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Más de 50.000 libretos de álbumes ya están disponibles en Apple Music Classical

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El servicio de transmisión Apple Music Classic ahora incluye más de 50,000 folletos de álbumes con notas detalladas, traducciones y más.

Proporciona a los amantes de la música la información detallada que desean.

Apple Music Classical profundiza en los catálogos de álbumes

Clásico de la música de Apple Transmite el catálogo de música clásica más grande del mundo, incluida una búsqueda totalmente optimizada y el audio de mayor calidad disponible. Sin embargo, es un complemento gratuito para Apple Music. Con su propia aplicación.

Los miles de folletos de álbumes disponibles a través de la aplicación ofrecen notas multilingües, biografías de compositores, información sobre orquestas, directores y solistas, así como textos cantados y libretos de ópera, cuando corresponda. El objetivo es “enriquecer la comprensión y la conexión del oyente con la música”, según Apple.

Más funciones y contenido

Además, Apple Music Classical agregó recientemente el Classical Top 100. Este gráfico semanal combina cinco fuentes de datos de más de 165 países para brindar los álbumes de música clásica más populares del mundo.

El servicio de transmisión también colabora con muchos de los lugares, compañías de ópera y orquestas más importantes del mundo para ofrecer a los suscriptores contenido y grabaciones nuevos y exclusivos, muchos de ellos en sonido espacial. Las organizaciones asociadas incluyen la Orquesta Filarmónica de Berlín, el Carnegie Hall, la Orquesta Sinfónica de Chicago, la Orquesta Concertgeubao, la Orquesta Sinfónica de Londres, la Ópera Metropolitana, la Orquesta Filarmónica de Nueva York, la Ópera Nacional de París, el Centro de Artes de Seúl, la Orquesta Sinfónica de Shanghai, el Suntory Hall y la Orquesta Filarmónica de Viena.

Como se mencionó anteriormente, los suscriptores de Apple Music pueden disfrutar de Apple Music Classical sin costo adicional.

Suscríbete ahora desde: manzana

el Solicitud gratuita del servicio. Disponible para ambos iPhone y iPad.



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Advancing Quantum Classical Computing with CUDA Quantum 0.5

Advancing Quantum Classical Computing with CUDA Quantum 0.5

Quantum-classical computing applications are rapidly evolving, with the CUDA Quantum platform playing an instrumental role in this development. The open-source platform is designed to facilitate the building of quantum-classical computing applications, compatible with quantum processor units (QPUs), GPUs, and CPUs. The latest release, CUDA Quantum 0.5, introduces a host of new features and improvements, making it a crucial tool in the realm of heterogeneous computing.

CUDA Quantum accelerates workflows in quantum simulation, quantum machine learning, and quantum chemistry by harnessing the power of GPUs. This acceleration is essential as it allows for more efficient and faster computations, enabling researchers and developers to solve complex problems more quickly. With the latest release, CUDA Quantum 0.5, the platform has broadened its scope, introducing more QPUs backends, more simulators, and other enhancements to streamline quantum-classical computing applications development.

CUDA Quantum 0.5

One of the key improvements is the platform’s support for running adaptive quantum kernels, a specification from the Quantum Integrated Runtime (QIR) alliance. This is a significant step towards integrated quantum-classical programming, a concept that combines classical and quantum computing principles to solve complex problems more efficiently.

CUDA Quantum 0.5 also introduces new kernels for quantum chemistry simulations, including Fermionic and Givens rotation and fermionic SWAP kernels. These kernels are instrumental in performing intricate calculations and simulations in the field of quantum chemistry. Furthermore, the platform now supports exponentials of Pauli matrices, which are useful for quantum simulations of physical systems and for developing quantum algorithms for optimization problems.

Quantum Computers

In terms of data handling, CUDA Quantum 0.5 has improved its support for std::vector and (C style) arrays. This enhanced support allows for more flexible and efficient data management, crucial for handling large data sets in quantum computing applications. The platform also now supports execution of for-and while-loops of known lengths on quantum hardware backends, a feature that enhances the efficiency of loop execution in quantum algorithms.

The new release of CUDA Quantum also expands its compatibility with different quantum hardware backends. IQM and Oxford Quantum Circuits (OQC) quantum computers are now supported as QPU backends in CUDA Quantum, joining the already supported quantum computers from Quantinuum and IonQ. This wider range of supported hardware opens up more possibilities for developers and researchers to run their quantum algorithms on different quantum hardware platforms.

Getting started with Quantum Classical Computing

Finally, CUDA Quantum 0.5 has also made significant strides in the area of quantum simulators. The platform has improved its tensor network-based simulators, which are suitable for large-scale simulation of certain classes of quantum circuits. Furthermore, a matrix product state (MPS) simulator has been added to CUDA Quantum. MPS simulators can handle a large number of qubits and more gate depth for certain classes of quantum circuits on a relatively small memory footprint, making them a valuable tool for quantum computing simulations.

The latest release of CUDA Quantum, with its host of new features and improvements, is a significant milestone in the development of quantum-classical computing applications. By providing a platform that supports a variety of quantum hardware, offers advanced kernels for quantum simulations, and improves data handling and simulation capabilities, CUDA Quantum 0.5 is paving the way for the future of quantum-classical computing.

If you would like to get started with CUDA Quantum NVIDIA has created a introductory guide on getting started with CUDA Quantum taking you step-by-step with Python and C++ examples that provide a quick learning path for CUDA Quantum capabilities.

Filed Under: Technology News, Top News





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