Quellnetzwerk-Lastspeichertechnologie-Ansatz
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Ansatz ist die Elektrolyse, bei der mithilfe von elektrischer Energie Wasser in Wasserstoff und Sauerstoff gespalten wird. Am häufigsten kommen alkalische Elektrolyseure und PEM-Elektrolyseure zum Einsatz, Hochtemperatur-elektrolyse befindet sich noch im Entwick-lungsstadium. Alkalische Elektrolyseure sind
What is ansatz architecture?
Similar to classical NN, the ansatz architecture is critical to the performance of VQA and is actively being studied. Some ansatz architectures, e.g., Quantum CNN , Quantum RNN , Quantum LSTM , and Quantum GNN , are inspired by successful classical NN architectures.
What is a PQC ansatz?
quantum computation is performed, is called an ansatz. De-spite the intuitive principle of PQCs, the manual design of a beneficial circuit ansatz is non-trivial, since the criteria for an optimal ansatz in a particular scenario is still an active research area. Moreover, PQCs may encounter trainability issues, such as barren plateaus .
What is a quantum ansatz in machine learning?
There are also some two-qubit gates connecting different qubit pairs and formulating a network structure. The ansatz is executed on a quantum processor to evaluate a cost function determined by the specific machine learning task, which is similar to a forward execution of NN.
Why is ansatz architecture important in VQA?
The ansatz architecture of VQA can significantly affect the optimization result quality, trainability, execution overhead, etc. An optimal ansatz architecture towards all these design objectives is, to the best of our knowledge, not yet known or may not exist.
Is VQE based on a unitary coupled cluster ansatz?
However, when considering the VQE algorithm, it is notable that many proposed VQE approaches, whether based on unitary coupled cluster ansatz or hardware-efficient ansatz 20, 34, 35, 36, 37, result in circuits that exceed the capabilities of current quantum computers for all but the smallest chemical problems.
What is an ansatz in machine learning?
The ansatz is executed on a quantum processor to evaluate a cost function determined by the specific machine learning task, which is similar to a forward execution of NN. The parameters in the ansatz are tuned through a classical optimizer to minimize the cost function, which is the analogy of NN training.