Innovative quantum neuroelectronic devices mimic key functions of brain synapses, demonstrating promise for reducing effects of age-related cognitive decline.
Many of today’s quantum devices rely on collections of qubits, also called spins. These quantum bits have only two energy levels, the ‘0’ and the ‘1’. However, unlike classical bits, qubits can exist in superpositions, meaning they can simultaneously be in a combination of the ‘0’ and ‘1’ states. Spins in real devices also interact with light and vibrations known as bosons, greatly complicating calculations.
In a new publication in Physical Review Letters (“Fast quantum state preparation and bath dynamics using non-Gaussian variational Ansatz and quantum optimal control”), researchers in Amsterdam demonstrate a way to describe spin-boson systems and use this to efficiently configure quantum devices in a desired state.
Quantum devices use the quirky behaviour of quantum particles to perform tasks that go beyond what ‘classical’ machines can do, including quantum computing, simulation, quantum sensing, quantum communication and quantum metrology. These devices can take many forms, such as a collection of superconducting circuits, or a lattice of atoms or ions held in place by lasers or electric fields.
Researchers at Rensselaer Polytechnic Institute have fabricated a device no wider than a human hair that will help physicists investigate the fundamental nature of matter and light. Their findings, published in the journal Nature Nanotechnology (“Topological valley Hall polariton condensation”), could also support the development of more efficient lasers, which are used in fields ranging from medicine to manufacturing.
The device is made of a special kind of material called a photonic topological insulator. A photonic topological insulator can guide photons, the wave-like particles that make up light, to interfaces specifically designed within the material while also preventing these particles from scattering through the material itself.
Because of this property, topological insulators can make many photons coherently act like one photon. The devices can also be used as topological “quantum simulators,” miniature laboratories where researchers can study quantum phenomenon, the physical laws that govern matter at very small scales.
A secure method for cloud-based quantum computing harnesses the power of quantum physics to keep data confidential.
Progress in quantum technology has been swift, but we still are far from the day when everyone will have a quantum computer in their house or at their business. The early stages of quantum computing will likely rely on a quantum version of the “cloud,” where users send data and computing tasks to a state-of-the-art quantum machine hosted by Google, IBM, or another company. But is that approach secure? It can be, thanks to the impenetrable secrecy of quantum-based protocols. A recent experiment demonstrates a version of “blind quantum computing” using trapped ions [1]. The protocol is scalable, meaning it offers potential to be incorporated into larger and larger quantum computing systems.
Quantum computers have the potential to be game changers in computationally intensive tasks such as drug discovery and material design. In these highly competitive sectors, there would be concerns about using a cloud-based quantum computer. “A company searching for a new wonder drug or for a high-performance battery material wouldn’t want to reveal confidential secrets,” explains Peter Drmota of the University of Oxford. However, it has been shown—in theory—that one can perform computations on a remote quantum computer while hiding the data and the operations done on such data. “Blind quantum computing could give a client confidence to use whoever’s quantum computer,” Drmota says.
In a new publication in Physical Review Letters, researchers in Amsterdam demonstrate a way to describe spin-boson systems and use this to efficiently configure quantum devices in a desired state.
Quantum devices use the quirky behavior of quantum particles to perform tasks that go beyond what “classical” machines can do, including quantum computing, simulation, sensing, communication and metrology. These devices can take many forms, such as a collection of superconducting circuits, or a lattice of atoms or ions held in place by lasers or electric fields.
Regardless of their physical realization, quantum devices are typically described in simplified terms as a collection of interacting two-level quantum bits or spins. However, these spins also interact with other things in their surroundings, such as light in superconducting circuits or oscillations in the lattice of atoms or ions. Particles of light (photons) and vibrational modes of a lattice (phonons) are examples of bosons.
UNSW quantum engineers have developed a new amplifier that could help other scientists search for elusive dark matter particles.
In a study published in Nature Materials, scientists from the University of California, Irvine describe a new method to make very thin crystals of the element bismuth – a process that may aid in making the manufacturing of cheap flexible electronics an everyday reality.
“Bismuth has fascinated scientists for over a hundred years due to its low melting point and unique electronic properties,” said Javier Sanchez-Yamagishi, assistant professor of physics & astronomy at UC Irvine and a co-author of the study. “We developed a new method to make very thin crystals of materials such as bismuth, and in the process reveal hidden electronic behaviors of the metal’s surfaces.”
The bismuth sheets the team made are only a few nanometers thick. Sanchez-Yamagishi explained how theorists have predicted that bismuth contains special electronic states allowing it to become magnetic when electricity flows through it – something essential for quantum electronic devices based on the magnetic spin of electrons.
Researchers have developed a machine learning model that generates quantum circuits from text descriptions, similar to how models like Stable Diffusion create images. This method, improves the efficiency and adaptability of quantum computing.
One of the most important recent developments in Machine Learning (ML) is generative models such as diffusion models. These include Stable Diffusion and Dall.e, which are revolutionizing the field of image generation. These models are able to produce high-quality images based on text descriptions.
“Our new model for programming quantum computers does the same but, instead of generating images, it generates quantum circuits based on the text description of the quantum operation to be performed,” explains Gorka Muñoz-Gil from the Department of Theoretical Physics of the University of Innsbruck, Austria.
Researchers at Rensselaer Polytechnic Institute have fabricated a device no wider than a human hair that will help physicists investigate the fundamental nature of matter and light. Their findings, published in the journal Nature Nanotechnology, could also support the development of more efficient lasers, which are used in fields ranging from medicine to manufacturing.
Physicists call the dark energy that drives the universe “the cosmological constant.” Now the largest map of the cosmos to date hints that this mysterious energy has been changing over billions of years.
