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Category: quantum physics – Page 30

Lockheed uses IBM quantum processor to solve major chemistry puzzle

Researchers at IBM and Lockheed Martin teamed up high-performance computing with quantum computing to accurately model the electronic structure of ‘open-shell’ molecules, methylene, which has been a hurdle with classic computing over the years. This is the first demonstration of the sample-based quantum diagonalization (SQD) technique to open-shell systems, a press release said.

Quantum computing, which promises computations at speeds unimaginable by even the fastest supercomputers of today, is the next frontier of computing. Leveraging quantum states of molecules to serve as quantum bits, these computers supersede computational capabilities that humanity has had access to in the past and open up new research areas.

Detecting the primordial black holes that could be today’s dark matter

Besides particles like sterile neutrinos, axions and weakly interacting massive particles (WIMPs), a leading candidate for the cold dark matter of the universe are primordial black holes—black holes created from extremely dense conglomerations of subatomic particles in the first seconds after the Big Bang.

Primordial black holes (PBHs) are classically stable, but as shown by Stephen Hawking in 1975, they can evaporate via , radiating nearly like a blackbody. Thus, they have a lifetime; it’s proportional to the cube of their initial mass. As it’s been 13.8 billion years since the Big Bang, only PBHs with an initial mass of a trillion kilograms or more should have survived to today.

However, it has been suggested that the lifetime of a black hole might be considerably longer than Hawking’s prediction due to the memory burden effect, where the load of information carried by a black hole stabilizes it against evaporation.

Simpler method refines ultrapure diamond film fabrication for quantum and electronic applications

Diamond is one of the most prized materials in advanced technologies due to its unmatched hardness, ability to conduct heat and capacity to host quantum-friendly defects. The same qualities that make diamond useful also make it difficult to process.

Engineers and researchers who work with diamond for quantum sensors, or thermal management technologies need it in ultrathin, ultrasmooth layers. But traditional techniques, like laser cutting and polishing, often damage the material or create surface defects.

Ion implantation and lift-off is a way to separate a thin layer of diamond from a larger crystal by bombarding a diamond with high-energy carbon ions, which penetrate to a specific depth below the surface. The process creates a buried layer in the diamond substrate where the crystalline lattice has been disrupted. That damaged layer effectively acts like a seam: Through high-temperature annealing, it turns into smooth graphite, allowing for the diamond layer above it to be lifted off in one uniform, ultrathin wafer.

Unlocking Scalable Chemistry Simulations for Quantum-Supercomputing

We’re announcing the world’s first scalable, error-corrected, end-to-end computational chemistry workflow. With this, we are entering the future of computational chemistry.

Quantum computers are uniquely equipped to perform the complex computations that describe chemical reactions – computations that are so complex they are impossible even with the world’s most powerful supercomputers.

However, realizing this potential is a herculean task: one must first build a large-scale, universal, fully fault-tolerant quantum computer – something nobody in our industry has done yet. We are the farthest along that path, as our roadmap, and our robust body of research, proves. At the moment, we have the world’s most powerful quantum processors, and are moving quickly towards universal fault tolerance. Our commitment to building the best quantum computers is proven again and again in our world-leading results.

Physicists rewrite quantum rules by bending light through both time and space

The significance of this experiment extends beyond telecommunications, computing, and medicine. Metamaterials like the ones used in this research could have broader applications in industries such as energy, transportation, aerospace, and defense.

For instance, controlling light at such a fine level might enable more efficient energy systems or advanced sensor technologies for aircraft and vehicles. Even black hole physics could be explored through these new quantum experiments, adding to the wide-ranging impact of this research.

As technology advances, the role of metamaterials and quantum physics will become increasingly critical. The ability to manipulate light in space and time holds the promise of reshaping how we interact with the world, offering faster, more efficient, and more precise tools across industries.

Machine learning method generates circuit synthesis for quantum computing

Researchers from the University of Innsbruck have unveiled a novel method to prepare quantum operations on a given quantum computer, using a machine learning generative model to find the appropriate sequence of quantum gates to execute a quantum operation.

The study, recently published in Nature Machine Intelligence, marks a significant step forward in realizing the full extent of .

Generative models like diffusion models are one of the most important recent developments in (ML), with models such as Stable Diffusion and DALL·E revolutionizing the field of image generation. These models are able to produce high quality images based on text description.

Quantum eyes on energy loss: Diamond quantum imaging can enable next-gen power electronics

Improving energy conversion efficiency in power electronics is vital for a sustainable society, with wide-bandgap semiconductors like GaN and SiC power devices offering advantages due to their high-frequency capabilities. However, energy losses in passive components at high frequencies hinder efficiency and miniaturization. This underscores the need for advanced soft magnetic materials with lower energy losses.

In a study published in Communications Materials, a research team led by Professor Mutsuko Hatano from the School of Engineering, Institute of Science, Tokyo, Japan, has developed a novel method for analyzing such losses by simultaneously imaging the amplitude and phase of alternating current (AC) stray fields, which are key to understanding hysteresis losses.

Using a diamond quantum sensor with nitrogen-vacancy (NV) centers and developing two protocols—qubit frequency tracking (Qurack) for kHz and quantum heterodyne (Qdyne) imaging for MHz frequencies—they realized wide-range AC magnetic field imaging. This study was carried out in collaboration with Harvard University and Hitachi, Ltd.