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Category: robotics/AI – Page 115

Universal logical quantum photonic neural network processor via cavity-assisted interactions

Encoding quantum information within bosonic modes offers a promising direction for hardware-efficient and fault-tolerant quantum information processing. However, achieving high-fidelity universal control over bosonic encodings using native photonic hardware remains a significant challenge. We establish a quantum control framework to prepare and perform universal logical operations on arbitrary multimode multi-photon states using a quantum photonic neural network. Central to our approach is the optical nonlinearity, which is realized through strong light-matter interaction with a three-level Λ atomic system. The dynamics of this passive interaction are asymptotically confined to the single-mode subspace, enabling the construction of deterministic entangling gates and overcoming limitations faced by many nonlinear optical mechanisms. Using this nonlinearity as the element-wise activation function, we show that the proposed architecture is able to deterministically prepare a wide array of multimode multi-photon states, including essential resource states. We demonstrate universal code-agnostic control of bosonic encodings by preparing and performing logical operations on symmetry-protected error-correcting codes. Our architecture is not constrained by symmetries imposed by evolution under a system Hamiltonian such as purely χ and χ processes, and is naturally suited to implement non-transversal gates on photonic logical qubits. Additionally, we propose an error-correction scheme based on non-demolition measurements that is facilitated by the optical nonlinearity as a building block. Our results pave the way for near-term quantum photonic processors that enable error-corrected quantum computation, and can be achieved using present-day integrated photonic hardware.

Basani, J.R., Niu, M.Y. & Waks, E. Universal logical quantum photonic neural network processor via cavity-assisted interactions. npj Quantum Inf 11, 142 (2025). https://doi.org/10.1038/s41534-025-01096-9

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Social experiments assess ‘artificial’ altruism displayed by large language models

Altruism, the tendency to behave in ways that benefit others even if it comes at a cost to oneself, is a valuable human quality that can facilitate cooperation with others and promote meaningful social relationships. Behavioral scientists have been studying human altruism for decades, typically using tasks or games rooted in economics.

Two researchers based at Willamette University and the Laureate Institute for Brain Research recently set out to explore the possibility that (LLMs), such as the model underpinning the functioning of the conversational platform ChatGPT, can simulate the observed in humans. Their findings, published in Nature Human Behavior, suggest that LLMs do in fact simulate in specific social experiments, offering a possible explanation for this.

“My paper with Nick Obradovich emerged from my longstanding interest in altruism and cooperation,” Tim Johnson, co-author of the paper, told Tech Xplore. “Over the course of my career, I have used computer simulation to study models in which agents in a population interact with each other and can incur a cost to benefit another party. In parallel, I have studied how people make decisions about altruism and cooperation in laboratory settings.

Scorpion-inspired pressure sensors let robots feel their surroundings

Nature, the master engineer, is coming to our rescue again. Inspired by scorpions, scientists have created new pressure sensors that are both highly sensitive and able to work across a wide variety of pressures.

Pressure sensors are key components in an array of applications, from and industrial control systems to robotics and human-machine interfaces. Silicon-based piezoresistive sensors are among the most common types used today, but they have a significant limitation. They can’t be super sensitive to changes and work well across a range of pressures at the same time. Often, you have to choose one over the other.

Smart microrobots learn to communicate and collaborate in water

In a major step toward intelligent and collaborative microrobotic systems, researchers at the Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN) at Chemnitz University of Technology have developed a new generation of autonomous microrobots—termed smartlets—that can communicate, respond, and work together in aqueous environments.

These tiny devices, each just a millimeter in size, are fully integrated with onboard electronics, sensors, actuators, and . They are able to receive and transmit optical signals, respond to stimuli with motion, and exchange information with other microrobots in their vicinity.

The findings are published in Science Robotics, in a paper titled “Si chiplet–controlled 3D modular microrobots with smart communication in natural aqueous environments.”

Physics-inspired computer architecture solves complex optimization problems

A line of engineering research seeks to develop computers that can tackle a class of challenges called combinatorial optimization problems. These are common in real-world applications such as arranging telecommunications, scheduling, and travel routing to maximize efficiency.

Unfortunately, today’s technologies run into limits for how much processing power can be packed into a computer chip, while training artificial-intelligence models demands tremendous amounts of energy.

Researchers at UCLA and UC Riverside have demonstrated a new approach that overcomes these hurdles to solve some of the most difficult optimization problems. The team designed a system that processes information using a network of oscillators, components that move back and forth at certain frequencies, rather than representing all data digitally. This type of computer architecture, called an Ising machine, has special power for parallel computing, which makes numerous, complex calculations simultaneously. When the oscillators are in sync, the optimization problem is solved.

AI can find cancer pathologists miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to find subtle tissue changes that allow the cancer to be detected long before it becomes visible to the human eye.

Previous research has demonstrated that AI is able to detect tissue changes indicative of cancer. In the current study, published in Scientific Reports, the researchers show that AI can also find cancers missed by pathologists.

“The study has been nicknamed the ‘missed study’, as the goal of finding the cancer was ‘missed’ by the pathologists. We have now shown that with the help of AI, it is possible to find signs of prostate cancer that were not observed by pathologists in more than 80 per cent of samples from men who later developed cancer,” says Carolina Wählby, who led the AI development in the study.

“When we looked at the patterns that the AI ranked as informative, we saw changes in the tissue surrounding the glands in the prostate”, says Carolina Wählby. Photo: Mikael Wallerstedt.

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