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Technology Landscape Review of In-Sensor Photonic Intelligence: From Optical Sensors to Smart Devices

Optical sensors have undergone significant evolution, transitioning from discrete optical microsystems toward sophisticated photonic integrated circuits (PICs) that leverage artificial intelligence (AI) for enhanced functionality. This review systematically explores the integration of optical sensing technologies with AI, charting the advancement from conventional optical microsystems to AI-driven smart devices. First, we examine classical optical sensing methodologies, including refractive index sensing, surface-enhanced infrared absorption (SEIRA), surface-enhanced Raman spectroscopy (SERS), surface plasmon-enhanced chiral spectroscopy, and surface-enhanced fluorescence (SEF) spectroscopy, highlighting their principles, capabilities, and limitations. Subsequently, we analyze the architecture of PIC-based sensing platforms, emphasizing their miniaturization, scalability, and real-time detection performance. This review then introduces the emerging paradigm of in-sensor computing, where AI algorithms are integrated directly within photonic devices, enabling real-time data processing, decision making, and enhanced system autonomy. Finally, we offer a comprehensive outlook on current technological challenges and future research directions, addressing integration complexity, material compatibility, and data processing bottlenecks. This review provides timely insights into the transformative potential of AI-enhanced PIC sensors, setting the stage for future innovations in autonomous, intelligent sensing applications.

This New Treatment Can Adjust to Parkinson’s Symptoms in Real Time

Starting today, people with Parkinson’s disease will have a new treatment option, thanks to U.S. Food and Drug Administration approval of groundbreaking new technology.

The therapy, known as adaptive deep brain stimulation, or aDBS, uses an implanted device that continuously monitors the brain for signs that Parkinson’s symptoms are developing. When it detects specific patterns of brain activity, it delivers precisely calibrated electric pulses to keep symptoms at bay.

The FDA approval covers two treatment algorithms that run on a device made by Medtronic, a medical device company. Both work by monitoring the same part of the brain, called the subthalamic nucleus. But they respond in different ways.

The FDA has approved an adaptive deep brain stimulation (aDBS) treatment for people with with Parkinson’s disease, making this groundbreaking technology available to people nationwide.

The dawn of quantum advantage

Quantum computing is about to enter an important stage — the era of quantum advantage. The first claims of quantum advantage are emerging, and over the next few years, we expect researchers and developers to continue presenting compelling hypotheses for quantum advantages. In turn, the broader community will either disprove these hypotheses with cutting-edge techniques — or the advantage holds.

Put simply, quantum advantage means that a quantum computer can run a computation more accurately, cheaply, or efficiently than a classical computer. Between now and the end of 2026, we predict that the quantum community will have uncovered the first quantum advantages. But there’s more to it than that.

We have arrived already at a place where quantum computing is a useful scientific tool capable of performing computations that even the best exact classical algorithms can’t. We and our partners are already conducting a range of experiments on quantum computers that are competitive with the leading classical approximation methods. At the same time, computing researchers are testing advantage claims with innovative new classical approaches.

New approach allows drone swarms to autonomously navigate complex environments at high speed

Unmanned aerial vehicles (UAVs), commonly known as drones, are now widely used worldwide to tackle various real-world tasks, including filming videos for various purposes, monitoring crops or other environments from above, assessing disaster zones, and conducting military operations. Despite their widespread use, most existing drones either need to be fully or partly operated by human agents.

In addition, many drones are unable to navigate cluttered, crowded or unknown environments without colliding with nearby objects. Those that can navigate these environments typically rely on expensive or bulky components, such as advanced sensors, graphics processing units (GPUs) or .

Researchers at Shanghai Jiao Tong University have recently introduced a new insect-inspired approach that could enable teams of multiple drones to autonomously navigate complex environments while moving at high speed. Their proposed approach, introduced in a paper published in Nature Machine Intelligence, relies on both a deep learning algorithm and core physics principles.

New tool predicts cardiovascular disease risk more accurately

A new risk prediction tool developed by the American Heart Association (AHA) estimated cardiovascular disease (CVD) risk in a diverse patient cohort more accurately than current models, according to a recent study published in Nature Medicine.

The tool, called the Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations which was developed in 2023, could help health care providers more accurately identify patients who have higher CVD risk and enhance preventive care efforts, according to Sadiya Khan, the Magerstadt Professor of Cardiovascular Epidemiology and co-first author of the study.

“Evaluating the new PREVENT equations in a diverse sample of patients is critical to provide primary care providers and cardiologists with further assurance that they can utilize these equations to accurately predict patients’ CVD risk, particularly in vulnerable populations,” said Khan, who is also an associate professor of Medical Social Sciences in the Division of Determinants of Health and of Preventive Medicine in the Division of Epidemiology.

Hunting for quantum-classical crossover in condensed matter problems

The intensive pursuit for quantum advantage in terms of computational complexity has further led to a modernized crucial question of when and how will quantum computers outperform classical computers. The next milestone is undoubtedly the realization of quantum acceleration in practical problems. Here we provide a clear evidence and arguments that the primary target is likely to be condensed matter physics. Our primary contributions are summarized as follows: 1) Proposal of systematic error/runtime analysis on state-of-the-art classical algorithm based on tensor networks; 2) Dedicated and high-resolution analysis on quantum resource performed at the level of executable logical instructions; 3) Clarification of quantum-classical crosspoint for ground-state simulation to be within runtime of hours using only a few hundreds of thousand physical qubits for 2d Heisenberg and 2d Fermi-Hubbard models, assuming that logical qubits are encoded via the surface code with the physical error rate of p = 10–3. To our knowledge, we argue that condensed matter problems offer the earliest platform for demonstration of practical quantum advantage that is order-of-magnitude more feasible than ever known candidates, in terms of both qubit counts and total runtime.

Yoshioka, N., Okubo, T., Suzuki, Y. et al. Hunting for quantum-classical crossover in condensed matter problems. npj Quantum Inf 10, 45 (2024). https://doi.org/10.1038/s41534-024-00839-4

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Human-AI teamwork uncovers hidden magnetic states in quantum spin liquids

At the forefront of discovery, where cutting-edge scientific questions are tackled, we often don’t have much data. Conversely, successful machine learning (ML) tends to rely on large, high-quality data sets for training. So how can researchers harness AI effectively to support their investigations?

In Physical Review Research, scientists describe an approach for working with ML to tackle complex questions in condensed matter physics. Their method tackles hard problems which were previously unsolvable by physicist simulations or by ML algorithms alone.

The researchers were interested in frustrated magnets— in which competing interactions lead to exotic magnetic properties. Studying these materials has helped to advance our understanding of quantum computing and shed light on . However, frustrated magnets are very difficult to simulate, because of the constraints arising from the way magnetic ions interact.