January 19, 2025
Federated Quantum Machine Learning

Federated Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen […]

Nvidia logo

Nvidia announces its Quantum Stack cuQuantum

Nvidia has just announced at their GTC event about the performance of quantum simulators using the DGX A100 and its own custom-cooked quantum development software stack, called cuQuantum. The thing is, most of the quantum […]

Pablo Bonilla Ataides (left) with co-author Dr Ben Brown from the School of Physics. Credit: Louise Cooper

Student’s homework picked up by Amazon

University of Sydney science undergraduate Pablo Bonilla Ataides has tweaked some computing code to effectively double its capacity to correct errors in the quantum computers. This homework has attracted the attention of quantum computing programmers at Amazon […]

IBM Quantum Developer Certification

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

Quantum algorithm to generate optimized portfolios

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 […]

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

New step in Quantum Machine Learning

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

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 […]