PASQAL, a leader in neutral atoms quantum computing, has launched Qadence, an open-source Python library aimed at making digital-analog quantum programs and quantum machine learning more accessible. The software is part of a hybrid approach combining digital quantum computing’s precision with analog quantum computing’s continuous control. Qadence is particularly effective in quantum machine learning applications, offering a simplified interface for developers. The software is expected to accelerate the evolution of digital-analog quantum computing (DAQC) and quantum machine learning. Mario Dagrada, VP of Quantum Software at PASQAL, believes Qadence will fill a gap in the current quantum software ecosystem.
Introduction to Qadence: A New Python-based Software Library
PASQAL, a company specialising in neutral atoms quantum computing, has recently launched Qadence, an open-source Python library. This new software is designed to simplify the process of creating digital-analog quantum programs on interacting qubit systems. This development is a testament to PASQAL’s dedication to pioneering new methods in quantum computing.
Understanding Digital Analog Quantum Computing (DAQC)
Digital Analog Quantum Computing (DAQC) is a hybrid model that seeks to merge the accuracy of digital quantum computing with the continuous control and interactions of analog quantum computing. There has been a growing interest in alternative models like analog and DAQC within the rapidly evolving quantum computing field. This method is seen as a potential route to achieving early quantum advantage. The next generation of PASQAL’s neutral atoms quantum computers will be designed to natively execute digital-analog quantum algorithms.
Qadence’s Role in Quantum Machine Learning Applications
Qadence is particularly notable in quantum machine learning applications with DAQC. It features native symbolic parameters, integration with the PyTorch automatic differentiation engine, and advanced parameter shift rules for higher-order differentiation on real neutral atoms quantum devices. Qadence aims to speed up the development of DAQC and quantum machine learning by offering a simplified interface. This allows developers to easily construct analog and digital-analog quantum algorithms, transition smoothly from simulations to real devices, and express complex interactions among qubits efficiently.
Qadence’s Future Goals and Objectives
The goal for Qadence is to become the standard for executing digital-analog programs. It emphasises a user-friendly interface, accurate emulation of quantum platforms, and a seamless transition from simulation to real quantum hardware. PASQAL plans for Qadence to further enhance its library by incorporating noise channels, tailored error mitigation techniques for interacting qubit systems, and additional digital-analog emulation modes.
PASQAL is a company that constructs quantum computers from ordered neutral atoms in 2D and 3D arrays. Their aim is to bring a practical quantum advantage to their customers and address real-world problems. Founded in 2019, out of the Institut d’Optique, by Georges-Olivier Reymond, Christophe Jurczak, Professor Dr. Alain Aspect, Nobel Prize Laureate Physics, 2022, Dr. Antoine Browaeys, and Dr. Thierry Lahaye, PASQAL has secured more than €140 million in financing to date.
“Qadence fills a gap in the current quantum software ecosystem by providing a user-friendly interface for the increasingly popular digital-analog quantum computing and accelerating the research in quantum machine learning leveraging this approach,” says Mario Dagrada, VP of Quantum Software at PASQAL.
Quick Summary
PASQAL has launched Qadence, an open-source Python library aimed at simplifying the process of building digital-analog quantum programs on interacting qubit systems, a hybrid approach combining the precision of digital quantum computing with the continuous control of analog quantum computing. The software is particularly effective in quantum machine learning applications, offering a user-friendly interface and seamless transition from simulations to real quantum hardware.
- PASQAL, a leading company in neutral atoms quantum computing, has launched Qadence, an open-source Python library.
- Qadence simplifies the process of building digital-analog quantum programs on interacting qubit systems, making it more accessible to researchers.
- Digital analog quantum computing (DAQC) is a hybrid approach that combines the precision of digital quantum computing with the continuous control and interactions of analog quantum computing.
- DAQC is seen as a likely path to early quantum advantage and the next generation of PASQAL’s neutral atoms quantum computers will be capable of executing digital-analog quantum algorithms.
- Qadence is particularly useful in quantum machine learning applications with DAQC, offering native symbolic parameters, integration with PyTorch automatic differentiation engine, and advanced parameter shift rules for higher-order differentiation on real neutral atoms quantum devices.
- Mario Dagrada, VP of Quantum Software at PASQAL, believes Qadence fills a gap in the current quantum software ecosystem by providing a user-friendly interface for DAQC and accelerating research in quantum machine learning.
- PASQAL aims for Qadence to become the gold standard for executing digital-analog programs, with plans to further enrich its library by incorporating noise channels, tailored error mitigation techniques for interacting qubit systems, and additional digital-analog emulation modes.
