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Category: information science

What’s going on inside quantum computers? New method simplifies process tomography

Quantum computers work by applying quantum operations, such as quantum gates, to delicate quantum states. Ideally, quantum computers can solve complex equations at staggeringly fast speeds that vastly outpace regular computers. In real hardware, the operations of quantum computers often deviate from the ideal behavior because of device imperfections and unwanted noise from the environment. To build reliable quantum machines, researchers need a way to accurately determine what a quantum device is actually doing.

Quantum process tomography (QPT) is a standard method for this. However, traditional QPT becomes very costly as the system grows, because the number of required measurements and calculations increases rapidly with the number of qubits.

To address this challenge, a research team from Tohoku University, the Nara Institute of Science and Technology (NAIST), and the University of Information Technology (Vietnam National University, Ho Chi Minh City) has introduced a new framework called compilation-based quantum process tomography (CQPT). The work is published in Advanced Quantum Technologies.

All-in-Focus Fourier Ptychographic Microscopy via 3D Implicit Neural Representation

Microscopy has long been essential to biomedical research, enabling detailed analyses of complex samples. Fourier ptychographic microscopy (FPM), a computational imaging technique, provides high-resolution, wide-field images without requiring extensive hardware modifications. However, current FPM algorithms struggle with samples exhibiting depth variations, such as tilted or 3-dimensional (3D) objects. The limited depth of field (DoF) leads to images with only focal-plane areas in sharp focus, while regions outside appear blurred. To address this limitation, we propose an all-in-focus FPM algorithm using physics-informed 3D neural representations to reconstruct sharp, wide-field images of 3D objects under limited DoF. Unlike previous methods, our approach samples the full depth range to create a 3D feature volume that incorporates spatial and depth information.

Biology, not physics, holds the key to reality

Three centuries after Newton described the universe through fixed laws and deterministic equations, science may be entering an entirely new phase.

According to biochemist and complex systems theorist Stuart Kauffman and computer scientist Andrea Roli, the biosphere is not a predictable, clockwork system. Instead, it is a self-organising, ever-evolving web of life that cannot be fully captured by mathematical models.

Organisms reshape their environments in ways that are fundamentally unpredictable. These processes, Kauffman and Roli argue, take place in what they call a “Domain of No Laws.”

This challenges the very foundation of scientific thought. Reality, they suggest, may not be governed by universal laws at all—and it is biology, not physics, that could hold the answers.

Tap here to read more.

Brain organoids can be trained to solve a goal-directed task

This research is the first rigorous academic demonstration of goal-directed learning in lab-grown brain organoids, and lays the foundation for adaptive organoid computation—exploring the capacity of lab-grown brain organoids to learn and solve tasks.

Using organoids derived from mouse stem cells and an electrophysiology system developed by industry partners Maxwell Biosciences, the researchers use electrical simulation to send and receive information to and from neurons. By using stronger or weaker signals, they communicate to the organoid the angle of the pole, which exists in a virtual environment, as it falls in one direction or the other. As this happens, the researchers observe as the organoid sends back signals of how to apply force to balance the pole, and they apply this force to the virtual pole.

For their pole-balancing experiments, the researchers observe as the organoid controls the pole until it drops, which is called an episode. Then, the pole is reset and a new episode begins. In essence, the organoid plays a video game in which the goal is to balance the pole upright for as long as possible.

The researchers observe the organoid’s progress in five-episode increments. If the organoid keeps the pole upright for longer on average in the past five episodes as compared to the past 20, it receives no training signal since it has been improving. If it does not improve the average time it keeps the pole upright, it receives a training signal.

Training feedback is not given to the organoid while it is balancing the pole—only at the end of an episode. An AI algorithm called reinforcement learning is used to select which neurons within the organoid get the training signal.

The results of this study prove that the reinforcement learning algorithm can guide the brain organoids toward improved performance at the cart-pole task—meaning organoids can learn to balance the pole for longer periods of time.

The researchers adopted a rigorous framework for success to make sure they were observing true improvement, and not just random success, including a threshold for the minimum time an organoid needs to balance the pole to “win” the game.

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‘It seemed to defy the laws of physics’: The everlasting ‘memory crystals’ that could slash data centre emissions

In the face of rising emissions from data centres, researchers are turning to micro-explosions in glass, and using DNA to solve big data’s big problem.

Mathematicians make a breakthrough on 2,000-year-old problem of curves

From the article:

“A Rule for Every Curve”

That’s where the new proof comes in. Its authors present a formula that can be applied to any curve in the mathematical universe, whatever its degree. It doesn’t say precisely how many rational points that curve has, but it gives an upper limit on what that number can be.

Previous formulas of this kind either didn’t apply to all curves or depended on the specific equation used to define them. The new formula is something mathematicians have hoped for since Faltings’s proof, a “uniform” statement that applies to all curves without depending on the coefficients in their equations. “This one statement gives us a broad sweep of understanding,” Mazur says.

It depends on only two things. The first is the degree of the polynomial that defines the curve—the higher the degree is, the weaker the statement becomes. The second thing the formula depends on is called the “Jacobian variety,” a special surface that can be constructed from any curve. Jacobian varieties are interesting in their own right, and the formula offers a tantalizing path for studying them as well.”

Since ancient Greece, researchers have tried to isolate special rational points on curves. Now they have the first ever formula that applies uniformly to all curves.

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