The Quantum Insider (TQI) is the leading online resource dedicated exclusively to Quantum Computing.
Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.
The last two years saw cloud technology heavily encouraged across almost every sector. For businesses wishing to thrive in the chaos of the pandemic, the move to cloud environments became a necessity amidst the shift to remote work and the frequent inability to access data centers.
As a result, more businesses than ever — including many in established industries such as manufacturing, retail and healthcare — have accelerated their adoption of cloud-first models and strategies. This approach is empowering these industries with more agility and efficiency in what has been a very uncertain time for the world and thus, for business.
❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers.
📝 The paper “High-Resolution Image Synthesis with Latent Diffusion Models” is available here:
https://ommer-lab.com/research/latent-diffusion-models/
https://github.com/mallorbc/stable-diffusion-klms-gui.
Continue reading “Stable Diffusion: DALL-E 2 For Free, For Everyone!” »
A relatively new kind of semiconductor, layered atop a mirror-like structure, can mimic the way that leaves move energy from the sun over relatively long distances before using it to fuel chemical reactions. The approach may one day improve the efficiency of solar cells.
“Energy transport is one of the crucial steps for solar energy harvesting and conversion in solar cells,” said Bin Liu, a postdoctoral researcher in electrical and computer engineering and first author of the study in the journal Optica.
“We created a structure that can support hybrid light-matter mixture states, enabling efficient and exceptionally long-range energy transport.”
According to the researcher, the same technology could be applied to beetles and cicadas as well.
It’s a fun and futuristic vision: an army of remotely controlled cyborg insects that can infiltrate hard to reach locations or monitor crops.
But scientists will have to advance the tech carefully — nobody wants to risk a cyborg cockroach uprising.
Circa 2016 face_with_colon_three
The Deutsche Physikalische Gesellschaft (DPG) with a tradition extending back to 1,845 is the largest physical society in the world with more than 61,000 members. The DPG sees itself as the forum and mouthpiece for physics and is a non-profit organisation that does not pursue financial interests. It supports the sharing of ideas and thoughts within the scientific community, fosters physics teaching and would also like to open a window to physics for all those with a healthy curiosity.
Did you know there’s a silent war going on inside your home? Alternating current (AC) electricity comes in from the grid, but many of your appliances and lighting run on direct current (DC). Every time you plug in a TV, computer or cell phone charger, power must be individually converted from AC to DC — a costly and inefficient process. Purdue University researchers have proposed a solution to the problem by retrofitting an entire house to run on its own efficient DC-powered nano-grid.
The project to transform a 1920s-era West Lafayette home into the DC Nanogrid House began in 2017 under the direction of Eckhard Groll, the William E. and Florence E. Perry Head of Mechanical Engineering, and member of Purdue’s Center for High Performance Buildings. “We wanted to take a normal house and completely retrofit it with DC appliances and DC architecture,” Groll said. “To my knowledge, no other existing project has pursued an experimental demonstration of energy consumption improvements using DC power in a residential setting as extensively as we have.”
A new optical device measures photon indistinguishability—an important property for future light-based quantum computers.
Photons can be used to perform complex computations, but they must be identical or close to identical. A new device can determine the extent to which several photons emitted by a source are indistinguishable [1]. Previous methods only gave a rough estimate of the indistinguishability, but the new method offers a precise measurement. The device—which is essentially an arrangement of interconnected waveguides—could work as a diagnostic tool in a quantum optics laboratory.
In optical quantum computing, sequences of photons are made to interact with each other in complex optical circuits (see Synopsis: Quantum Computers Approach Milestone for Boson Sampling). For these computations to work, the photons must have the same frequency, the same polarization, and the same time of arrival in the device. Researchers can easily check if two photons are indistinguishable by sending them through a type of interferometer in which two waveguides—one for each photon—come close enough that one photon can hop into the neighboring waveguide. If the two photons are perfectly indistinguishable, then they always end up together in the same waveguide.
so-called qubits, to perform computations much faster than any classical computer ever could.
While multiple frontrunner startups have explored various technology platforms, from superconducting qubits and ion trap systems to diamond-based quantum accelerators, scaling the number of qubits from a few dozen to hundreds, thousands, and eventually millions of qubits has remained notoriously difficult. But this might change with photonic quantum computing.
The startup ORCA Computing builds photonic quantum computers that use photons, the fundamental particles of light, as qubits. Using quantum memories and established telecommunications technology, it can scale its devices more easily and integrate with existing computing infrastructure e.g. in data centers. Based on the core memory technology developed by Kris Kaczmarek, ORCA was officially co-founded by Ian Walmsley, Richard Murray, Josh Nunn, and Cristina Escoda in Oxford in the fall of 2019. This summer 2022, it has raised a $15M Series A led by Octopus Ventures and joined by Oxford Science Enterprises, Quantonation, and Verve Ventures, with additional, project-based funding provided by Innovate UK. Previous investors also include Atmos Ventures and Creative Destruction Lab.
Neural Radiance Fields (NeRF) were first developed, greatly enhancing the quality of new vision synthesis. It was first suggested as a way to rebuild a static picture using a series of posed photographs. However, it has been swiftly expanded to include dynamic and uncalibrated scenarios. With the assistance of sizable controlled datasets, recent work additionally concentrate on animating these human radiance field models, thereby broadening the application domain of radiance-field-based modeling to provide augmented reality experiences. In this study, They are focused on the case when just one video is given. They aim to rebuild the human and static scene models and enable unique posture rendering of the person without the need for pricey multi-camera setups or manual annotations.
Neural Actor can create inventive human poses, but it needs several films. Even with the most recent improvements in NeRF techniques, this is far from a simple task. The NeRF models must be trained using many cameras, constant lighting and exposure, transparent backgrounds, and precise human geometry. According to the table below, HyperNeRF cannot be controlled by human postures but instead creates a dynamic scene based on a single video. ST-NeRF uses many cameras to rebuild each person using a time-dependent NeRF model, although the editing is only done to change the bounding box. HumanNeRF creates a human model from a single video with masks that have been carefully annotated; however, it does not demonstrate generalization to novel postures.
With a model trained on a single video, Vid2Actor can produce new human poses, but it cannot model the surroundings. They solve these issues by proposing NeuMan, a system that can create unique human stances and novel viewpoints while reconstructing the person and the scene from a single in-the-wild video. Figure 1’s high-quality pose-driven rendering is made possible by NeuMan, a cutting-edge framework for training NeRF models for both the human and the scene. They first estimate the camera poses, the sparse scene model, the depth maps, the human stance, the human form, and the human masks from a moving camera’s video.
