- class ReverseEstimatorGradient(derivative_type=DerivativeType.REAL)¶
Estimator gradients with the classically efficient reverse mode.
This gradient implementation is based on statevector manipulations and scales exponentially with the number of qubits. However, for small system sizes it can be very fast compared to circuit-based gradients.
This class implements the calculation of the expectation gradient as described in . By keeping track of two statevectors and iteratively sweeping through each parameterized gate, this method scales only linearly with the number of parameters.
- : Jones, T. and Gacon, J. “Efficient calculation of gradients in classical simulations
of variational quantum algorithms” (2020). arXiv:2009.02823.
DerivativeType) – Defines whether the real, imaginary or real plus imaginary part of the gradient is returned.
Run the job of the estimator gradient on the given circuits.
Update the gradient's default options setting.
- SUPPORTED_GATES = ['rx', 'ry', 'rz', 'cp', 'crx', 'cry', 'crz']¶
Return the derivative type (real, imaginary or complex).
- Return type
The derivative type.