On September 30, 2023, Pasqal announced the launch of Qadence, an open-source Python library that simplifies the process of creating analog-digital quantum programs on systems of interacting qubits, demonstrating PASQAL’s commitment to promoting new approaches to quantum computing.
Qadence particularly stands out in quantum machine learning applications with Digital Analog Quantum Computing (DAQC), boasting native symbolic parameters, integration with the PyTorch automatic differentiation engine, and advanced parameter shift rules for higher-order differentiation on quantum devices with real neutral atoms.
The user-friendly Python programming package, Qadence, accelerates the evolution of DAQC and quantum machine learning by offering a simplified interface that allows developers to:
- Easily construct analog and digital-analog quantum algorithms
- Seamlessly transition from simulations to real devices, such as PASQAL’s neutral atoms quantum computers
- Easily express complex interaction among qubits and readily incorporate them into efficient executions on simulator backends
- Translate certain types of analog or digital-analog operations into numerically efficient simulations similar to digital quantum circuits
Qadence’s goal is to become the gold standard for executing digital-analog programs, emphasizing a user-friendly interface, precise emulation of quantum platforms, and a seamless transition from simulation to real quantum hardware. PASQAL aims for Qadence to further enrich its library by incorporating noise channels, tailored error mitigation techniques for interacting qubit systems, and additional digital-analog emulation modes.
To get started or dive into the Qadence technical details and the package documentation, you can follow this link. For any feed back or feature request, you can open an issue here.
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