targeted_einsum

pyquil.numpy_simulator.targeted_einsum(gate, wf, wf_target_inds)[source]

Left-multiplies the given axes of the wf tensor by the given gate matrix.

Note that the matrix must have a compatible tensor structure. For example, if you have an 6-qubit state vector wf with shape (2, 2, 2, 2, 2, 2), and a 2-qubit unitary operation op with shape (2, 2, 2, 2), and you want to apply op to the 5th and 3rd qubits within input_state, then the output state vector is computed as follows:

output_state = targeted_einsum(op, input_state, [5, 3])

This method also works when the right hand side is a matrix instead of a vector. If a unitary circuit’s matrix is old_effect, and you append a CNOT(q1, q4) operation onto the circuit, where the control q1 is the qubit at offset 1 and the target q4 is the qubit at offset 4, then the appended circuit’s unitary matrix is computed as follows:

new_effect = targeted_left_multiply(CNOT.reshape((2, 2, 2, 2)), old_effect, [1, 4])
Parameters:
  • gate (ndarray) – What to left-multiply the target tensor by.
  • wf (ndarray) – A tensor to carefully broadcast a left-multiply over.
  • wf_target_inds (List[int]) – Which axes of the target are being operated on.
Return type:

ndarray

Returns:

The output tensor.