Lifeboat Foundation

Vishnu Reddy, Michael S. Kelley, Jessie Dotson, Davide Farnocchia, Nicolas Erasmus, David Polishook, Joseph Masiero, Lance A. M. Benner, James Bauer, Miguel R. Alarcon, David Balam, Daniel Bamberger, David Bell, Fabrizio Barnardi, Terry H. Bressi, Marina Brozovic, Melissa J. Brucker, Luca Buzzi, Juan Cano, David Cantillo, Ramona Cennamo, Serge Chastel, Omarov Chingis, Young-Jun Choi, Eric Christensen, Larry Denneau, Marek Dróżdż, Leonid Elenin, Orhan Erece, Laura Faggioli, Carmelo Falco, Dmitry Glamazda, Filippo Graziani, Aren N. Heinze, Matthew J. Holman, Alexander Ivanov, Cristovao Jacques, Petro Janse van Rensburg, Galina Kaiser, Krzysztof Kamiński, Monika K. Kamińska, Murat Kaplan, Dong-Heun Kim, Myung-Jin Kim, Csaba Kiss, Tatiana Kokina, Eduard Kuznetsov, Jeffrey A. Larsen, Hee-Jae Lee, Robert C.

Recent advances in the field of artificial intelligence (AI) and computing have enabled the development of new tools for creating highly realistic media, virtual reality (VR) environments and video games. Many of these tools are now widely used by graphics designers, animated film creators and videogame developers worldwide.

One aspect of virtual and digitally created environments that can be difficult to realistically reproduce is fabrics. While there are already various computational tools for digitally designing realistic fabric-based items (e.g., scarves, blankets, pillows, clothes, etc.), creating and editing realistic renderings of these fabrics in real-time can be challenging.

Researchers at Shandong University and Nanjing University recently introduced a new lightweight artificial neural network for the real-time rendering of woven fabrics. Their proposed network, introduced in a paper published as part of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ‘24, works by encoding the patterns and parameters of fabrics as a small latent vector, which can later be interpreted by a decoder to produce realistic representations of various fabrics.

As the name suggests, most electronic devices today work through the movement of electrons. But materials that can efficiently conduct protons—the nucleus of the hydrogen atom—could be key to a number of important technologies for combating global climate change.

Most proton-conducting inorganic materials available now require undesirably high temperatures to achieve sufficiently high conductivity. However, lower-temperature alternatives could enable a variety of technologies, such as more efficient and durable fuel cells to produce clean electricity from hydrogen, electrolyzers to make clean fuels such as hydrogen for transportation, solid-state proton batteries, and even new kinds of computing devices based on iono-electronic effects.

In order to advance the development of proton conductors, MIT engineers have identified certain traits of materials that give rise to fast proton conduction. Using those traits quantitatively, the team identified a half-dozen new candidates that show promise as fast proton conductors. Simulations suggest these candidates will perform far better than existing materials, although they still need to be conformed experimentally. In addition to uncovering potential new materials, the research also provides a deeper understanding at the atomic level of how such materials work.

Selection rules play an important role in Darwinian evolution. Now, it has been shown that selective templation enables the purification of oligomer libraries in a coacervate model, and that the oligomer library can reversibly affect the coacervates’ fusion behaviour.

Dive into our latest feature that unveils the seamless melding of the human mind with sophisticated technology. Witness how brain-machine interfaces are setting the stage for a revolution in how we interact with the digital world.

The quantum advantage, a key goal in quantum computation, is achieved when a quantum computer’s computational capability surpasses classical means. A recent study introduced a type of Instantaneous Quantum Polynomial-Time (IQP) computation, which was challenged by IBM Quantum and IonQ researchers who developed a faster classical simulation algorithm. IQP circuits are beneficial due to their simplicity and moderate hardware requirements, but they also allow for classical simulation. The IQP circuit, known as the HarvardQuEra circuit, is built over n 3m 32k inputs. There are two types of simulation for quantum computations: noiseless weak/direct and noisy.

The quantum advantage is a key goal for the quantum computation community. It is achieved when a quantum computer’s computational capability becomes so complex that it cannot be reproduced by classical means. This ongoing negotiation between classical simulations and quantum computational experiments is a significant focus in the field.

A recent publication by Bluvstein et al. introduced a type of Instantaneous Quantum Polynomial-Time (IQP) computation, complemented by a 48-qubit logical experimental demonstration using quantum hardware. The authors projected the simulation time to grow rapidly with the number of CNOT layers added. However, researchers from IBM Quantum and IonQ reported a classical simulation algorithm that computes an amplitude for the 48-qubit computation in only 0.00257947 seconds, which is roughly 10³ times faster than that reported by the original authors. This algorithm is not subject to a significant decline in performance due to the additional CNOT layers.

Carbon nanotube networks made with high purity and ultraclean interfaces can be used to make a tensor processing unit that contains 3,000 transistors in a systolic array architecture to improve energy efficiency in accelerating neural network tasks.

Furthermore, many experimental factors, such as fabrication errors and physical misalignments, can affect the performance of diffractive processors during the experimental deployment stage. Investigating the inherent robustness of different nonlinear encoding strategies to such imperfections, as well as their integration with vaccination-based training strategies³⁹ or in situ training methods⁴⁰, would provide more comprehensive guidance on the implementation and limitations of these approaches. These considerations would be crucial for future research and practical implementations of diffractive optical processors.

Throughout the manuscript, our analyses assumed that diffractive optical processors consist of several stacked diffractive layers interconnected through free-space light propagation, as commonly used in the literature^10,13,41,42. Our forward model employs the angular spectrum method for light propagation, a broadly applicable technique known for its accuracy, covering all the propagating modes in free space. While our forward model does not account for multiple reflections between the diffractive layers, it is important to note that such cascaded reflections are much weaker than the transmitted light and, thus, have a negligible impact on the optimization process. This simplification does not compromise the model’s experimental validity since a given diffractive model also acts as a 3D filter for such undesired secondary sources that were ignored in the optimization process; stated differently, a by-product of the entire optimization process is that the resulting diffractive layers collectively filter out some of these undesired sources of secondary reflections, scattering them outside the output FOV. The foundation of our model has been extensively validated through various experiments^{10,11,16,18,43}, providing a good match to the corresponding numerical model in each case, further supporting the accuracy of our forward model and diffractive processor design scheme.

Finally, our numerical analyses were conducted using coherent monochromatic light, which has many practical, real-world applications such as holographic microscopy and sensing, laser-based imaging systems, optical communications, and biomedical imaging. These applications, and many others, benefit from the precise control of the wave information carried by coherent light. In addition to coherent illumination, diffractive optical processors can also be designed to accommodate temporally and spatially incoherent illumination. By optimizing the layers for multiple wavelengths of illumination, a diffractive processor can be effectively designed to operate under broadband illumination conditions^{18,19,29,43,44,45,46,47}. Similarly, by incorporating spatial incoherence into the forward model simulations, we can design diffractive processors that function effectively with spatially incoherent illumination^30,48. Without loss of generality, our current study focuses on coherent monochromatic light to establish a foundational understanding of nonlinear encoding strategies in diffractive information processing using linear optical materials by leveraging the precise control that coherent processors offer. Future work could explore the extension of these principles to spatially or temporally incoherent illumination scenarios, further broadening the applicability of diffractive optical processors in practical settings.

Quantum field theory (QFT) was a crucial step in our understanding of the fundamental nature of the Universe. In its current form, however, it is poorly suited for describing composite particles, made up of multiple interacting elementary particles. Today, QFT for hadrons has been largely replaced with quantum chromodynamics, but this new framework still leaves many gaps in our understanding, particularly surrounding the nature of strong nuclear force and the origins of dark matter and dark energy. Through a new algebraic formulation of QFT, Dr Abdulaziz Alhaidari at the Saudi Center for Theoretical Physics hopes that these issues could finally be addressed.

The emergence of quantum field theory (QFT) was one of the most important developments in modern physics. By combining the theories of special relativity, quantum mechanics, and the interaction of matter via classical field equations, it provides robust explanations for many fundamental phenomena, including interactions between charged particles via the exchange of photons.

Still, QFT in its current form is far from flawless. Among its limitations is its inability to produce a precise description of composite particles such as hadrons, which are made up of multiple interacting elementary particles that are confined (cannot be observed in isolation). Since these particles possess an internal structure, the nature of these interactions becomes far more difficult to define mathematically, stretching the descriptive abilities of QFT beyond its limits.