With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or quantum, rather than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization. The core library of the framework is implemented in C++, and the user level is implemented in Python, and it supports deployment on quantum and classical hardware. In this article, we analyze the development trend of the new generation machine learning framework and introduce the design principles of VQNet 2.0 in detail: unity, practicality, efficiency, and compatibility, as well as full particulars of implementation. We illustrate the functions of VQNet 2.0 through several basic applications, including classical convolutional neural networks, quantum autoencoders, hybrid classical-quantum networks, etc. After that, through extensive experiments, we demonstrate that the operation speed of VQNet 2.0 is higher than the comparison method. Finally, through extensive experiments, we demonstrate that VQNet 2.0 can deploy on different hardware platforms, the overall calculation speed is faster than the comparison method. It also can be mixed and optimized with quantum circuits composed of multiple quantum computing libraries.

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.

Quantum information will not only reformulate our view of the nature of computation and communication but will also open up fundamentally new possibilities for realizing high-performance computer architecture and telecommunication networks. Since our data will no longer remain safe in the traditional Internet when commercial quantum computers become fully available,1,2,8,15,34 there will be a need for a fundamentally different network structure: the quantum Internet.22,25,32,33,45,47 While quantum computational supremacy refers to tasks and problems that quantum computers can solve but are beyond the capability of classical computers, the quantum supremacy of the quantum Internet identifies the properties and attributes that the quantum Internet offers but are unavailable in the traditional Internet.a The quantum Internet uses the fundamental concepts of quantum mechanics for networking (see Sidebars 1–7 in the online Supplementary Information at https://dl.acm.org/doi/10.1145/3524455). The main attributes of the quantum Internet are advanced quantum phenomena and protocols (such as quantum superposition and quantum entanglement, quantum teleportation, and advanced quantum coding methods), unconditional security (quantum cryptography), and an entangled network structure. In contrast to traditional repeaters,b quantum repeaters cannot apply the receive–copy-retransmit mechanism because of the so-called no-cloning theorem, which states that it is impossible to make a perfect copy of a quantum system (see Sidebar 4). This fundamental difference between the nature of classical and quantum information does not just lead to fundamentally different networking mechanisms; it also necessitates the definition of novel networking services in a quantum Internet scenario. Quantum memories in quantum repeater units are a fundamental part of any global-scale quantum Internet. A challenge connected to quantum memory units is the noise quantum memories adds to storing quantum systems.