Quantum Programming News
Collateral Debt Obligation (CDO) has been one of the most commonly used structured financial products and is intensively studied in quantitative finance. By setting the asset pool into different tranches, it effectively works out and
Arline announced the release of Arline Benchmarks which is the first open-source automated benchmarking platform for quantum compilers! The name was given to the honour of Arline Greenbaum, the first wife of Richard Feynman, one
Researchers at Google’s AI Quantum team have used a quantum computer to simulate a chemical reaction, the Hartree-Fock approximation of a real chemical system, for the first time. The reaction is a simple one, but this marks
BBVA is closely following through various lines of research aimed at exploring applications of quantum computing in the world of finance. As part of this work, and in collaboration with Spanish startup Multiverse, the joint research team
Scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have developed a new algorithm based on reinforcement learning to find the optimal parameters for the Quantum Approximate Optimization Algorithm (QAOA). QAOA allows a quantum computer
Researchers at Chicago Quantum continue to investigate the use of quantum computers in Finance. They have now looked for building an optimal portfolio out of a universe of 60 U.S. listed, liquid equities. Starting from
Researchers at the University of Sussex have created an algorithm that speeds up the rate of calculations in the early quantum computers which are currently being developed. They have created a new way to route
Researchers at IBM T. J. Watson Research Center discovered unexpectedly a new mathematical proof of quantum advantage – the elusive threshold at which quantum computers outperform classical machines in certain use cases. They study shows
Computer scientists at UCLA have shown that existing compilers, which tell quantum computers how to use their circuits to execute quantum programs, inhibit the computers’ ability to achieve optimal performance. Specifically, their research has revealed
Boltzmann machine is a powerful tool for modeling probability distributions that govern the training data. A thermal equilibrium state is typically used for Boltzmann machine learning to obtain a suitable probability distribution. The Boltzmann machine
