Qubits are inherently sensitive to noise, and it is expected that even the most robust qubits will always exhibit noise levels orders of magnitude from what’s required for practical quantum applications. This noise problem is solved with quantum error correction (QEC). This is a collection of techniques that can identify and eliminate errors in a controlled way, so long as qubits can be…
]]>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 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…
]]>Heterogeneous computing architectures—those that incorporate a variety of processor types working in tandem—have proven extremely valuable in the continued scalability of computational workloads in AI, machine learning (ML), quantum physics, and general data science. Critical to this development has been the ability to abstract away the heterogeneous architecture and promote a framework that…
]]>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…
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