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Computer Modeling

“From Kindergarten to Complex Tasks: How ‘Basic Skills’ Enhance AI Learning”

We need to learn our letters before we can learn to read and our numbers before we can learn how to add and subtract. The same principles are true with AI, a team of scientists has shown through laboratory experiments and computational modeling. In their work, researchers found that when recurrent neural networks (RNNs) are first trained on simple cognitive tasks, they are better equipped to handle more difficult and complex ones later on.

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From Kindergarten to Complex Tasks: How ‘Basic Skills’ Enhance AI Learning

Imagine you’re trying to learn a new language or how to play a musical instrument. You wouldn’t start with the intricacies of grammar rules or complicated melodies, would you? Instead, you’d begin with the basics – learning your alphabet, simple sentences, and basic chords. The same principles are true for artificial intelligence (AI), as a team of researchers from New York University has demonstrated through laboratory experiments and computational modeling.

In their work, published in the journal Nature Machine Intelligence, the scientists found that when recurrent neural networks (RNNs) are first trained on simple cognitive tasks, they are better equipped to handle more difficult and complex ones later on. They labeled this form of training ‘kindergarten curriculum learning’ as it centers on first instilling an understanding of basic tasks and then combining knowledge of these tasks in carrying out more challenging ones.

“From very early on in life, we develop a set of basic skills like maintaining balance or playing with a ball,” explains Cristina Savin, an associate professor in NYU’s Center for Neural Science and Center for Data Science. “With experience, these basic skills can be combined to support complex behavior – for instance, juggling several balls while riding a bicycle.”

The researchers took these principles to enhance the capabilities of RNNs, which first learn a series of easy tasks, store this knowledge, and then apply a combination of these learned tasks to successfully complete more sophisticated ones. This approach proved to be effective in improving the performance of RNNs in complex cognitive tasks.

To test their theory, the scientists conducted a series of experiments with laboratory rats. The animals were trained to seek out a water source in a box with several compartmentalized ports. However, in order to know when and where the water would be available, the rats needed to learn that delivery of the water was associated with certain sounds and the illumination of the port’s lights – and that the water was not delivered immediately after these cues.

In order to reach the water, then, the animals needed to develop basic knowledge of multiple phenomena (e.g., sounds precede water delivery, waiting after the visual and audio cues before trying to access the water) and then learn to combine these simple tasks in order to complete a goal (water retrieval). These results pointed to principles of how the animals applied knowledge of simple tasks in undertaking more complex ones.

The scientists took these findings to train RNNs in a similar fashion – but, instead of water retrieval, the RNNs managed a wagering task that required these networks to build upon basic decision making in order to maximize the payoff over time. They then compared this kindergarten curriculum learning approach to existing RNN-training methods.

Overall, the team’s results showed that the RNNs trained on the kindergarten model learned faster than those trained on current methods. “AI agents first need to go through kindergarten to later be able to better learn complex tasks,” observes Savin. “Overall, these results point to ways to improve learning in AI systems and call for developing a more holistic understanding of how past experiences influence learning of new skills.”

This research was funded by grants from the National Institute of Mental Health (1R01MH125571-01, 1K01MH132043-01A1) and conducted using research computing resources of the Empire AI consortium, with support from the State of New York, the Simons Foundation, and the Secunda Family Foundation.

Computer Modeling

Unveiling the Hidden Power of Quantum Computers: Scientists Discover Forgotten Particle that Could Unlock Universal Computation

Scientists may have uncovered the missing piece of quantum computing by reviving a particle once dismissed as useless. This particle, called the neglecton, could give fragile quantum systems the full power they need by working alongside Ising anyons. What was once considered mathematical waste may now hold the key to building universal quantum computers, turning discarded theory into a pathway toward the future of technology.

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The discovery of the “neglecton” particle, previously discarded in traditional approaches to topological quantum computation, has brought scientists closer to unlocking the full power of quantum computers. This new anyon emerges naturally from a broader mathematical framework and provides exactly the missing ingredient needed to complete the computational toolkit.

In a study published in Nature Communications, a team of mathematicians and physicists led by Aaron Lauda, professor of mathematics, physics, and astronomy at the USC Dornsife College of Letters, Arts, and Sciences, has demonstrated that Ising anyons can be made universal through braiding alone when combined with the newly discovered neglecton particle.

The breakthrough illustrates how abstract mathematics can solve concrete engineering problems in unexpected ways. By embracing mathematical structures previously considered useless, researchers have unlocked a whole new chapter for quantum information science.

“This work moves us closer to universal quantum computing with particles we already know how to create,” Lauda said. “The math gives a clear target: If experimentalists can find a way to realize this extra stationary anyon, it could unlock the full power of Ising-based systems.”

The research opens new directions both in theory and in practice, with mathematicians working to extend their framework to other parameter values and clarify the role of unitarity in non-semisimple TQFTs. Experimentalists aim to identify specific material platforms where the stationary neglecton could arise and develop protocols that translate their braiding-based approach into realizable quantum operations.

The study was supported by National Science Foundation Grants, Army Research Office Grants, Simons Foundation Collaboration Grant, and PSC CUNY Enhanced Award. The team of researchers includes Filippo Iulianelli, Sung Kim, and Joshua Sussan, among others.

In conclusion, the discovery of the neglecton particle has brought scientists closer to unlocking the full power of quantum computers, offering new directions in theory and practice, and highlighting the potential for abstract mathematics to solve concrete engineering problems.

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Computer Graphics

Cracking the Code: Scientists Breakthrough in Quantum Computing with a Single Atom

A research team has created a quantum logic gate that uses fewer qubits by encoding them with the powerful GKP error-correction code. By entangling quantum vibrations inside a single atom, they achieved a milestone that could transform how quantum computers scale.

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Scientists have achieved a major breakthrough in quantum computing by successfully cracking the code hidden within a single atom. To build a large-scale quantum computer that works, scientists and engineers need to overcome the spontaneous errors that quantum bits, or qubits, create as they operate.

The team at the Quantum Control Laboratory at the University of Sydney Nano Institute has demonstrated a type of quantum logic gate that drastically reduces the number physical qubits needed for its operation. They built an entangling logic gate on a single atom using an error-correcting code nicknamed the ‘Rosetta stone’ of quantum computing.

This curiously named Gottesman-Kitaev-Preskill (GKP) code has long offered a theoretical possibility for significantly reducing the physical number of qubits needed to produce a functioning ‘logical qubit.’ Albeit by trading efficiency for complexity, making the codes very difficult to control. The research published in Nature Physics demonstrates this as a physical reality.

Led by Sydney Horizon Fellow Dr Tingrei Tan at the University of Sydney Nano Institute, scientists have used their exquisite control over the harmonic motion of a trapped ion to bridge the coding complexity of GKP qubits, allowing a demonstration of their entanglement.

The team’s experiment has shown the first realization of a universal logical gate set for GKP qubits. They did this by precisely controlling the natural vibrations or harmonic oscillations of a trapped ion in such a way that they can manipulate individual GKP qubits or entangle them as a pair.

A logic gate is an information switch that allows computers – quantum and classical – to be programmable to perform logical operations. Quantum logic gates use the entanglement of qubits to produce a completely different sort of operational system to that used in classical computing, underpinning the great promise of quantum computers.

The researchers have effectively stored two error-correctable logical qubits in a single trapped ion and demonstrated entanglement between them using quantum control software developed by Q-CTRL. This result massively reduces the quantum hardware required to create these logic gates, which allow quantum machines to be programmed.

This research represents an important demonstration that quantum logic gates can be developed with a reduced physical number of qubits, increasing their efficiency. The authors declare no competing interests. Funding was received from various sources including the Australian Research Council and private funding from H. and A. Harley.

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Civil Engineering

A Groundbreaking Magnetic Trick for Quantum Computing: Stabilizing Qubits with Exotic Materials

Researchers have unveiled a new quantum material that could make quantum computers much more stable by using magnetism to protect delicate qubits from environmental disturbances. Unlike traditional approaches that rely on rare spin-orbit interactions, this method uses magnetic interactions—common in many materials—to create robust topological excitations. Combined with a new computational tool for finding such materials, this breakthrough could pave the way for practical, disturbance-resistant quantum computers.

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The article you provided was well-written, but I made some adjustments to improve clarity, structure, and style for general readers. Here’s the rewritten content:

A Groundbreaking Magnetic Trick for Quantum Computing: Stabilizing Qubits with Exotic Materials

Quantum computers have long been touted as revolutionaries in solving complex problems that conventional supercomputers can’t handle. However, their development has been hindered by one major challenge: qubits, the basic units of quantum computers, are extremely delicate and prone to losing their quantum states due to external disturbances.

Researchers from Chalmers University of Technology in Sweden and Aalto University and the University of Helsinki in Finland have now made a groundbreaking discovery that could change this. They’ve developed a new type of exotic quantum material that exhibits robust topological excitations, which are significantly more stable and resilient than other quantum states.

This breakthrough is an important step towards realising practical topological quantum computing by constructing stability directly into the material’s design. The researchers’ innovative approach uses magnetism as the key ingredient to achieve this effect, harnessing magnetic interactions to engineer robust topological excitations in a broader spectrum of materials.

“The advantage of our method is that magnetism exists naturally in many materials,” explains Guangze Chen, postdoctoral researcher in applied quantum physics at Chalmers and lead author of the study published in Physical Review Letters. “You can compare it to baking with everyday ingredients rather than using rare spices. This means that we can now search for topological properties in a much broader spectrum of materials, including those that have previously been overlooked.”

To accelerate the discovery of new materials with useful topological properties, the research team has also developed a new computational tool that can directly calculate how strongly a material exhibits topological behavior.

“Our hope is that this approach can help guide the discovery of many more exotic materials,” says Guangze Chen. “Ultimately, this can lead to next-generation quantum computer platforms, built on materials that are naturally resistant to the kind of disturbances that plague current systems.”

This magnetic trick has the potential to revolutionize the development of practical topological quantum computing and pave the way for next-generation quantum computer platforms. As researchers continue to explore and develop new exotic materials with robust topological excitations, we may finally see the dawn of a new era in quantum computing.

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