New research by Curtin University has achieved a breakthrough in eco-friendly display technology, creating highly efficient and stable blue quantum dot LEDs (QLEDs) that could power the next generation of televisions, smartphones, VR headsets and energy-efficient lighting – without using toxic heavy metals.

Q.ANT, a pioneer in photonic processing for AI, has launched a dedicated production line for its high-performance photonic AI chips at the Institute of Microelectronics Stuttgart (IMS CHIPS), marking a significant semiconductor manufacturing milestone. By integrating Q.ANT’s patented photonic chip technology on the base of Thin-Film Lithium Niobate (TFLN) material and upcycling the existing CMOS production facility at IMS CHIPS, the partners have established a first-of-its-kind manufacturing line to accelerate the production of energy-efficient, high-performance processors for AI applications. Q.ANT has invested € 14 million in machinery and equipment to complement the new line for photonic chips.

Zhao leads a group of researchers focused on computational catalyst design. This uses quantum mechanics — science that analyzes the behavior of molecules and atoms and their constituents: electrons, protons, neutrons, quarks, etc. — and machine learning to understand chemical reactions and design more energy-efficient and reactive catalysts.

Rice University physicist Emilia Morosan is part of an international research collaboration that has been awarded multimillion-dollar funding from The Kavli Foundation to develop and test next-generation superconductors through artificial intelligence and quantum geometry. This global initiative, spearheaded by Päivi Törmä of Aalto University in Finland, seeks to push the boundaries of quantum materials science and superconductivity.

Quantum Computing Inc. (“QCi” or the “Company”), an innovative, integrated photonics and quantum optics technology company, today announced it has received a fifth purchase order for its thin film lithium niobate (TFLN) photonic chip foundry. The latest order comes from a research group based in Canada to support its research efforts on quantum photonics.

Demcon has partnered with TNO and the University of Twente to accelerate low-energy modulation of silicon nitride (SiN) photonic chips. These advanced photonic chips promise to create cheaper, faster, and more energy-efficient devices, enabling earlier disease diagnostics, safe self-driving vehicles, and more efficient data communication. By combining their expertise, Demcon, University of Twente and TNO are set to drive significant advancements in photonic chip technology. The research is partly funded by the National Growth Fund project PhotonDelta.

Researchers have created a new ultra-broadband electro-optic comb that packs 450 nm of light precision into a chip smaller than a coin, paving the way for smarter, more efficient photonic devices.

BTQ Technologies Corp. , a global quantum technology company focused on securing mission-critical networks, is pleased to announce to announce the publication of the research paper "Proof-of-Work Consensus by Quantum Sampling" in the prestigious journal Quantum Science and Technology, published by IOP Publishing. This peer-reviewed, open-access article marks a significant milestone in advancing sustainable and quantum-secure blockchain technology.

Multiverse Computing, a global leader in value-based quantum and quantum-inspired AI software solutions, today announced an investment from the largest Italian investor in venture capital CDP Venture Capital Sgr, as part of the company’s Series A round. The investment was carried out through two compartments of the Corporate Partners I fund, ServiceTech and Energytech, a corporate venture capital fund participated by some of the main Italian corporates as limited partners, including Baker Hughes, BNL BNP Paribas, Edison, GPI, Italgas, Snam and Terna Forward. Financial details of the investment were not disclosed.

Photonic hardware, which can perform machine-learning computations with light, offers a faster and more energy-efficient alternative. However, there are some types of neural network computations that a photonic device can’t perform, requiring the use of off-chip electronics or other techniques that hamper speed and efficiency. Building on a decade of research, scientists from MIT and elsewhere have developed a new photonic chip that overcomes these roadblocks. They demonstrated a fully integrated photonic processor that can perform all the key computations of a deep neural network optically on the chip.