Artificial intelligence (AI) is used in numerous fields of science, yet the initial research questions and targets are still almost always provided by human researchers. AI-generated creative ideas in science are rare and often vague, so that it remains a human task to execute them. Automating idea generation and implementation in one coherent system would significantly shift the role of humans in the scientific process. Here we present AI-Mandel, an LLM agent that can generate and implement ideas in quantum physics. AI-Mandel formulates ideas from the literature and uses a domain-specific AI tool to turn them into concrete experiment designs that can readily be implemented in laboratories. The generated ideas by AI-Mandel are often scientifically interesting - for two of them we have already written independent scientific follow-up papers. The ideas include new variations of quantum teleportation, primitives of quantum networks in indefinite causal orders, and new concepts of geometric phases based on closed loops of quantum information transfer. AI-Mandel is a prototypical demonstration of an AI physicist that can generate and implement concrete, actionable ideas. Building such a system is not only useful to accelerate science, but it also reveals concrete open challenges on the path to human-level artificial scientists.

Scientist claim they achieved a massive breakthrough in teleportation by beaming data between quantum computers. Researchers at the University of Oxford successfully teleported logical gates - the basic components of a computer algorithm - between two quantum processors separated by more than six feet. Using particles of light (or photons), the scientists were able to form a shared quantum link between the two separate devices. This allowed two processors to work remotely, sharing the same algorithm to complete their computing tasks. The breakthrough may solve the'scalability problem' that has plagued the construction of usable quantum computers.

Federated learning enables collaborative model training across multiple clients without sharing raw data, thereby enhancing privacy. However, the exchange of model updates can still expose sensitive information. Quantum teleportation, a process that transfers quantum states between distant locations without physical transmission of the particles themselves, has recently been implemented in real-world networks. This position paper explores the potential of integrating quantum teleportation into federated learning frameworks to bolster privacy. By leveraging quantum entanglement and the no-cloning theorem, quantum teleportation ensures that data remains secure during transmission, as any eavesdropping attempt would be detectable. We propose a novel architecture where quantum teleportation facilitates the secure exchange of model parameters and gradients among clients and servers. This integration aims to mitigate risks associated with data leakage and adversarial attacks inherent in classical federated learning setups. We also discuss the practical challenges of implementing such a system, including the current limitations of quantum network infrastructure and the need for hybrid quantum-classical protocols. Our analysis suggests that, despite these challenges, the convergence of quantum communication technologies and federated learning presents a promising avenue for achieving unprecedented levels of privacy in distributed machine learning.

Quantum computing promises to revolutionize machine learning, offering significant efficiency gains in tasks such as clustering and distance estimation. Additionally, it provides enhanced security through fundamental principles like the measurement postulate and the no-cloning theorem, enabling secure protocols such as quantum teleportation and quantum key distribution. While advancements in secure quantum machine learning are notable, the development of secure and distributed quantum analogues of kernel-based machine learning techniques remains underexplored. In this work, we present a novel approach for securely computing common kernels, including polynomial, radial basis function (RBF), and Laplacian kernels, when data is distributed, using quantum feature maps. Our methodology introduces a robust framework that leverages quantum teleportation to ensure secure and distributed kernel learning. The proposed architecture is validated using IBM's Qiskit Aer Simulator on various public datasets.

From Santa Barbara, California, to Hefei, China, scientists are developing a new kind of computer that will make today's machines look like toys. Harnessing the mysterious powers of quantum mechanics, the technology will perform tasks in minutes that even supercomputers could not complete in thousands of years. In the fall of 2019, Google unveiled an experimental quantum computer showing this was possible. Two years later, a lab in China did much the same. But quantum computing will not reach its potential without help from another technological breakthrough.

The digital revolution highlights the need for awareness of quantum technologies. The world is now preparing for further digital transformation with a revolution in quantum technology. The countries that authorize quantum computing technology will command over the information processing space for a long period giving them control and influence over sectors such as modern manufacturing, pharmaceuticals, the digital economy, logistics, national security, and intelligence. Quantum computing is a fundamentally different approach to computation compared with the kinds of calculations that we do on today's laptops, workstations, and mainframes. It won't replace these devices, but by leveraging the principles of quantum physics it will solve specific, typically very complex problems of a statistical nature that are difficult for current computers.

This was later perceived as a rallying cry for developing a quantum computer, leading to today's rapid progress in the search for quantum supremacy. The race for quantum supremacy is on, to being able to demonstrate a practical quantum device that can solve a problem that no classical computer can solve in any feasible amount of time. Speed--and sustainability--has always been the measure of the jump to the next stage of computing. In 1944, Richard Feynman, then a junior staff member at Los Alamos, organized a contest between human computers and the Los Alamos IBM facility, with both performing a calculation for the plutonium bomb. For two days, the human computers kept up with the machines.

While Star Trek-style teleportation is still a long way off, researchers have revealed a major breakthrough in the field of quantum travel. Two separate teams have transferred quantum information over several miles of fibre optic networks in an urban network. The results could lead to more secure bank accounts, a faster internet and possibly even open the door to the controversial idea of human teleportation. One of the potential applications for quantum teleportation is a network of quantum computers (illustrated) and a'quantum internet' that is far faster and much more secure than current networks When atoms are'entangled' the measurement of one of the atoms will not only cause it to'pick' one state, but it will also instantaneously cause the atoms it is entangled with to do the same, even if that atom has not been measured itself. This means we automatically know information about all the atoms that are entangled at once, just by measuring one, and it does not matter how far apart in space the two entangled atoms are.