quantum electrodynamics (QED)

Simulating Effective QED on Quantum Computers

In recent years simulations of chemistry and condensed materials has emerged as one of the preeminent applications of quantum computing, offering an exponential speedup for the solution of the electronic structure for certain strongly correlated […]

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Twist, a new language for Quantum Computing

Scientists from MIT‘s Computer Science and Artificial Intelligence (CSAIL) created their own programming language for Quantum Computing called Twist. Twist can describe and verify which pieces of data are entangled in a quantum program, through […]

Google Quantum Summer Symposium 2021

The Quantum Summer Symposium (QSS) is Google Quantum AI’s annual conference. It brings together experts in academia, industry and government to discuss progress in quantum computing research. This year’s event is virtual. If you are […]

Overview of DRL for our quantum architecture search framework

Quantum Architecture Search via Deep Reinforcement Learning

Recent advances in Quantum Computing have drawn considerable attention to building realistic application for and using quantum computers. However, designing a suitable quantum circuit architecture requires expert knowledge. For example, it is non-trivial to design […]

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 […]

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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 […]

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 enhance quantum machine learning algorithms

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 […]