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.
Unleash the power of quantum computing with The Classiq Platform. Simplify circuit design, optimize algorithms, and access over 4,000 executed circuits for free. Join the quantum revolution today!
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.
Over the centuries, the poem was copied onto countless other tablets in both the original Sumerian language as well as Akkadian.
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.
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.
How the brain adjusts connections between #neurons during learning: this new insight may guide further research on learning in brain networks and may inspire faster and more robust learning #algorithms in #artificialintelligence.
Researchers from the MRC Brain Network Dynamics Unit and Oxford University’s Department of Computer Science have set out a new principle to explain how the brain adjusts connections between neurons during learning. This new insight may guide further research on learning in brain networks and may inspire faster and more robust learning algorithms in artificial intelligence.
The essence of learning is to pinpoint which components in the information-processing pipeline are responsible for an error in output. In artificial intelligence, this is achieved by backpropagation: adjusting a model’s parameters to reduce the error in the output. Many researchers believe that the brain employs a similar learning principle.
However, the biological brain is superior to current machine learning systems. For example, we can learn new information by just seeing it once, while artificial systems need to be trained hundreds of times with the same pieces of information to learn them. Furthermore, we can learn new information while maintaining the knowledge we already have, while learning new information in artificial neural networks often interferes with existing knowledge and degrades it rapidly.
In their public lecture at Perimeter on May 1, 2019, neuroscientist Anne M. Andrews and nanoscientist Paul S. Weiss outlined their scientific collaboration and explained the importance of communicating across disciplines to target significant problems. \
\
Perimeter Institute (charitable registration number 88,981 4323 RR0001) is the world’s largest independent research hub devoted to theoretical physics, created to foster breakthroughs in the fundamental understanding of our universe, from the smallest particles to the entire cosmos. The Perimeter Institute Public Lecture Series is made possible in part by the support of donors like you. Be part of the equation: https://perimeterinstitute.ca/inspiri…\
\
Subscribe for updates on future live webcasts, events, free posters, and more: https://insidetheperimeter.ca/newslet…\
\
facebook.com/pioutreach \
twitter.com/perimeter \
instagram.com/perimeterinstitute \
Donate: https://perimeterinstitute.ca/give-today
Alter 3 has just been unveiled by the University of Tokyo and its powered by GPT-4, capable of human-like activities and interpreting verbal instructions. Researchers at the Technical University of Munich developed a self-aware robot with proprioception, enhancing its movement and interaction capabilities. The University of Southern California introduced RoboCLIP, an algorithm that trains robots to perform tasks in new environments with minimal instruction. Intel Labs and partners created advanced motor control for robots using hierarchical generative models, significantly improving their ability to perform complex tasks.\
\
Deep Learning AI Specialization: https://imp.i384100.net/GET-STARTED\
AI Marketplace: https://taimine.com/\
\
AI news timestamps:\
0:00 Alter 3 GPT4 powered AI robot\
1:31 Robot self awareness\
3:30 RoboCLIP\
5:22 Motor control for autonomous robots\
\
#ai #robot #technology
