We analyse five potential trajectories for the development of quantum computing, based on current technical achievements and fundamental challenges. We draw from recent experimental results including Google’s Willow processor achieving below-threshold error correction. We also consider IBM’s quantum roadmap and emerging classical algorithms that challenge quantum supremacy. Additionally, our evaluation includes the bifurcation between NISQ and fault-tolerant approaches.
Astronomers have discovered what may be a massive star exploding while trying to swallow a black hole companion, offering an explanation for one of the strangest stellar explosions ever seen.
The discovery was made by a team led by the Center for Astrophysics | Harvard & Smithsonian (CfA) and the Massachusetts Institute of Technology (MIT) as part of the Young Supernova Experiment. The results are published in The Astrophysical Journal.
The blast, named SN 2023zkd, was first discovered in July 2023 by the Zwicky Transient Facility. A new AI algorithm designed to scan for unusual explosions in real time first detected the explosion, and that early alert allowed astronomers to begin follow-up observations immediately—an essential step in capturing the full story of the explosion. By the time the explosion was over, it had been observed by a large set of telescopes, both on the ground and from space.
In everyday life, continuously doing work on a system is found to heat it up. Rubbing your hands together warms them. Hammering a piece of metal makes it hot. Even without knowing the equations, we learn from experience: driving any system, whether by stirring, pressing, or striking, leads to a rise in the system’s temperature.
The same expectation holds for microscopic quantum systems: when we continuously excite a many-particle system, especially one with strong particle-particle interactions, we expect it to absorb energy and to heat up. But is this always the case, in particular at the quantum level?
No, says an experiment carried out by a team from Hanns-Christoph Nägerl’s group at the Department of Experimental Physics of the University of Innsbruck. The research has been published in Science.
A study published in Nature Biotechnology reveals a powerful new use for artificial intelligence: designing small, drug-like molecules that can stick to and break down harmful proteins in the body — even when scientists don’t know what those proteins look like. The breakthrough could lead to new treatments for diseases that have long resisted traditional drug development, including certain cancers, brain disorders, and viral infections.
The study was published on August 13, 2025 by a multi-institutional team of researchers from McMaster University, Duke University, and Cornell University. The AI tool, called PepMLM, is based on an algorithm originally built to understand human language and used in chatbots, but was trained to understand the “language” of proteins.
In 2024, the Nobel Prize in Chemistry was awarded to researchers at Google DeepMind for developing AlphaFold, an AI system that predicts the 3D structure of proteins – a major advance in drug discovery. But many disease-related proteins, including those involved in cancer and neurodegeneration, don’t have stable structures. That’s where PepMLM takes a different approach – instead of relying on structure, the tool uses only the protein’s sequence to design peptide drugs. This makes it possible to target a much broader range of disease proteins, including those that were previously considered “undruggable.”
Researchers have demonstrated that brain cells learn faster and carry out complex networking more effectively than machine learning by comparing how both a Synthetic Biological Intelligence (SBI) system known as “DishBrain” and state-of-the-art RL (reinforcement learning) algorithms react to certain stimuli.
The study, “Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning,” published in Cyborg and Bionic Systems, is the first known of its kind.
The research was led by Cortical Labs, the Melbourne-based startup which created the world’s first commercial biological computer, the CL1. The CL1, through which the research was conducted, fuses lab-cultivated neurons from human stem cells with hard silicon to create a more advanced and sustainable form of AI, known as SBI.
Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate predictions by analyzing large amounts of data. These networks are structured in layers, each of which transforms input data into ‘features’ that guide the analysis of the next layer.
The process through which DNNs learn features has been the topic of numerous research studies and is ultimately the key to these models’ good performance on a variety of tasks. Recently, some computer scientists have started exploring the possibility of modeling feature learning in DNNs using frameworks and approaches rooted in physics.
Researchers at the University of Basel and the University of Science and Technology of China discovered a phase diagram, a graph resembling those used in thermodynamics to delineate liquid, gaseous and solid phases of water, that represents how DNNs learn features under various conditions. Their paper, published in Physical Review Letters, models a DNN as a spring-block chain, a simple mechanical system that is often used to study interactions between linear (spring) and nonlinear (friction) forces.
Humanoid robots, robots with a human-like body structure, have so far been primarily tested on manual tasks that entail supporting humans in their daily activities, such as carrying objects, collecting samples in hazardous environments, supporting older adults or acting as physical therapy assistants. In contrast, their potential for completing expressive physical tasks rooted in creative disciplines, such as playing an instrument or participating in performance arts, remains largely unexplored.
Researchers at SUPSI, IDSIA and Politecnico di Milano recently introduced Robot Drummer, a new humanoid robot that can play the drums both accurately and expressively, supported by a reinforcement learning algorithm. This robot, presented in a paper published on the arXiv preprint server, was found to gradually acquire human-like behaviors, including movements that are often performed by drummers.
“The idea for Robot Drummer actually emerged from a spontaneous conversation over coffee with my co-author, Loris Roveda,” Asad Ali Shahid, first author of the paper, told Tech Xplore. “We were discussing how humanoid robots have become increasingly capable at a wide range of tasks, but rarely engage in creative and expressive domains. That raised a fascinating question: what if a humanoid robot could take on a creative role, like performing music? Drumming seemed like a perfect frontier, as it’s rhythmic, physical, and requires rapid coordination across limbs.”
In advanced multi-static radar (MSR), multidimensional information from target echo signals is collected by different receivers to enable precise localization using various algorithms. Owing to its efficient target localization and tracking capability, MSR has found wide applications in sensing, military operations, aviation, and aerospace. Multi-static nature of MSR also makes it difficult to counter. Here, we propose an anti-radar methodology based on space-time-coding metasurface (STCM) to counter MSR. By designing the physical characteristics of STCM and developing adaptive and robust electronic countermeasure (ECM) control strategies, we realize a cost-effective, miniaturized and low-complexity ECM system with the flexible controlling capabilities. Under non-cooperative and dynamic ECM scenarios, the proposed method shows exceptional concealment and deception performance. To validate the methodology, we develop a prototype of the STCM-based anti-MSR system and successfully demonstrate its ability to neutralize various MSR technologies. The proposed method is expected to find practical applications in the anti-MSR scenarios.
This study proposes an anti-radar methodology based on space-time-coding metasurface to counter multi-static radar, which enables a cost-effective, miniaturized, and low complexity electronic countermeasure system.