Schematic Diagram of dual-PQC GAN to reproduce images of 2n pixels.
Cambridge Quantum Computing (CQC) and CERN have posted an article on Arxiv proposing the dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Network (GAN) for generative modelling applications in High-Energy Physics (specifically for generating typical calorimeter images). […]
IBM Quantum and Qiskit team announced the IBM Quantum Developer Certification — the world’s first ever developer certification for programming a quantum computer. The purpose of this test, a 60-questions exam, is to certify that the members […]
DWave, Multiverse, BBVA business case
Using the D-Wave hybrid solver service, Multiverse Computing developed an algorithmic approach to rapidly generate portfolios that can be optimized against a variety of constraints. Every investment entails some measure of risk—the fundamental question is whether the reward […]
Except for Value at Risk and option pricing, quantum algorithm are not often used to solve problems in finance. Indeed, such analysis are computationally too expensive to perform on classical computers. In this study, authors […]
An example comparison of compilation strategies (fixed colours) when used on application-motivated circuits run on a real device.
Benchmarking of Quantum Computing devices is necessary to measure their performance, and to guide their use and future development. As Quantum Computing systems become more complex, they need to be benchmarked in terms of practical […]
A barren plateau is a trainability problem that occurs in machine learning optimization algorithms when the problem-solving space turns flat as the algorithm is run. Researchers at Los Alamos National Laboratory have developed theorems to prove that any given algorithm will avoid a barren plateau as it scales up to run on a quantum computer. Credit: Los Alamos National Laboratory March 19, 2021
Many machine learning algorithms on quantum computers suffer from the dreaded “barren plateau” of unsolvability, where they run into dead ends on optimization problems. Researchers at Los Alamos National Laboratory have established theorems that guarantee […]
Iterative quantum amplitude estimation
A team of researchers at IBM Quantum and ETH, Switzerland, has introduced a variant of Quantum Amplitude Estimation (QAE), called Iterative QAE (IQAE), which does not rely on Quantum Phase Estimation (QPE) but is only based on Grover’s Algorithm, which reduces […]
Comparison of the new quantum circuit with our previous one Credit: Kenji Sugisaki, Takeji Takui, Kazunobu Sato
A team of researchers at Osaka City University has developed a quantum algorithm they called Bayesian eXchange coupling parameter calculator with Broken-symmetry wave functions (BxB), that predicts the electronic states of atoms and molecules by […]
Illustration of a Restricted Boltzmann Machine (RBM) bipartite graph where viviv_i are visible nodes, hjhjh_j are hidden nodes and wijwijw_{ij} are the weights connecting the hidden and visible nodes.
Researchers at Florida State University found a way to automatically infer parameters used in an important quantum Boltzmann machine algorithm for machine learning applications. The work could help build artificial neural networks that could be used […]
Cambridge Quantum Computing (CQC) announces the publication of a research paper on the online pre-print repository arxiv (available here) that provides details of the largest ever experimental implementation of Natural Language Processing (NLP) tasks on a […]
