed as detrimental, tailored dissipation can be harnessed to drive a system into complex quantum states while actively protecting it from decoherence. This approach, known as autonomous stabilization, has been previously used to create entanglement. However, entanglement stabilization has been confined to short distances due to the challenge of engineering shared dissipation between remote sites. Our experiment overcomes this challenge by employing an open waveguide as a one-dimensional photonic bath. We demonstrate that, under appropriate conditions, the interference of photons emitted into a waveguide from two qubits can stabilize them in an entangled stationary state when the qubits are strongly driven. Crucially, we can reconstruct the entangled state despite significant waveguide-induced dissipation by measuring the emitted photons. Our demonstration is made possible by precise control over qubit frequencies and efficient qubit-waveguide interfaces in superconducting circuits.

Eminent physicists assemble to discuss quantum enigmas.

John von Neumann, John Wheeler, Hans Bethe, Robert Serber, Robert Marshak, Abraham Pais, J. Robert Oppenheimer, David Bohm, and Richard Feynman at the Shelter Island Conference of 1947:

https://repository.aip.org/islandora/object/nbla%3A310818…

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Neutron stars are timelike matter with a maximum mass of about 2.34 solar masses in quantum chromodynamics (the strong color force). Black holes are spacelike matter that have no maximum mass, but a minimum mass of 2.35 solar masses. Indeed, black holes have been identified with millions or billions of solar masses.

In work published in Science Advances, Hayato Goto from the RIKEN Center for Quantum Computing in Japan has proposed a new quantum error correction approach using what he calls “many-hypercube codes.”

Physicists at the University of Bonn and the University of Kaiserslautern-Landau (RPTU) have created a one-dimensional gas out of light. This has enabled them to test theoretical predictions about the transition into this exotic state of matter for the first time. The method used in the experiment by the researchers could be used for examining quantum effects. The results have been published in Nature Physics.

The foundation of nearly all quantum information applications—such as computation and communication—rely on the quantum properties of superposition and entanglement.

Research teams from Wuhan University and the China University of Geosciences (Wuhan) have revealed new insights into the formation mechanism of nitrogen-vacancies (NV) centers in type-Ib diamonds, a phenomenon critical to quantum sensing and computing advancements. Using a novel irradiation and annealing method, the teams demonstrated how controlled temperature and orientation can significantly increase the density and depth of NV centers, paving the way for new applications in biological imaging and quantum technologies.

Scientists have finally figured out a way to connect the dots between the macroscopic and the microscopic worlds. Their magical equation might provide us answers to questions like why black holes don’t collapse and how quantum gravity works.

Researchers from the University of Pisa developed a quantum subroutine to improve matrix multiplication for AI and machine learning applications.

When you multiply two large matrices—this is a common task in fields like machine learning, but it can be time-consuming, even for powerful computers…

In a recent study published in IEEE Access, a team of researchers from the University of Pisa introduced a quantum subroutine designed to streamline matrix multiplication. This subroutine is a new feature in the toolbox of matrix multiplication that could improve computational efficiency, particularly in applications like machine learning and data processing.

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