Terra Quantum (Switzerland)

The paper presents an approach to laser treatment for a wide range of conditions using laser tissue modification through non-ablative thermomechanical action and the creation of microdefects, including gas bubbles, microcracks, micropores, and denatured micro zones. Microdefects can be temporary or permanent and control the physicochemical and mechanical properties of tissues, facilitating their modification aimed at restoring the compliance of calcified arteries and regenerating damaged articular cartilage. Microdefects and thermomechanical effects promote stress relaxation and intercellular interactions through the generation and transfer of signaling molecules, thereby regulating cellular nutrition and metabolism. A theoretical model has been developed based on a sequence of processes, including tissue heating under spatiotemporally modulated laser radiation, local rupture of chemical bonds leading to the formation of microdefects and thermomechanical stresses. Backscattered light measurements are used to monitor the formation of microdefects to develop a real-time feedback system to ensure the effectiveness and safety of laser therapy.

We present a ferroelectric-based data reservoir designed for neuromorphic computing applications. The reservoir consists of an ensemble of nonlinear ferroelectric nanodots capable of storing information through their stable polarization states. These nanodots are confined between electrodes and form a network of parallel connected capacitive elements, which can either be addressed individually or integrated between common conducting plates. By leveraging the intrinsic nonlinearity and memory of ferroelectric polarization switching, the device maps incoming signals into a high-dimensional space of polarization configurations. This physical transformation enables efficient temporal information encoding and provides a rich dynamic representation for subsequent processing in a readout layer of a neuromorphic circuit.

Time remains one of the most elusive concepts in physics, lying at the intersection of quantum mechanics, relativity, and thermodynamics. This work proposes a reformulation in which time arises as a local informational field rather than as a universal coordinate. Temporal direction is identified with gradients in stored information, linking geometry and entropy through an informational potential that generates both curvature and the arrow of time. The resulting field Ta(x)=∂aSinfo(x) defines causal order and temporal flow through local information exchange, unifying dynamical evolution and entropic asymmetry within a single framework. The formulation preserves general relativity in the macroscopic limit while extending its validity to microscopic regimes where information dynamics supersede geometric structure. It thus offers a coherent physical basis for temporal asymmetry, a bridge between quantum and gravitational descriptions, and a platform for simulating time as an observable field.

Abstract The first hardware evidence is reported that a Quantum Memory Matrix (QMM) ‐ conceived as a Planck‐scale lattice of finite‐dimensional memory cells ‐ functions as an ultra‐shallow, measurement‐free error‐suppression layer for noisy intermediate‐scale quantum processors. On a seven‐qubit IBM transmon device, quantum states are imprint onto local cells with single‐qubit and nearest‐neighbor gates and later retrieve them through a controlled‐SWAP, guaranteeing unitary reversibility. A single imprint–retrieval cycle attains a hardware fidelity of ; prepending the same layer to a conventional repetition code boosts the logical fidelity to ‐ a 32 % improvement obtained without additional CX gates. Incorporating the layer into a variational quantum classifier lowers the final training loss by 35 % and halves run‐to‐run variance, evidencing its value for hybrid quantum‐classical workloads. Noise‐calibrated simulations further show that stacking three QMM layers brings the logical error rate to within 20 % of a distance‐three surface code while using an order of magnitude fewer qubits. Because the QMM booster is fully unitary and eschews mid‐circuit measurement, it is directly compatible with platforms where rapid stabilizer read‐out is impractical and provides empirical support for the broader notion that space‐time itself may behave as a distributed quantum memory.

Abstract Thermoelectric transport is widely used to study Abrikosov vortex dynamics in unconventional superconductors. However, only a few thermoelectric studies have been conducted near the dimensional crossover that occurs when the vortex‐vortex interaction length scale becomes comparable to the sample size. Here, the effects of finite size on the dissipation mechanisms of the Nernst effect in the optimally doped Bi2Sr2CaCu2O8 + x high‐temperature superconductor are reported, down to the atomic length limit. To access this regime, a new generation of thermoelectric chips based on silicon nitride microprinted circuit boards is developed. These chips ensure optimized signals while preventing sample deterioration. The results demonstrate that lateral confinement at the nanoscale can effectively reduce vortex dissipation. Investigating vortex dissipation at the micro‐ and nano‐scale is essential for creating stable, miniaturized superconducting circuits.

Single-pixel imaging (SPI) is an alternative method for obtaining images using a single photodetector, which has numerous advantages over the traditional matrix-based approach. However, most experimental SPI realizations provide relatively low resolution compared to matrix-based imaging systems. Here, we show a simple yet effective experimental method to scale up the resolution of SPI. Our imaging system utilizes patterns based on Hadamard matrices, which, when reshaped to a variable aspect ratio, allow us to improve resolution along one of the axes, while sweeping of patterns improves resolution along the second axis. This work paves the way towards novel imaging systems that retain the advantages of SPI and obtain resolution comparable to matrix-based systems.

Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era, where circuits with a large number of qubits are currently infeasible. This model demonstrated a record-breaking classification accuracy of 99.21% on the full MNIST dataset, surpassing the performance of known quantum–classical models, while having eight times fewer parameters than its classical counterpart. Also, the results of testing this hybrid model on a Medical MNIST (classification accuracy over 99%), and on CIFAR-10 (classification accuracy over 82%), can serve as evidence of the generalizability of the model and highlights the efficiency of quantum layers in distinguishing common features of input data. Our second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process. The model matches the performance of its classical counterpart, having four times fewer trainable parameters, and outperforms a classical model with equal weight parameters. These models represent advancements in quantum machine learning research and illuminate the path towards more accurate image classification systems.

Abstract Room temperature superconductivity under normal conditions has been a major challenge of physics and material science since its discovery. Here the global room‐temperature superconductivity observed in cleaved highly oriented pyrolytic graphite carrying dense arrays of nearly parallel surface line defects is reported. The multiterminal measurements performed at the ambient pressure in the temperature interval 4.5 K ≤ T ≤ 300 K and at magnetic fields 0 ≤ B ≤ 9 T applied perpendicular to the basal graphitic planes reveal that the superconducting critical current I c ( T , B ) is governed by the normal state resistance R N ( T , B ) so that I c ( T , B ) is proportional to 1/ R N ( T , B ). Magnetization M ( T , B ) measurements of superconducting screening and hysteresis loops together with the critical current oscillations with temperature that are characteristic for superconductor‐ferromagnet‐superconductor Josephson chains, provide strong support for the occurrence of superconductivity at T > 300 K. A theory of global superconductivity emerging in the array of linear structural defects is developed which well describes the experimental findings and demonstrate that global superconductivity arises as a global phase coherence of superconducting granules in linear defects promoted by the stabilizing effect of underlying Bernal graphite via tunneling coupling to the three dimensional (3D) material.