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Github Anmolchaure Quantum Machine Learning Qml Using Pennylane As we’re working with customers at protiviti to find advantageous use cases in qml, we rely daily on a tool called pennylane from xanadu. join host konstantinos karagiannis, and special cohost emily stamm, for a chat with nathan killoran from xanadu to learn about this powerful, free software. Quantum machine learning, or qml, is one of the three major application categories for quantum computing, along with optimization and simulation. as we’re working with customers at protiviti to find advantageous use cases in qml, we rely daily on a tool called pennylane from xanadu. Abstract pennylane is a python based software framework for optimization and machine learning of quantum and hybrid quantum classical computations. it extends the automatic differentiation algorithms common in machine learning to work with quantum and hybrid computations. Quantum machine learning, or qml, is one of the three major application categories for quantum computing, along with optimization and simulation. as we’re working with customers at protiviti to find advantageous use cases in qml, we rely daily on a tool called pennylane from xanadu. Devices require routines that are of shallow depth and robust against errors. the design paradigm of hybrid algorithms which integrat quantum and classical processing has therefore become increasingly important. possibly the most well known class of hybrid algorithms is that of variational circuits, which are parameter dependent quantum circuits t. Pennylane is an open source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. create meaningful quantum algorithms, from inspiration to implementatio.