A joint research team has developed an innovative quantum-classical computing approach to design photochromic materials—light-sensitive compounds—offering a powerful tool to accelerate material discovery. Their findings were published in Intelligent Computing.
Building on their previous work in the same journal, the researchers introduced a computational-basis variational quantum deflation method as the foundation of their approach.
To validate its effectiveness, the team conducted a case study in photopharmacology, screening 4,096 diarylethene derivatives. They identified five promising candidates that exhibited two critical properties: large maximum absorbance wavelengths and high oscillator strengths. These characteristics are crucial for applications such as light-controlled drug delivery in photopharmacology.
Nanoparticle researchers spend most of their time on one thing: counting and measuring nanoparticles. Each step of the way, they have to check their results. They usually do this by analyzing microscopic images of hundreds of nanoparticles packed tightly together. Counting and measuring them takes a long time, but this work is essential for completing the statistical analyses required for conducting the next, suitably optimized nanoparticle synthesis.
Alexander Wittemann is a professor of colloid chemistry at the University of Konstanz. He and his team repeat this process every day. “When I worked on my doctoral thesis, we used a large particle counting machine for these measurements. It was like a cash register, and, at the time, I was really happy when I could measure three hundred nanoparticles a day,” Wittemann remembers.
However, reliable statistics require thousands of measurements for each sample. Today, the increased use of computer technology means the process can move much more rapidly. At the same time, the automated methods are very prone to errors, and many measurements still need to be conducted, or at least double-checked, by the researchers themselves.
AI, Deep Dive, spacetime inertia, unified energy framework, gravity, dark matter, dark energy, black holes, emergent gravity, energy inertia, mass-energy interactions, missing mass problem, cosmic expansion, event horizon mechanics, Einstein’s General Relativity, spacetime curvature, galactic rotation curves, quantum field theory, spacetime as energy, energy resistance, inertial effects, graviton alternative, energy density distribution, inverse-square law, gravitational lensing, galactic halos, high-energy cosmic regions, X-ray emissions, electromagnetic fields, cosmological constant, accelerating universe, large-scale inertia, spacetime resistance, event horizon physics, singularity alternatives, James Webb Space Telescope, early galaxy formation, modified gravity, inertia-driven cosmic expansion, energy saturation point, observational cosmology, new physics, alternative gravity models, astrophysical testing, theoretical physics, unification of forces, experimental validation, fundamental physics revolution, black hole structure, cosmic energy fields, energy gradient effects, resistance in spacetime, extreme energy zones, black hole event horizons, quantum gravity, astrophysical predictions, future space observations, high-energy astrophysics, cosmic structure formation, inertia-based galaxy evolution, spacetime fluid dynamics, reinterpreting physics, mass-energy equivalence.
Description: In this deep dive into the nature of gravity, dark matter, and dark energy, we explore a groundbreaking hypothesis that could revolutionize our understanding of the universe. What if gravity is not a fundamental force but an emergent property of spacetime inertia? This novel framework, proposed by Dave Champagne, reinterprets the role of energy and inertia within the fabric of the cosmos, suggesting that mass-energy interactions alone can account for gravitational effects—eliminating the need for exotic matter or hypothetical dark energy forces.
We begin by examining the historical context of gravity, from Newton’s classical mechanics to Einstein’s General Relativity. While these theories describe gravitational effects with incredible accuracy, they still leave major mysteries unsolved, such as the unexplained motions of galaxies and the accelerating expansion of the universe. Traditionally, these anomalies have been attributed to dark matter and dark energy—hypothetical substances that have yet to be directly observed. But what if there’s another explanation?
By treating spacetime itself as an energy field with intrinsic inertia, we propose that gravitational effects arise naturally from the resistance of this energy to changes in motion. Just as mass resists acceleration due to inertia, energy may also exhibit resistance at cosmic scales, leading to effects that mimic gravity, dark matter, and dark energy. This perspective offers a fresh way to interpret the missing mass problem, suggesting that the high-energy environments surrounding galaxies create inertia effects that explain their rotational speeds—without requiring an invisible mass component.
We explore how this framework extends to cosmic expansion. Instead of postulating an unknown repulsive force (dark energy), spacetime inertia may drive the acceleration of the universe as a natural consequence of energy distribution at vast scales. Could this be an alternative to Einstein’s cosmological constant? We analyze how large-scale resistance effects could account for the observations of an accelerating cosmos.
Quantum computing is an alternative computing paradigm that exploits the principles of quantum mechanics to enable intrinsic and massive parallelism in computation. This potential quantum advantage could have significant implications for the design of future computational intelligence systems, where the increasing availability of data will necessitate ever-increasing computational power. However, in the current NISQ (Noisy Intermediate-Scale Quantum) era, quantum computers face limitations in qubit quality, coherence, and gate fidelity. Computational intelligence can play a crucial role in optimizing and mitigating these limitations by enhancing error correction, guiding quantum circuit design, and developing hybrid classical-quantum algorithms that maximize the performance of NISQ devices. This webinar aims to explore the intersection of quantum computing and computational intelligence, focusing on efficient strategies for using NISQ-era devices in the design of quantum-based computational intelligence systems.
Speaker Biography: Prof. Giovanni Acampora is a Professor of Artificial Intelligence and Quantum Computing at the Department of Physics “Ettore Pancini,” University of Naples Federico II, Italy. He earned his M.Sc. (cum laude) and Ph.D. in Computer Science from the University of Salerno. His research focuses on computational intelligence and quantum computing. He is Chair of the IEEE-SA 1855 Working Group, Founder and Editor-in-Chief of Quantum Machine Intelligence. Acampora has received multiple awards, including the IEEE-SA Emerging Technology Award, IBM Quantum Experience Award and Fujitsu Quantum Challenge Award for his contributions to computational intelligence and quantum AI.
When world-leading teams join forces, new findings are bound to be made. This is what happened when quantum physicists from the Physikalisch-Technische Bundesanstalt (PTB) and the Max Planck Institute for Nuclear Physics (MPIK) in Heidelberg combined atomic and nuclear physics with unprecedented accuracy using two different methods of measurement.
Together with new calculations of the structure of atomic nuclei, theoretical physicists from the Technical University of Darmstadt and Leibniz University Hannover were able to show that measurements on the electron shell of an atom can provide information about the deformation of the atomic nucleus. At the same time, the precision measurements have set new limits regarding the strength of a potential dark force between neutrons and electrons.
The results have been published in the current issue of the journal Physical Review Letters.
A team of researchers at the University of Konstanz has succeeded in adapting an artificial intelligence (AI) system to reliably assist with making nanoparticle measurements which speeds up the research process significantly.
The findings have been published in Scientific Reports (“Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model”).
Nanoparticle researchers spend most of their time on one thing: counting and measuring nanoparticles. Each step of the way, they have to check their results. They usually do this by analyzing microscopic images of hundreds of nanoparticles packed tightly together. Counting and measuring them takes a long time, but this work is essential for completing the statistical analyses required for conducting the next, suitably optimized nanoparticle synthesis.
David Furman, an immunologist and data scientist at the Buck Institute for Research on Aging and Stanford University, uses artificial intelligence to parse big data to identify interventions for healthy aging.
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David Furman uses computational power, collaborations, and cosmic inspiration to tease apart the role of the immune system in aging.
Paleontologists aren’t easily deterred by evolutionary dead ends or a sparse fossil record. But in the last few years, they’ve developed a new trick for turning back time and studying prehistoric animals: building experimental robotic models of them. In the absence of a living specimen, scientists say, an ambling, flying, swimming, or slithering automaton is the next best thing for studying the behavior of extinct organisms. Learning more about how they moved can in turn shed light on aspects of their lives, such as their historic ranges and feeding habits.
Digital models already do a decent job of predicting animal biomechanics, but modeling complex environments like uneven surfaces, loose terrain, and turbulent water is challenging. With a robot, scientists can simply sit back and watch its behavior in different environments. “We can look at its performance without having to think of every detail, [as] in the simulation,” says John Nyakatura, an evolutionary biologist at Humboldt University in Berlin.
