Microchips power almost every modern device — phones, laptops and even fridges. But behind the scenes, making them is a complex process. But researchers say they have found a way to tap into the power ...

The computational demands of today’s AI systems are starting to outpace what classical hardware can deliver. How can we fix this? One possible solution is quantum machine learning (QML). QML ...

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...

This collection supports and amplifies research related to SDG 9 - Industry, innovation and infrastructure. Quantum Machine Learning is currently listed as one of the most promising candidates for ...

Integrating quantum computing into AI doesn’t require rebuilding neural networks from scratch. Instead, I’ve found the most effective approach is to introduce a small quantum block—essentially a ...

Telstra has completed a trial with Silicon Quantum Computing (SQC) that sought to apply quantum machine learning to boost network automation. The 12-month trial saw the pair leverage Watermelon, SQC’s ...

This illustration draws a parallel between quantum state tomography and natural language modeling. In quantum tomography, structured measurements yield probability outcomes that are aggregated to ...

Reservoir computing is a promising machine learning-based approach for the analysis of data that changes over time, such as weather patterns, recorded speech or stock market trends. Classical ...

The Seeker quantum processor from Quantum Circuits now supports Nvidia's CUDA-Q, enabling developers to combine quantum computing with AI and machine learning. Quantum Circuits announced that its dual ...

IonQ today laid out its five-year roadmap for trapped ion quantum computers. The company plans to deploy rack-mounted modular quantum computers small enough to be networked together in a datacenter by ...