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 […]
Reinforcement learning to train quantum algorithm
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 […]
Portfolio optimization of 60 stocks using Quantum algorithms
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 […]
Predicting computational power of quantum computers
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 […]
Clifford Group investigation reveals new Quantum Advantage proof
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 […]
New benchmarks to optimize quantum computer performance
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 learning with a variational quantum algorithm
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 […]
MoG-VQE: Multiobjective Genetic Variational Quantum Eigensolver
Variational quantum eigensolver (VQE) emerged as a first practical algorithm for near-term quantum computers. Its success largely relies on the chosen variational ansatz, corresponding to a quantum circuit that prepares an approximate ground state of […]
First quantum algorithm to characterize noise
Researchers at University of Sydney Nano Institute have developed the first system-wide quantum algorithm to characterize noise. Noise is the main obstacle to building large-scale quantum computers. To tame the noise (interference or instability), scientists […]
Quantum simulation for solving multidimensional Poisson equations
Many methods solve Poisson equations by using grid techniques which discretize the problem in each dimension. Most of these algorithms are subject to the curse of dimensionality, so that they need exponential runtime. Researchers at […]
