Lifeboat Foundation

Year 2014 Basically once the master qubit is found it could even lead to a sorta master algorithm. Also it could show who actually pulls the strings of reality.

Whatever the u-bit is, it rotates quickly (Image: Natalie Nicklin)

Our best theory of nature has imaginary numbers at its heart. Making quantum physics more real conjures up a monstrous entity pulling the universe’s strings

Continue reading “From i to u: Searching for the quantum master bit” »

A new, potentially revolutionary artificial intelligence framework called “Blackout Diffusion” generates images from a completely empty picture, meaning that the machine-learning algorithm, unlike other generative diffusion models, does not require initiating a “random seed” to get started. Blackout Diffusion, presented at the recent International Conference on Machine Learning (“Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces”), generates samples that are comparable to the current diffusion models such as DALL-E or Midjourney, but require fewer computational resources than these models.

“Generative modeling is bringing in the next industrial revolution with its capability to assist many tasks, such as generation of software code, legal documents and even art,” said Javier Santos, an AI researcher at Los Alamos National Laboratory and co-author of Blackout Diffusion. “Generative modeling could be leveraged for making scientific discoveries, and our team’s work laid down the foundation and practical algorithms for applying generative diffusion modeling to scientific problems that are not continuous in nature.”

A new generative AI model can create images from a blank frame. (Image: Los Alamos National Laboratory)

It is still unclear whether and how quantum computing might prove useful in solving known large-scale classical machine learning problems. Here, the authors show that variants of known quantum algorithms for solving differential equations can provide an advantage in solving some instances of stochastic gradient descent dynamics.

In fields such as physics and engineering, partial differential equations (PDEs) are used to model complex physical processes to generate insight into how some of the most complicated physical and natural systems in the world function.

To solve these difficult equations, researchers use high-fidelity numerical solvers, which can be very time consuming and computationally expensive to run. The current simplified alternative, data-driven surrogate models, compute the goal property of a solution to PDEs rather than the whole solution. Those are trained on a set of data that has been generated by the high-fidelity solver, to predict the output of the PDEs for new inputs. This is data-intensive and expensive because complex physical systems require a large number of simulations to generate enough data.

In a new paper, “Physics-enhanced deep surrogates for partial differential equations,” published in December in Nature Machine Intelligence, a new method is proposed for developing data-driven surrogate models for complex physical systems in such fields as mechanics, optics, thermal transport, fluid dynamics, physical chemistry, and climate models.

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German researchers are developing an algorithm to help decode ancient cuneiform tablets — including those containing the oldest known work of world literature.

Ancient poem: The Epic of Gilgamesh is a Babylonian poem first written in cuneiform characters on clay tablets around 4,000 years ago. It tells the story of Gilgamesh, the king of the city of Uruk, and his quest for immortality.

Continue reading “AI is helping decode the oldest story in the world” »

The required precision to perform quantum simulations beyond the capabilities of classical computers imposes major experimental and theoretical challenges. The key to solving these issues are highly precise ways of characterizing analog quantum sim ulators. Here, we robustly estimate the free Hamiltonian parameters of bosonic excitations in a superconducting-qubit analog quantum simulator from measured time-series of single-mode canonical coordinates. We achieve the required levels of precision in estimating the Hamiltonian parameters by maximally exploiting the model structure, making it robust against noise and state-preparation and measurement (SPAM) errors. Importantly, we are also able to obtain tomographic information about those SPAM errors from the same data, crucial for the experimental applicability of Hamiltonian learning in dynamical quantum-quench experiments. Our learning algorithm is highly scalable both in terms of the required amounts of data and post-processing. To achieve this, we develop a new super-resolution technique coined tensorESPRIT for frequency extraction from matrix time-series. The algorithm then combines tensorESPRIT with constrained manifold optimization for the eigenspace reconstruction with pre-and post-processing stages. For up to 14 coupled superconducting qubits on two Sycamore processors, we identify the Hamiltonian parameters — verifying the implementation on one of them up to sub-MHz precision — and construct a spatial implementation error map for a grid of 27 qubits. Our results constitute a fully characterized, highly accurate implementation of an analog dynamical quantum simulation and introduce a diagnostic toolkit for understanding, calibrating, and improving analog quantum processors.

Submitted 18 Aug 2021 to Quantum Physics [quant-ph]

Subjects: quant-ph cond-mat.quant-gas physics.comp-ph.

Join the discussion on this paper page.

How can artificial intelligence help to improve the accuracy of lung cancer screening among people at high risk of developing the disease? Read to find out.

Lung cancers, the vast majority of which are caused by cigarette smoking, are the leading cause of cancer-related deaths in the United States. Lung cancer kills more people than cancers of the breast, prostate, and colon combined. By the time lung cancer is diagnosed, the disease has often already spread outside the lung. Therefore, researchers have sought to develop methods to screen for lung cancer in high-risk populations before symptoms appear. They are evaluating whether the integration of artificial intelligence – the use of computer programs or algorithms that use data to make decisions or predictions – could improve the accuracy and speed of diagnosis, aid clinical decision-making, and lead to better health outcomes.