Porting existing CPU applications to NVIDIA GPUs can unlock performance gains, enabling users to solve problems at a much greater scale and speed. While the process of adapting code for NVIDIA GPU acceleration requires an initial investment of time and effort, the resulting improvements in throughput and efficiency often far outweigh the costs. Such an undertaking may seem daunting and raise…
]]>As quantum computers scale, they will integrate with AI supercomputers to tackle some of the world’s most challenging problems. These accelerated quantum supercomputers will run applications leveraging the capabilities of CPUs, GPUs, and QPUs. With the NVIDIA CUDA-Q platform, users can easily perform research and develop applications that can seamlessly run on accelerated quantum…
]]>Computing is an essential tool for the modern financial services industry. Profits are won and lost based on the speed and accuracy of algorithms guiding financial decision making. Accelerated quantum computing has the potential to impact the financial services industry with new algorithms able to speed-up or enhance existing tools, such as portfolio optimization techniques.
]]>The NVIDIA CUDA-X math libraries empower developers to build accelerated applications for AI, scientific computing, data processing, and more. Two of the most important applications of CUDA-X libraries are training and inference LLMs, whether for use in everyday consumer applications or highly specialized scientific domains like drug discovery. Multiple CUDA-X libraries are indispensable…
]]>Noise is the notorious adversary of quantum computing. Qubits are sensitive to the slightest environmental perturbations, quickly causing errors to accumulate and make the results of even the simplest quantum algorithms too noisy to be meaningful. Quantum error correction (QEC) circumvents this problem by using many noisy physical qubits to encode logical qubits effectively immune to noise.
]]>Infleqtion, a world leader in neutral atom quantum computing, used the NVIDIA CUDA-Q platform to first simulate, and then orchestrate the first-ever demonstration of a material science experiment on logical qubits, on their Sqale physical quantum processing unit (QPU). Qubits, the basic units of information in quantum computing, are prone to errors, and far too unreliable to make meaningful…
]]>In the past decade, quantum computers have progressed significantly and could one day be used to undermine current cybersecurity practices. If run on a quantum computer, for example, an algorithm discovered by the theoretical computer scientist Peter Shor could crack common encryption schemes, including the Rivest-Shamir-Adleman (RSA) encryption algorithm. Post-quantum cryptography (PQC) is…
]]>As quantum computers scale, tasks such as controlling quantum hardware and performing quantum error correction become increasingly complex. Overcoming these challenges requires tight integration between quantum processing units (QPUs) and AI supercomputers, a paradigm known as accelerated quantum supercomputing. Increasingly, AI methods are being used by researchers up and down the quantum…
]]>In the wake of ever-growing power demands, power systems optimization (PSO) of power grids is crucial for ensuring efficient resource management, sustainability, and energy security. The Eastern Interconnection, a major North American power grid, consists of approximately 70K nodes (Figure 1). Aside from sheer size, optimizing such a grid is complicated by uncertainties such as catastrophic…
]]>Quantum dynamics describes how complex quantum systems evolve in time and interact with their surroundings. Simulating quantum dynamics is extremely difficult yet critical for understanding and predicting the fundamental properties of materials. This is of particular importance in the development of quantum processing units (QPUs), where quantum dynamics simulations enable QPU developers to…
]]>Accelerated quantum supercomputing combines the benefits of AI supercomputing with quantum processing units (QPUs) to develop solutions to some of the world’s hardest problems. Realizing such a device involves the seamless integration of one or more QPUs into a traditional CPU and GPU supercomputing architecture. An essential component of any accelerated quantum supercomputer is a programming…
]]>NVIDIA’s vision of accelerated quantum supercomputers integrates quantum hardware and AI supercomputing to turn today’s quantum processors into tomorrow’s useful quantum computing devices. At Supercomputing 2024 (SC24), NVIDIA announced a wave of projects with partners that are driving the quantum ecosystem through those challenges standing between today’s technologies and this accelerated…
]]>Improving sources of sustainable energy is a worldwide problem with environmental and economic security implications. Ying-Yi Hong, distinguished professor of Power Systems and Energy at Chung Yuan Christian University in Taiwan, researches hybrid quantum-classical methods. These approaches leverage quantum computing to solve challenging problems in power systems and sustainable energy.
]]>Quantum dynamics describe how objects obeying the laws of quantum mechanics interact with their surroundings, ultimately enabling predictions about how matter behaves. Accurate quantum dynamics simulations inform the development of new materials, solar cells, batteries, sensors, and many other cutting-edge technologies. They’re also a critical tool in designing and building useful quantum…
]]>AI techniques like large language models (LLMs) are rapidly transforming many scientific disciplines. Quantum computing is no exception. A collaboration between NVIDIA, the University of Toronto, and Saint Jude Children’s Research Hospital is bringing generative pre-trained transformers (GPTs) to the design of new quantum algorithms, including the Generative Quantum Eigensolver (GQE) technique.
]]>Quantum computers can use the quantum properties of superposition, entanglement, and interference to generalize learnings and insights from data. Such quantum machine learning (QML) techniques will eventually run on quantum-accelerated supercomputers that combine the processing powers of CPUs, GPUs, and QPUs to solve some of the world’s most complex problems. Many QML algorithms offer…
]]>NVIDIA CUDA-Q (formerly NVIDIA CUDA Quantum) is an open-source programming model for building hybrid-quantum classical applications that take full advantage of CPU, GPU, and QPU compute abilities. Developing these applications today is challenging and requires a flexible, easy-to-use coding environment coupled with powerful quantum simulation capabilities to efficiently evaluate and improve the…
]]>The previous post How to Accelerate Quantitative Finance with ISO C++ Standard Parallelism demonstrated how to write a Black-Scholes simulation using ISO C++ standard parallelism with the code found in the /NVIDIA/accelerated-quant-finance GitHub repo. This approach enables you to productively write code that is both concise and portable. Using solely standard C++, it’s possible to write an…
]]>NVIDIA CUDA-Q (formerly NVIDIA CUDA Quantum) is an open-source programming model for building quantum accelerated supercomputing applications that take full advantage of CPU, GPU, and QPU compute abilities. Developing these applications today is challenging and requires an easy-to-use coding environment coupled with powerful quantum simulation capabilities to efficiently evaluate and improve the…
]]>Building a useful quantum computer in practice is incredibly challenging. Significant improvements are needed in the scale, fidelity, speed, reliability, and programmability of quantum computers to fully realize their benefits. Powerful tools are needed to help with the many complex physics and engineering challenges that stand in the way of useful quantum computing.
]]>The development of useful quantum computing is a massive global effort, spanning government, enterprise, and academia. The benefits of quantum computing could help solve some of the most challenging problems in the world related to applications such as materials simulation, climate modeling, risk management, supply chain optimization, and bioinformatics. Realizing the benefits of quantum…
]]>Quantitative finance libraries are software packages that consist of mathematical, statistical, and, more recently, machine learning models designed for use in quantitative investment contexts. They contain a wide range of functionalities, often proprietary, to support the valuation, risk management, construction, and optimization of investment portfolios. Financial firms that develop such…
]]>Quantum computing has the potential to revolutionize various aspects of industry, ranging from numerical simulations and optimization of complex systems to machine learning (ML). Many computational challenges within the automotive industry are well-suited for quantum computing, including the development of novel materials, efficient design of parts, optimal manufacturing processes…
]]>