Reimagining Nuclear Medicine — Dr. Stephen Moran, Ph.D., Global Program Head, Neuroendocrine Tumors & Other Radiosensitive Cancers, Advanced Accelerator Applications, Novartis
Dr. Stephen Moran, Ph.D., is Global Program Head, Neuroendocrine Tumors & Other Radiosensitive Cancers, for Advanced Accelerator Applications (AAA — https://www.adacap.com/), a Novartis company and also a member of the Oncology Development Unit Leadership Team at Novartis.
Researchers at Simon Fraser University have made a crucial breakthrough in the development of quantum technology.
Their research, published in Nature today, describes their observations of more than 150,000 silicon “T center” photon-spin qubits, an important milestone that unlocks immediate opportunities to construct massively scalable quantum computers and the quantum internet that will connect them.
Quantum computing has enormous potential to provide computing power well beyond the capabilities of today’s supercomputers, which could enable advances in many other fields, including chemistry, materials science, medicine and cybersecurity.
Senior Analyst, AI & Quantum Computing, Paul Smith-Goodson, dives in as in today’s world, many critical business decisions and customer-facing services rely on accurate machine learning insights. Today, he discusses Google’s latest A.I. benchmarking test dethroning NVIDIA’s.
NVIDIA introduces QODA, a new platform for hybrid quantum-classical computing, enabling easy programming of integrated CPU, GPU, and QPU systems.
The past decade has seen quantum computing leap out of academic labs into the mainstream. Efforts to build better quantum computers proliferate at both startups and large companies. And while it is still unclear how far we are away from using quantum advantage on common problems, it is clear that now is the time to build the tools needed to deliver valuable quantum applications.
To start, we need to make progress in our understanding of quantum algorithms. Last year, NVIDIA announced cuQuantum, a software development kit (SDK) for accelerating simulations of quantum computing. Simulating quantum circuits using cuQuantum on GPUs enables algorithms research with performance and scale far beyond what can be achieved on quantum processing units (QPUs) today. This is paving the way for breakthroughs in understanding how to make the most of quantum computers.
The potential of quantum computing can in no way be undermined today as it solves some of the most obstinate challenges from bringing down global warming to dramatically bringing down drug discovery time and much more. And with this, several companies are in a spree to bring up quantum computing capabilities.
Nvidia has announced a unified computing platform that will bring in an open environment across quantum processors and classical computers. The company said that the platform aims at speeding enhanced quantum research and development across Artificial Intelligence (AI), High Performance Computing (HPC), health, finance and other disciplines.
The company claims that Nvidia Quantum Optimized Device Architecture or QODA is a first-of-its-kind platform for hybrid quantum-classical computers and aims to make quantum computing more accessible by creating a comprehensive hybrid quantum-classical programming model.
When talking about quantum physics, people will often nonchalantly say that particles can be in two places at once. Physicist Sabine Hossenfelder explores what is actually going on.
Two independent groups have experimentally demonstrated surface-code quantum error correction—an approach for remedying errors in quantum computations.
The small robotic crab can walk, bend, twist, turn and jump The smallest-ever remote-controlled walking robot has been created by Northwestern University engineers, and it takes the shape of a tiny, cute peekytoe crab. The tiny crabs, which are about half a millimeter wide, can bend, twist, craw.
Brain-machine interfaces (BMIs) are devices that enable direct communication/translation between biological neuronal networks (e.g. a brain or a spine) and external machines. They are currently being used as a tool for fundamental neuroscience research and also for treating neurological disorders and for manipulating neuro-prosthetic devices. As remarkable as today’s BMIs are, however, the next generation BMIs will require new hardware and software with improved resolution and specificity in order to precisely monitor and control the activities of complex neuronal networks. In this talk, I will describe my group’s effort to develop new neuroelectronic devices enabled by silicon nanotechnology that can serve as high-precision, highly multiplexed interface to neuronal networks. I will then describe the promises, as well as potential pitfalls, of next generation BMIs. Hongkun Park is a Professor of Chemistry and Chemical Biology and a Professor of Physics at Harvard University. He is also an Institute Member of the Broad Institute of Harvard and MIT and a member of the Harvard Center for Brain Science and Harvard Quantum Optics Center. He serves as an associate editor of Nano Letters. His research interests lie in exploring solid-state photonic, optoelectronic, and plasmonic devices for quantum information processing as well as developing new nano-and microelectronic interfaces for living cells, cell networks, and organisms. Awards and honors that he received include the Ho-Am Foundation Prize in Science, NIH Director’s Pioneer Award, and the US Vannevar Bush Faculty Fellowship, the David and Lucile Packard Foundation Fellowship for Science and Engineering, the Alfred P. Sloan Research Fellowship, and the Camille Dreyfus Teacher-Scholar Award. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.
