doc: add tags to equations for LaTeX rendering in RST docs (#112)

Co-authored-by: Abe Coull <[email protected]>

src/braket/experimental/algorithms/qc_qmc/qc_qmc.py

@@ -222,7 +222,9 @@ def local_energy_quantum(  # noqa: C901
     trial_state_circuit: Callable,
     dev: qml.device,
 ) -> complex:
-    r"""This function estimates the integral $\\langle \\Psi_Q|H|\\phi_l\rangle$ with rotated basis.
+    r"""
+    This function estimates the integral :math:`$\\langle \\Psi_Q|H|\\phi_l\rangle$`
+    with rotated basis.
 
     Args:
         walker (ndarray): np.ndarray; matrix representation of the walker state, not necessarily

src/braket/experimental/algorithms/quantum_approximate_optimization/quantum_approximate_optimization.py

@@ -152,7 +152,9 @@ def cost_layer(
 
 @circuit.subroutine(register=True)
 def decomposed_zz_gate(qubit0: int, qubit1: int, gamma: float) -> Circuit:
-    """Return a circuit implementing exp(-i gamma Z_i Z_j) using CNOT gates if ZZ not supported.
+    """
+    Return a circuit implementing :math:`exp(-i * \\gamma * Z_i * Z_j)` using CNOT gates
+    if ZZ not supported.
 
     Args:
         qubit0 (int): Index value for the controlling qubit for CNOT gate

src/braket/experimental/algorithms/quantum_circuit_born_machine/qcbm.py

@@ -151,7 +151,8 @@ def gradient(self, params: np.ndarray) -> np.ndarray:
 def _compute_kernel(px: np.ndarray, py: np.ndarray, sigma_list: List[float] = [0.1, 1]) -> float:
     r"""Gaussian radial basis function (RBF) kernel.
 
-    `K(x, y) = sum_\sigma exp(-|x-y|^2/(2\sigma^2 ))`
+    .. math::
+        K(x, y) = sum_\sigma exp(-|x-y|^2/(2\sigma^2 ))
 
     Args:
         px (ndarray): Probability distribution
@@ -173,11 +174,13 @@ def mmd_loss(px: np.ndarray, py: np.ndarray, sigma_list: List[float] = [0.1, 1])
 
     MMD determines if two distributions are equal by looking at the difference between
     their means in feature space.
-    ::
+
+    .. math::
         MMD(x, y) = | \sum_{j=1}^N \phi(y_j) - \sum_{i=1}^N \phi(x_i) |_2^2
 
     With a RBF kernel, we apply the kernel trick to expand MMD to
-    ::
+
+    .. math::
         MMD(x, y) = \sum_{j=1}^N \sum_{j'=1}^N k(y_j, y_{j'})
                 + \sum_{i=1}^N \sum_{i'=1}^N k(x_i, x_{i'})
                 - 2 \sum_{j=1}^N \sum_{i=1}^N k(y_j, x_i)