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A study that could help revolutionize wireless communication introduces a novel method to curve terahertz signals around an obstacle.

While cellular networks and Wi-Fi systems are more advanced than ever, they are also quickly reaching their bandwidth limits. Scientists know that in the near future they’ll need to transition to much higher communication frequencies than what current systems rely on, but before that can happen there are a number of — quite literal — obstacles standing in the way.

Researchers from Brown University and Rice University say they’ve advanced one step closer to getting around these solid obstacles, like walls, furniture and even people — and they do it by curving light.

Varying the parameters of weight distribution did not account for the observed amount of HD information conveyed by PoSub-FS cells (Fig. 2a). Rather, we found that the number of inputs received by each output unit was a key factor influencing the amount of HD information (Extended Data Fig. 5e). Varying both weight distribution and the number of input units, we obtained a distribution of HD information in output tuning curves that matched the real data (Extended Data Fig. 5f), revealing that the tuning of PoSub-FS cells can be used to estimate both the distribution of weights and the number of input neurons. Notably, under optimal network conditions, Isomap projection of output tuning curve auto-correlograms has a similar geometry to that of real PoSub-FS cells (Extended Data Fig. 5g), confirming similar distribution of tuning shapes.

To further quantify the relative contributions of ADN and local PoSub inputs to PoSub-FS cell tuning, we expanded the simulation to include the following two inputs: one with tuning curve widths corresponding to ADN-HD cells and one with tuning curve widths corresponding to PoSub-HD cells (Fig. 4h, left). We then trained the model using gradient descent to find the variances and means of input weights that result in the best fit between the simulated output and real data. The combination of parameters that best described the real data resulted in ADN inputs distributed in a near Gaussian-like manner but a heavy-tailed distribution of PoSub-HD inputs (Fig. 4h, middle). Using these distribution parameters, we performed simulations to determine the contribution of ADN-HD and PoSub-HD inputs to the output tuning curves and established that PoSub-FS cell-like outputs are best explained by flat, high firing rate inputs from ADN-HD cells and low firing rate, HD-modulated inputs from PoSub-HD cells (Fig. 4h, right).

Our simulations, complemented by direct analytical derivation (detailed in the Supplementary Methods), not only support the hypothesis that the symmetries observed in PoSub-FS cell tuning curves originate from local cortical circuits but also demonstrate that these symmetries emerge from strongly skewed distributions of synaptic weights.

How can we guarantee that data sent over the internet is only accessible to its intended recipient? Currently, our data is secured using encryption methods based on the premise that factoring large numbers is a complex task. However, as quantum computing advances, these encryption techniques may become vulnerable and potentially ineffective in the future.

Encryption by means of physical laws

Tobias Vogl, a professor of Quantum Communication Systems Engineering, is working on an encryption process that relies on principles of physics. “Security will be based on the information being encoded into individual light particles and then transmitted. The laws of physics do not permit this information to be extracted or copied. When the information is intercepted, the light particles change their characteristics. Because we can measure these state changes, any attempt to intercept the transmitted data will be recognized immediately, regardless of future advances in technology,” says Tobias Vogl.

Jason Matheny is a delight to speak with, provided you’re up for a lengthy conversation about potential technological and biomedical catastrophe.

Now CEO and president of Rand Corporation, Matheny has built a career out of thinking about such gloomy scenarios. An economist by training with a focus on public health, he dived into the worlds of pharmaceutical development and cultivated meat before turning his attention to national security.

As director of Intelligence Advanced Research Projects Activity, the US intelligence community’s research agency, he pushed for more attention to the dangers of biological weapons and badly designed artificial intelligence. In 2021, Matheny was tapped to be President Biden’s senior adviser on technology and national security issues. And then, in July of last year, he became CEO and president of Rand, the oldest nonprofit think tank in the US, which has shaped government policy on nuclear strategy, the Vietnam War, and the development of the internet.

Researchers have produced, stored, and retrieved quantum information for the first time, a critical step in quantum networking.

The ability to share quantum information is crucial for developing quantum networks for distributed computing and secure communication. Quantum computing will be useful for solving some important types of problems, such as optimizing financial risk, decrypting data, designing molecules, and studying the properties of materials.

“Interfacing two key devices together is a crucial step forward in allowing quantum networking, and we are really excited to be the first team to have been able to demonstrate this.” —

A diss track featuring the apparent vocals of rapper Kendrick Lamar made its rounds on social media earlier this week, escalating the beef between him and Aubrey “Drake” Graham.

Now a 23-year-old musician who goes by the moniker Sly the Rapper has come forward, alleging he’s behind the viral track, which was titled simply “Freestyle.” And guess what? He says it was AI-generated.

That’s impressive, because it fooled plenty of people into believing it was the real thing.

A collaboration of scientists from various universities in the UK and Europe have stored and retrieved data from quantum computers, marking a “crucial connection for ‘quantum internet,’” in a global first.

This is an essential step in quantum networking as the world gears up for the next generation of computing.

With its ultrafast computational speeds, quantum computing is touted to solve the world’s problems in designing new drugs, understanding the properties of materials, and optimizing financial risk.

The expansion of fiber optics is progressing worldwide, which not only increases the bandwidth of conventional Internet connections, but also brings closer the realization of a global quantum Internet. The quantum internet can help to fully exploit the potential of certain technologies. These include much more powerful quantum computing through the linking of quantum processors and registers, more secure communication through quantum key distribution or more precise time measurements through the synchronization of atomic clocks.

However, the differences between the glass fiber standard of 1,550 nm and the system wavelengths of the various quantum bits (qubits) realized to date represent a hurdle, because those qubits are mostly in the visible or near-infrared spectral range. Researchers want to overcome this obstacle with the help of quantum frequency conversion, which can specifically change the frequencies of photons while retaining all other quantum properties. This enables conversion to the 1,550 nm telecom range for low-loss, long-range transmission of quantum states.

On November 30, 2022, Silicon Valley-based company OpenAI launched its artificial-intelligence-powered chatbot, ChatGPT. Overnight, AI transformed in the popular imagination from a science fiction trope to something anyone with an internet connection could try. ChatGPT was free to use, and it responded to typed prompts naturally enough to seem almost human. After the launch of the chatbot, worldwide Google searches for the term “AI” began a steep climb that still does not seem to have reached its peak.

Physicists were some of the earliest developers and adopters of technologies now welcomed under the wide umbrella term “AI.” Particle physicists and astrophysicists, with their enormous collections of data and the need to efficiently analyze it, are just the sort of people who benefit from the automation AI provides.

So we at Symmetry, an online magazine about particle physics and astrophysics, decided to explore the topic and publish a series on artificial intelligence. We looked at the many forms AI has taken; the ways the technology has helped shape the science (and vice versa); and the ways scientists use AI to advance experimental and theoretical physics, to improve the operation of particle accelerators and telescopes, and to train the next generation of physics students. You can expect to see the result of that exploration here in the coming weeks.