Scientists have finally figured out a way to connect the dots between the macroscopic and the microscopic worlds. Their magical equation might provide us answers to questions like why black holes don’t collapse and how quantum gravity works.
Researchers from the University of Pisa developed a quantum subroutine to improve matrix multiplication for AI and machine learning applications.
When you multiply two large matrices—this is a common task in fields like machine learning, but it can be time-consuming, even for powerful computers…
In a recent study published in IEEE Access, a team of researchers from the University of Pisa introduced a quantum subroutine designed to streamline matrix multiplication. This subroutine is a new feature in the toolbox of matrix multiplication that could improve computational efficiency, particularly in applications like machine learning and data processing.
Break it down: How AI can learn from the brain.
In a recent paper titled “A sensory-motor theory of the neocortex” published in the journal Nature Neuroscience, Rao posited that the brain uses active predictive coding (APC) to understand the world and break down complicated problems into simpler…
When you reach out to pet a dog, you expect it to feel soft. If it doesn’t feel like how you expect, your brain uses that feedback to inform your next action — maybe you pull your hand away. Previous models of how the brain works have typically separated perception and action. For Allen School professor Rajesh Rao, those two processes are closely intertwined, and their relationship can be mapped using a computational algorithm.
Roadmap: Quantum algorithms and quantum machine learning could assist high-energy physics, ranging from studying neutrino oscillations to reconstructing particle trajectories in colliders.
You know the classic game Pong with the paddles and ball that moves across the screen? Imagine the ball and paddles synchronized to music. Victor Tao approached the challenge as an optimization problem to figure out where the paddle and balls should go, based on the beats of a song:
Fortunately there is a mature field dedicated to optimizing an objective (screen utilization) with respect to variables (the locations of bounces) in the presence of constraints on those variables (physics and the beats of the song). If we write our requirements as a constrained optimization problem, we can use an off-the-shelf solver to compute optimal paddle positions instead of designing an algorithm ourselves.
Continue reading “Music visualizer in the style of a Pong game” »
Widely utilized across various industries such as chemistry, agriculture, and military, this technology relies on strategies like dispersive optics and narrow-band light filters.
However, limitations exist in these approaches. Additionally, the fabrication of large-scale InGaAs detector arrays poses challenges, necessitating the development of new experimental methods and algorithms to advance infrared hyperspectral imaging technology in terms of miniaturization and cost-effectiveness.
In a paper published in Light Science & Applications, a team led by Professor Baoqing Sun and Yuan Gao from Shandong University introduce a novel method for encoding near-infrared spectral and spatial data.
The study
The researchers monitored the brainwaves of 100 students as they performed a series of cognitive tasks. They then conducted a group comparison analysis between the performance of students with higher test scores (as recorded prior to the study) against those with lower test scores.
The brainwave analysis was then analyzed using algorithms running on a D-Wave quantum annealing computer. According to the researchers, the study resulted in new insights concerning how cognitive ability relates to testing outcomes.
The three final algorithms, which have now been released, are ML-KEM, previously known as kyber; ML-DSA (formerly Dilithium); and SLH-DSA (SPHINCS+). NIST says it will release a draft standard for FALCON later this year. “These finalized standards include instructions for incorporating them into products and encryption systems,” says NIST mathematician Dustin Moody, who heads the PQC standardization project. “We encourage system administrators to start integrating them into their systems immediately.”
Duncan Jones, head of cybersecurity at the firm Quantinuum welcomes the development. “[It] represents a crucial first step towards protecting all our data against the threat of a future quantum computer that could decrypt traditionally secure communications,” he says. “On all fronts – from technology to global policy – advancements are causing experts to predict a faster timeline to reaching fault-tolerant quantum computers. The standardization of NIST’s algorithms is a critical milestone in that timeline.”
In recent years, there have been many reviews investigating neuromorphic computing from the perspectives of device electrical properties,[ 9, 10 ] resistive switching materials,[ 11, 12 ] memristive synapses and neurons,[ 13 ] algorithm optimization,[ 14 ] and circuit design.[ 15 ] Different from the existing literature, we discuss the possibility of achieving brain-like computing from the perspective of memristor technology and review the establishment of spiking neural network neuromorphic computing systems. In this article, we first review the resistive switching mechanisms of different types of memristors and focus on factors, which affect device stability and the corresponding optimization measures that have been applied. Furthermore, we study the stochasticity, power consumption, switching speed, retention, endurance, and other properties of memristors, which are the basis for neuromorphic computing implementations. We then review various memristor-based neural networks and the building of spike neural network neuromorphic computing systems. Finally, we shed light upon the major challenges and offer our perspectives and opinions for memristor-based brain-like computing systems.
From the article:
When Saha and Sinha took a closer look at the resulting equations, they realized that they could express the number pi in this way, as well as the zeta function, which is the heart of the Riemann conjecture, one of the greatest unsolved mysteries in mathematics.
Continue reading “String Theorists Accidentally Find a New Formula for Pi” »
