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Category: information science

Adverse impact of acute Toxoplasma gondii infection on human spermatozoa

Ultimately, QIML proves that we don’t need a fully fault-tolerant quantum computer to see results. By using quantum processors to learn the complex “rules” of chaos, we can give classical computers the boost they need to make reliable, long-term predictions about the most turbulent environments in the natural world.

Modeling high-dimensional dynamical systems remains one of the most persistent challenges in computational science. Partial differential equations (PDEs) provide the mathematical backbone for describing a wide range of nonlinear, spatiotemporal processes across scientific and engineering domains (1–3). However, high-dimensional systems are notoriously sensitive to initial conditions and the floating-point numbers used to compute them (4–7), making it highly challenging to extract stable, predictive models from data. Modern machine learning (ML) techniques often struggle in this regime: While they may fit short-term trajectories, they fail to learn the invariant statistical properties that govern long-term system behavior. These challenges are compounded in high-dimensional settings, where data are highly nonlinear and contain complex multiscale spatiotemporal correlations.

ML has seen transformative success in domains such as large language models (8, 9), computer vision (10, 11), and weather forecasting (12–15), and it is increasingly being adopted in scientific disciplines under the umbrella of scientific ML (16). In fluid mechanics, in particular, ML has been used to model complex flow phenomena, including wall modeling (17, 18), subgrid-scale turbulence (19, 20), and direct flow field generation (21, 22). Physics-informed neural networks (23, 24) attempt to inject domain knowledge into the learning process, yet even these models struggle with the long-term stability and generalization issues that high-dimensional dynamical systems demand. To address this, generative models such as generative adversarial networks (25) and operator-learning architectures such as DeepONet (26) and Fourier neural operators (FNO) (27) have been proposed. While neural operators offer discretization invariance and strong representational power for PDE-based systems, they still suffer from error accumulation and prediction divergence over long horizons, particularly in turbulent and other chaotic regimes (28, 29). Recent work, such as DySLIM (30), enhances stability by leveraging invariant statistical measures. However, these methods depend on estimating such measures from trajectory samples, which can be computationally intensive and inaccurate in all forms of chaotic systems, especially in high-dimensional cases. These limitations have prompted exploration into alternative computational paradigms. Quantum machine learning (QML) has emerged as a possible candidate due to its ability to represent and manipulate high-dimensional probability distributions in Hilbert space (31). Quantum circuits can exploit entanglement and interference to express rich, nonlocal statistical dependencies using fewer parameters than their promising counterparts, which makes them well suited for capturing invariant measures in high-dimensional dynamical systems, where long-range correlations and multimodal distributions frequently arise (32). QML and quantum-inspired ML have already demonstrated potential in fields such as quantum chemistry (33, 34), combinatorial optimization (35, 36), and generative modeling (37, 38). However, the field is constrained on two fronts: Fully quantum approaches are limited by noisy intermediate-scale quantum (NISQ) hardware noise and scalability (39), while quantum-inspired algorithms, being classical simulations, cannot natively leverage crucial quantum effects such as entanglement to efficiently represent the complex, nonlocal correlations found in such systems. These challenges limit the standalone utility of QML in scientific applications today. Instead, hybrid quantum-classical models provide a promising compromise, where quantum submodules work together with classical learning pipelines to improve expressivity, data efficiency, and physical fidelity. In quantum chemistry, this hybrid paradigm has proven feasible, notably through quantum mechanical/molecular mechanical coupling (40, 41), where classical force fields are augmented with quantum corrections. Within such frameworks, techniques such as quantum-selected configuration interaction (42) have been used to enhance accuracy while keeping the quantum resource requirements tractable. In the broader landscape of quantum computational fluid dynamics, progress has been made toward developing full quantum solvers for nonlinear PDEs. Recent works by Liu et al. (43) and Sanavio et al. (44, 45) have successfully applied Carleman linearization to the lattice Boltzmann equation, offering a promising pathway for simulating fluid flows at moderate Reynolds numbers. These approaches, typically using algorithms such as Harrow-Hassidim-Lloyd (HHL) (46), promise exponential speedups but generally necessitate deep circuits and fault-tolerant hardware.

Quantum-enhanced machine learning (QEML) combines the representational richness of quantum models with the scalability of classical learning. By leveraging uniquely quantum properties such as superposition and entanglement, QEML can explore richer feature spaces and capture complex correlations that are challenging for purely classical models. Recent successes in quantum-enhanced drug discovery (37), where hybrid quantum-classical generative models have produced experimentally validated candidates rivaling state-of-the-art classical methods, demonstrate the practical potential of QEML even before full quantum advantage is achieved. Despite these strengths, practical barriers remain. QEML pipelines require repeated quantum-classical communication during training and rely on costly quantum data-embedding and measurement steps, which slow computation and limit accessibility across research institutions.

Quantum-centric supercomputing simulates 12,635-atom protein

The scale of chemistry simulations with quantum computing has increased dramatically in just the last few months. In the latest milestone for the field, researchers from Cleveland Clinic, RIKEN, and IBM used a quantum-centric supercomputing (QCSC) framework to calculate the electronic structure of a pair of large protein-ligand complexes, reaching a scale of 12,635 atoms in the largest simulation.

The molecules were T4-Lysozyme, a protein from a family of proteins involved in the immune system degradation of peptidoglycans in bacterial membranes, and Trypsin, produced in the pancreas and used in digestion. The team simulated these proteins binding to molecules they interact with in nature and immersed in a liquid water solution, at scales of 11,608 atoms and 12,635 atoms respectively. Bringing together an international team of researchers from across the United States and Japan made it possible to develop the necessary algorithm and workflow enhancements to reach this milestone.

The researchers achieved this scale just four months after modeling the 303-atom miniprotein Trp-cage using quantum computing for the first time. Today’s new result not only demonstrates a 40-fold increase in system size compared to the Trp-cage result, it represents a 210-times improvement in accuracy from previous state-of-the-art QCSC approaches in a specific step of the workflow.

Generalization Dynamics of LM Pre-training

An AI has a limited amount of “capacity” (brainpower). Early in training, it develops quick, shallow circuits to memorize data because that’s the easiest way to get the right answer. Later, it develops complex circuits for actual reasoning. Because space is limited, these two internal systems are constantly competing for control. Whichever type of data the AI happens to be reading in a specific moment determines which circuit wins the battle.

People typically assume that LMs stably mature from pattern-matching parrots to generalizable intelligence during pre-training. We build a toy eval suite and show this mental model is wrong: throughout pre-training, LMs frequently and suddenly hop between parrot-like and intelligence-like modes, i.e. distinct algorithms implemented by distinct circuits. We call this mode-hopping. Across our suite, LMs can suddenly latch onto memorized or in-context patterns instead of in-context learning, use System 1 instead of System 2 thinking, pick up what sounds true instead of what is true, fail at multi-hop persona QA, out-of-context reasoning, and emergent misalignment — then just as suddenly revert and generalize. Mode-hopping is not explained by standard optimization dynamics: it is locally stable and can not be fixed by checkpoint averaging. We instead think of it as a capacity allocation problem: in a capacity-bounded model, generalizable circuits must compete with the shallow ones learned early in training, and the data in each pre-training window decides which circuits win. Our suite provides a cheap set of pre-training monitors and a new lens on generalization. Building upon our insights, we demonstrate three applications: (i) select intermediate pre-training checkpoints that strongly generalize reasoning and alignment, better than the final pre-or mid-training checkpoints, (ii) select pre-training data that controls and stabilizes generalization dynamics, and (iii) test prior generalization predictors, falsifying the monolithic belief that “simpler solutions generalize better”

Building general AI without generalization is doable but meh. We want an intelligence that learns deep, transferable structure, not a parrot that matches shallow patterns. Real generalization would unblock many today’s key open problems: data-efficient (online) learning, shortcut learning, transfer capabilities from verifiable domains (math, coding) to broader non-verifiable yet economically valuable domains, and maintain a coherent character that truly aligns with human values.

The distinction between parrots and intelligence is computational. Parrots repeat in-context patterns; intelligence infers in-context functions. Parrots encode a persona as bags of disconnected facts and traits; intelligence learns a shared persona representation that connects all. Parrots memorize reasoning steps; intelligence forms general reasoning circuits for entity tracking, backtracking, or even for highly abstract concepts like truth.

Designing better quantum circuits with AI

Researchers from the group of theoretical physicist Hans Briegel have collaborated with NVIDIA to develop an AI method that automatically generates efficient quantum circuits, a key bottleneck in making quantum computers practically useful.

The work was published in Machine Learning: Science and Technology, in a paper titled “Synthesis of discrete–continuous quantum circuits with multimodal diffusion models.”

Before a quantum computer can perform any useful task, a quantum algorithm needs to be translated into a sequence of elementary quantum operations, known as quantum gates. Writing these quantum circuits efficiently is one of the hardest open problems in the field.

New quantum algorithm solves “impossible” materials problem in seconds

A new quantum-inspired algorithm has cracked a problem so massive that conventional supercomputers struggle to even approach it. Researchers used the method to simulate extraordinarily complex quantum materials known as quasicrystals, opening the door to powerful new quantum devices and ultra-efficient electronics. The work could help scientists design advanced topological qubits and materials for future quantum computers.

Quobly Toolbox Explores Quantum Phase Estimation Pipeline With Tensor Networks

An international collaboration between a French quantum startup and a major Taiwanese electronics manufacturer has yielded a new open-source tool for exploring a critical area of quantum computing. Quobly and Taiwan’s Hon Hai Research Institute, the R&D arm of Foxconn, jointly released a numerical toolbox dedicated to the Quantum Phase Estimation (QPE) algorithm, described as a cornerstone of fault-tolerant quantum computing with major applications in quantum chemistry and materials science. While QPE’s theoretical benefits are understood, simulating its practical resource needs has proven difficult; the toolbox aims to bridge this gap by allowing researchers to explore implementations and their implications. The tool focuses on practical, interpretable numerical experiments, enabling full circuit executions for up to 20 qubits and circuits ranging from 1,000 to 100,000 gates on standard laptops.

Quantum Phase Estimation Toolbox for Molecular Systems

While the theoretical underpinnings of QPE are well established, simulating its practical demands has proven a significant hurdle, limiting exploration beyond simplified models. The toolbox addresses this gap by offering a platform for practical, interpretable numerical experiments, allowing scientists to investigate QPE implementations without requiring access to full-scale quantum hardware, which is currently unavailable. Built upon advanced tensor network techniques and the open-source quimb library, the toolbox facilitates the preparation of initial states using DMRG and matrix product states, and allows encoding of molecular Hamiltonians into quantum circuits through methods like trotterization and qubitization. Researchers can directly compare standard QPE with the single-ancilla Robust Phase Estimation (RPE) method, analyzing circuit depth, gate counts, and potential error sources.

String theory is uniquely derived from basic assumptions about the universe, physicists show

If you could take an apple and break it into smaller and smaller parts, you would find molecules, then atoms, followed by subatomic particles like protons and the quarks and gluons that make them up. You might think you hit the bottom, but, according to string theorists, if you keep going to even smaller scales—about a billion billion times smaller than a proton—you will find more: tiny vibrating strings.

Developed in the 1960s, string theory proposes that everything in the universe is made from invisible strings. The theory arose as a possible solution to the problem of “quantum gravity,” the quest to align quantum mechanics, which describes our world at the smallest scales, with the general theory of relativity, which explains how our universe works on the largest scales (and includes gravity). Researchers have tried to reconcile the two theories—asking, for example, how gravity behaves in the quantum realm—but their equations go berserk, or in mathematical terms, go to infinity.

String theory is a mathematical solution that tames the unruly infinities. It purports that all particles, including the graviton—the hypothetical particle believed to convey the force of gravity—are generated by very small vibrating strings. The math behind string theory requires the strings to vibrate in at least 10 dimensions, rather than the four we live in (three for space and one for time), which is one of the reasons some scientists are not convinced that string theory is correct. But perhaps the biggest challenge for the theory is the ultrahigh energies required for testing it: Such an experiment would require a particle collider the size of a galaxy.

Engineered proteins store digital files with 30 times density at one-tenth cost

Massive volumes of digital data are generated every day from AI training, big data analytics and smart devices. As conventional hard drives and cloud storage are increasingly constrained by high costs, limited capacity, high power consumption and short lifespans, molecular data storage has emerged as a breakthrough storage alternative.

Researchers at The Hong Kong Polytechnic University (PolyU) have pioneered a method that uses engineered proteins to store digital data and, for the first time, completed the full process from data storage to data retrieval in de novo designed unnatural proteins.

This demonstrates the potential of establishing a protein-based storage framework with sustainability, high storage capacity and high stability, offering a promising solution to the explosive AI-generated growth in data globally.

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