The field of quantum computing is rapidly advancing, but effectively measuring this progress has become a significant challenge. Researchers Tobias Rohe, Federico Halberstädter Ruiloba, and Sabrina ...

Sparse matrices are fundamental to many computational problems, and their efficient representation in quantum computing is crucial for unlocking the potential of quantum algorithms. The core ...

Quantum computers that were previously limited to theoretical physics now learn to discover patterns, optimize decisions and ...

A line of engineering research seeks to develop computers that can tackle a class of challenges called combinatorial optimization problems. These are common in real-world applications such as ...

Moving Past “It Works” — Intelligent Optimization Is the Key to PCB Excellence Adaptive optimization algorithms promise better results and deeper insight into why certain designs perform better than ...

Zhenzhen Zhang, Zhixing Luo, Hu Qin, Andrew Lim, Exact Algorithms for the Vehicle Routing Problem with Time Windows and Combinatorial Auction, Transportation Science, Vol. 53, No. 2 (March–April 2019) ...

While large language models (LLMs) have achieved remarkable success through "scaling laws," their power diminishes in complex, data-scarce domains. A new Perspective article in National Science Review ...

In essence, quantum computing relies first on reducing the physical error rate of the qubits, by employing a specific qubit system, and then on an error correction scheme to mitigate the physical ...

A line of engineering research seeks to develop computers that can tackle a class of challenges called combinatorial optimization problems. These are common in real-world applications such as ...

S&P 500, Microsoft Corporation, International Business Machines, Honeywell International Inc. Read 's Market Analysis on Investing.com ...