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Category: quantum physics – Page 4

What Is Superposition and Why Is It Important?

Imagine touching the surface of a pond at two different points at the same time. Waves would spread outward from each point, eventually overlapping to form a more complex pattern. This is a superposition of waves. Similarly, in quantum science, objects such as electrons and photons have wavelike properties that can combine and become what is called superposed.

While waves on the surface of a pond are formed by the movement of water, quantum waves are mathematical. They are expressed as equations that describe the probabilities of an object existing in a given state or having a particular property. The equations might provide information on the probability of an electron moving at a specific speed or residing in a certain location. When an electron is in superposition, its different states can be thought of as separate outcomes, each with a particular probability of being observed. An electron might be said to be in a superposition of two different velocities or in two places at once. Understanding superposition may help to advance quantum technology such as quantum computers.

One of the fundamental principles of quantum mechanics, superposition explains how a quantum state can be represented as the sum of two or more states.

Scientists Turned Our Cells Into Quantum Computers—Sort Of

For the protein qubit to “encode” more information about what is going on inside a cell, the fluorescent protein needs to be genetically engineered to match the protein scientists want to observe in a given cell. The glowing protein is then attached to the target protein and zapped with a laser so it reaches a state of superposition, turning it into a nano-probe that picks up what is happening in the cell. From there, scientists can infer how a certain biological process happens, what the beginnings of a genetic disease look like, or how cells respond to certain treatments.

And eventually, this kind of sensing could be used in non-biological applications as well.

“Directed evolution on our EYFP qubit could be used to optimize its optical and spin properties and even reveal unexpected insights into qubit physics,” the researchers said. “Protein-based qubits are positioned to take advantage of techniques from both quantum information sciences and bioengineering, with potentially transformative possibilities in both fields.”

Superradiance Discovery Extends Quantum Entanglement Range 17-Fold

When the light field becomes more uniform, all the atoms find themselves optically close to each other, even if they are spatially distant. In other words, the “ambient” near-zero refractive index relaxes the strict distance between the atoms’ positions, an essential condition for the entanglement of quantum particles. Quantum entanglement corresponds to correlations between particles, essential for the development of information and quantum computers.

From electrodynamics to quantum computing

This is where the promising contribution of a team of researchers from UNamur, Harvard and Michigan Technological University (MTU) comes in, supported by Dr. Larissa Vertchenko, from Danish quantum technology company Sparrow Quantum. Adrien Debacq, FNRS aspirant researcher at the Namur Institute of Structured Matter (NISM) and co-author of the paper, assisted by Harvard PhD student Olivia Mello and Dr Larissa Vertchenko, have together theoretically developed a photonic chip capable of radically improving the range of entanglement between transmitters, up to 17 times greater than in a vacuum.

Quantum Computing Meets Finance

Eric Ghysels made a name for himself in financial econometrics and time-series analysis. Now he translates financial models into quantum algorithms.

Economist Eric Ghysels has spent most of his career fascinated by a fundamental problem in the financial industry: figuring out how to put a price on any financial asset whose future value depends on market conditions. Ghysels, a professor at the University of North Carolina at Chapel Hill, has now set himself a new problem: studying the impact that quantum computing could have on solving asset pricing, portfolio optimization, and other computationally intensive financial problems.

He admits that nobody knows when quantum computers will have commercially viable applications, but, he says, it’s important to invest now. Physics Magazine spoke with Ghysels to learn why.

Physicists demonstrate controlled expansion of quantum wavepacket in a levitated nanoparticle

Quantum mechanics theory predicts that, in addition to exhibiting particle-like behavior, particles of all sizes can also have wave-like properties. These properties can be represented using the wave function, a mathematical description of quantum systems that delineates a particle’s movements and the probability that it is in a specific position.

Scientist returns to microbial roots and discovers potential quantum computing advancement

During his Ph.D. at UMass, Nikhil Malvankar was laser-focused on quantum mechanics and the movement of electrons in superconductors. Now a professor at Yale, the native of Mumbai, India, has pivoted toward biology to explain how bacteria breathe deep underground without the aid of oxygen.

Sustainable AI: Physical neural networks exploit light to train more efficiently

Artificial intelligence is now part of our daily lives, with the subsequent pressing need for larger, more complex models. However, the demand for ever-increasing power and computing capacity is rising faster than the performance traditional computers can provide.

To overcome these limitations, research is moving towards innovative technologies such as physical neural networks, analog circuits that directly exploit the laws of physics (properties of light beams, quantum phenomena) to process information. Their potential is at the heart of the study published in the journal Nature. It is the outcome of collaboration between several international institutes, including the Politecnico di Milano, the École Polytechnique Fédérale in Lausanne, Stanford University, the University of Cambridge, and the Max Planck Institute.

The article entitled “Training of Physical Neural Networks” discusses the steps of research on training physical neural networks, carried out with the collaboration of Francesco Morichetti, professor at DEIB—Department of Electronics, Information and Bioengineering, and head of the university’s Photonic Devices Lab.

Quantum calculations provide a sharper image of subatomic stress

Stress is a very real factor in the structure of our universe. Not the kind of stress that students experience when taking a test, but rather the physical stresses that affect everyday objects. Consider the stress that heavy vehicles exert on a bridge as they cross over it—it’s essential that engineers understand and consider this factor when designing new trestles. Or consider the stresses that a star experiences—this internal factor influences everything from its shine to its lifetime.

Something From Nothing — Physicists Mimic the “Impossible” Schwinger Effect

Superfluid helium reveals a manageable analog to the Schwinger effect. It deepens understanding of vortices and quantum tunneling. In 1951, physicist Julian Schwinger proposed that applying a constant electric field to a vacuum could cause electron-positron pairs to emerge spontaneously, a proces

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