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The parameter shift rule (PSR) is a method used in quantum computing, specifically for variational quantum algorithms (VQAs), to compute the exact gradient of an expectation value with respect to a parameter in a quantum circuit. PSRs enabl efficient and exact optimization crucial for the advancement of quantum algorithms in chemistry, optimization, and machine learning.
A variational quantum circuit (VQC) is a parameterized quantum circuit (PQC) [1] where certain gates depend on continuous parameters. These parameters are adjusted to optimize a cost function, typically the expectation value of an observable, by training the circuit in a way analogous to training weights in a neural network. To optimize the parameters, one needs to compute the gradient of the cost function with respect to these parameters. However, directly computing gradients on quantum hardware is non-trivial due to the probabilistic nature of quantum measurements and the inability to directly access the quantum state.