Dutch quantum computing startup Fermioniq has announced that its quantum emulation product, Ava, is the first third-party tensor network simulator to be available through the NVIDIA CUDA-Q platform. CUDA-Q users will be able to leverage the powerful emulation capabilities of Ava to run their quantum programs, accessing the platform via the cloud alongside real quantum hardware.

A group of South Korean researchers has successfully developed an integrated quantum circuit chip using photons (light particles). This achievement is expected to enhance the global competitiveness of the team in quantum computation research. Electronics and Telecommunications Research Institute (ETRI) announced that they have developed a system capable of controlling eight photons using a photonic integrated-circuit chip. With this system, they can explore various quantum phenomena, such as multipartite entanglement resulting from the interaction of the photons.

IonQ, a leader in the quantum computing and networking industry, announced today the issuance of five new U.S. patents designed to deliver benefits across multiple industries and applications. With the pending acquisition of Qubitekk’s 118 patents, IonQ will have a total of over 600 U.S. and international issued and pending patents, standing apart from similarly-sized quantum companies based on its strength of IP protection and extensive combination of patents across different verticals.

A recent study by researchers from CSIRO and the University of Melbourne has made progress in quantum machine learning, a field aimed at achieving quantum advantage to outperform classical machine learning. Their work demonstrates that quantum circuits for data encoding in quantum machine learning can be greatly simplified without compromising accuracy or robustness. This research was published Sept.12 in Intelligent Computing, a Science Partner Journal.

Rigetti, a pioneer in full-stack quantum-classical computing, announced the successful demonstration of real-time and low latency quantum error correction on a Rigetti quantum computer.

Using the hybrid approach, researchers at Los Alamos National Laboratory proposed a specific realization of Grover’s algorithm. As one of the best-known quantum algorithms, Grover’s algorithm allows unstructured searches of large data sets that gobble up conventional computing resources.

QunaSys, a global leader in quantum software development, announced today the launch of QURI SDK, an innovative research and development platform designed for the fault-tolerant quantum computing (FTQC) era. As quantum technology advances, there is an increasing interest in researching FTQC algorithms. However, substantial technical demands have created considerable obstacles to accessing this technology. QURI SDK aims to overcome these challenges and promote the practical use of FTQC by providing a comprehensive research platform that integrates the latest architectural and algorithmic advancements.

With the recent development in quantum hardware, it has become feasible to physically swap qubits during computation (known as “physical SWAPs”). These operations are believed to be crucial for the scalability of quantum computers. The ParityQC team’s new publication, “Runtime Reduction in Linear Quantum Charge-Coupled Devices using the Parity Flow Formalism” argues that this is not the case. The authors (Federico Domínguez, Michael Fellner, Berend Klaver, Stefan Rombouts, Christian Ertler, Wolfgang Lechner) show that the ParityQC Architecture enables the implementation of quantum algorithms without the need for SWAP operations.

A team of researchers, led by scientist Lin Zhou of Ames National Laboratory, has made important progress towards understanding the role of surface oxides in improving quantum computing circuits performance. Surface oxides are a primary cause of decoherence, or loss of quantum properties in quantum circuits. The team is part of a larger effort by the Superconducting Quantum Materials and Systems Center (SQMS) to improve quantum computers.

A large collaboration of scientists, led by Giuseppe Carleo at EPFL has now developed a new benchmark called the "V-score" to tackle this issue. The V-score ("V" for "Variational Accuracy") offers a consistent way to compare how well different quantum methods perform on the same problem. The V-score can be used to identify the hardest-to-solve quantum systems, where current computational methods struggle, and where future methods—such as quantum computing—might offer an advantage.