Lifeboat Foundation

Researchers at Chalmers University of Technology, Gothenburg, Sweden, have developed a novel type of thermometer that can simply and quickly measure temperatures during quantum calculations with extremely high accuracy. The breakthrough provides a benchmarking tool for quantum computing of great value—and opens up for experiments in the exciting field of quantum thermodynamics.

Key components in quantum computers are coaxial cables and waveguides—structures that guide waveforms and act as the vital connection between the quantum processor and the classical electronics that control it. Microwave pulses travel along the waveguides to the quantum processor, and are cooled down to extremely low temperatures along the way. The waveguide also attenuates and filters the pulses, enabling the extremely sensitive quantum computer to work with stable quantum states.

In order to maximize control over this mechanism, the researchers need to be sure that these waveguides are not carrying noise due to thermal motion of electrons on top of the pulses that they send. In other words, they have to measure the temperature of the electromagnetic fields at the cold end of the microwave waveguides, the point where the controlling pulses are delivered to the computer’s qubits. Working at the lowest possible temperature minimizes the risk of introducing errors in the qubits.

Big Blue has made Qiskit Metal generally available, to let “anyone” try their hand at quantum hardware design.

“Previously reported detection of plant biomagnetism, which established the existence of measurable magnetic activity in the plant kingdom, was carried out using superconducting-quantum-interference-device (SQUID) magnetometers1, 5, 16. Atomic magnetometers are arguably more attractive for biological applications, since, unlike SQUIDs34, 35, they are non-cryogenic and can be miniaturized to optimize spatial resolution of measured biological features14, 15, 36. In the future, the SNR of magnetic measurements in plants will benefit from optimizing the low-frequency stability and sensitivity of atomic magnetometers. Just as noninvasive magnetic techniques have become essential tools for medical diagnostics of the human brain and body, this noninvasive technique could also be useful in the future for crop-plant diagnostics—by measuring the electromagnetic response of plants facing such challenges as sudden temperature change, herbivore attack, and chemical exposure.”

Upon stimulation, plants elicit electrical signals that can travel within a cellular network analogous to the animal nervous system. It is well-known that in the human brain, voltage changes in certain regions result from concerted electrical activity which, in the form of action potentials (APs), travels within nerve-cell arrays. Electro-and magnetophysiological techniques like electroencephalography, magnetoencephalography, and magnetic resonance imaging are used to record this activity and to diagnose disorders. Here we demonstrate that APs in a multicellular plant system produce measurable magnetic fields. Using atomic optically pumped magnetometers, biomagnetism associated with electrical activity in the carnivorous Venus flytrap, Dionaea muscipula, was recorded. Action potentials were induced by heat stimulation and detected both electrically and magnetically.

Pioneering experiment could help design energy-efficient materials.

In a groundbreaking study published in Physical Review Research, a group of University of Chicago scientists announced they were able to turn IBM’s largest quantum computer into a quantum material itself.

They programmed the computer such that it turned into a type of quantum material called an exciton condensate, which has only recently been shown to exist. Such condensates have been identified for their potential in future technology, because they can conduct energy with almost zero loss.

By Chuck Brooks

The world of computing has witnessed seismic advancements since the invention of the electronic calculator in the 1960s. The past few years in information processing have been especially transformational. What were once thought of as science fiction fantasies are now technological realties. Classical computing has become more exponentially faster and more capable and our enabling devices smaller and more adaptable.

It’s another step on the road to developing quantum information processing applications: A key experiment succeeded in going beyond the previously defined limits for photon applications. Anahita Khodadad Kashi and Prof. Dr. Michael Kues from the Institute of Photonics and the Cluster of Excellence PhoenixD at Leibniz University Hannover (Germany) have demonstrated a novel interference effect. The scientists have thus shown that new color-coded photonic networks can be tapped, and the number of photons involved can be scaled. “This discovery could enable new benchmarks in quantum communication, computational operations of quantum computers as well as quantum measurement techniques and is feasible with existing optical telecommunication infrastructure,” says Kues.

The decisive experiment was successfully performed in the newly established Quantum Photonics Laboratory (QPL) of the Institute of Photonics and the Hannover Centre for Optical Technologies at Leibniz University Hannover. Anahita Khodadad Kashi succeeded in quantum-mechanically interfering independently generated pure photons with different colors, i.e., frequencies. Khodadad Kashi detected a so-called Hong-Ou-Mandel effect.

Hong-Ou-Mandel interference is a fundamental effect of quantum optics that forms the basis for many quantum information processing applications—from quantum computing to quantum metrology. The effect describes how two photons behave when they collide on a spatial beam splitter and explains the phenomenon of quantum mechanical interference.

Many machine learning algorithms on quantum computers suffer from the dreaded “barren plateau” of unsolvability, where they run into dead ends on optimization problems. This challenge had been relatively unstudied—until now. Rigorous theoretical work has established theorems that guarantee whether a given machine learning algorithm will work as it scales up on larger computers.

“The work solves a key problem of useability for quantum machine learning. We rigorously proved the conditions under which certain architectures of variational quantum algorithms will or will not have barren plateaus as they are scaled up,” said Marco Cerezo, lead author on the paper published in Nature Communications today by a Los Alamos National Laboratory team. Cerezo is a post doc researching quantum information theory at Los Alamos. “With our theorems, you can guarantee that the architecture will be scalable to quantum computers with a large number of qubits.”

“Usually the approach has been to run an optimization and see if it works, and that was leading to fatigue among researchers in the field,” said Patrick Coles, a coauthor of the study. Establishing mathematical theorems and deriving first principles takes the guesswork out of developing algorithms.

Nodes known as Alice, Bob and Charlie share entangled state across two different labs.

Graph representations can solve complex problems in natural science, as patterns of connectivity can give rise to a magnitude of emergent phenomena. Graph-based approaches are specifically important during quantum communication, alongside quantum search algorithms in highly branched quantum networks. In a new report now published on Science Advances, Max Ehrhardt and a team of scientists in physics, experimental physics and quantum science in Germany introduced a hitherto unidentified paradigm to directly realize excitation dynamics associated with three-dimensional networks. To accomplish this, they explored the hybrid action of space and polarization degrees of freedom of photon pairs inside complex waveguide circuits. The team experimentally explored multiparticle quantum walks on complex and highly connected graphs as testbeds to pave the way to explore the potential applications of fermionic dynamics in integrated photonics.

Complex networks

Complex networks can occur across diverse fields of science, ranging from biological signaling pathways and biochemical molecules to exhibit efficient energy transport to neuromorphic circuits across to social interactions across the internet. Such structures are typically modeled using graphs whose complexity relies on the number of nodes and linkage patterns between them. The physical representation of a graph is limited by their requirement for arrangement in three-dimensional (3D) space. The human brain is a marked example of scaling behavior that is unfavorable for physical simulation due to its staggering number of 80 billion neurons, dwarfed by 100 trillion synapses that allow the flow of signals between them. Despite the number of comparably miniscule volume of nodes, discrete quantum systems faced a number of challenges owing to complex network topologies, efficient multipartite quantum communications and search algorithms.