Following its developer conference last week, Baidu today detailed Quantum Leaf, a new cloud quantum computing platform designed for programming, simulating, and executing quantum workloads. It’s aimed at providing a programming environment for quantum-infrastructure-as-a-service setups, Baidu says, and it complements the Paddle Quantum development toolkit the company released earlier this year.
Experts believe that quantum computing, which at a high level entails the use of quantum-mechanical phenomena like superposition and entanglement to perform computation, could one day accelerate AI workloads. Moreover, AI continues to play a role in cutting-edge quantum computing research.
Baidu says a key component of Quantum Leaf is QCompute, a Python-based open source development kit with a hybrid programming language and a high-performance simulator. Users can leverage prebuilt objects and modules in the quantum programming environment, passing parameters to build and execute quantum circuits on the simulator or cloud simulators and hardware. Essentially, QCompute provides services for creating and analyzing circuits and calling the backend.
Quantum Leaf dovetails with Quanlse, which Baidu also detailed today. The company describes Quanlse as a “cloud-based quantum pulse computing service” that bridges the gap between software and hardware by providing a service to design and implement pulse sequences as part of quantum tasks. (Pulse sequences are a means of reducing quantum error, which results from decoherence and other quantum noise.) Quanlse works with both superconducting circuits and nuclear magnetic resonance platforms and will extend to new form factors in the future, Baidu says.
The unveiling of Quantum Leaf and Quanlse follows the release of Amazon Braket and Google’s TensorFlow Quantum, a machine learning framework that can construct quantum data sets, prototype hybrid quantum and classic machine learning models, support quantum circuit simulators, and train discriminative and generative quantum models. Facebook’s PyTorch relies on Xanadu’s multi-contributor project for quantum computing PennyLane, a third-party library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations. And Microsoft offers several kits and libraries for quantum machine learning applications.