diff --git a/README.md b/README.md
index bd3c24c..a32b36c 100644
--- a/README.md
+++ b/README.md
@@ -15,7 +15,7 @@
 - [ ] Build out pauli decomposer
 - [X] Remove the weights argument and rename to data
 - [X] Add namespace
-- [ ] Add function equivalent of `get_state_iteration_unequal_weights` to SummedPauliOp
+- [ ] Add apply method to SummedPauliOp that takes precomputed weighted data
 - [ ] Writeup for docs
 - [ ] Add pybind11 interface and python examples
 - [ ] Change functions names over to default to parallel impl and use `_serial` for the serial implementation
diff --git a/include/__summed_pauli_op.hpp b/include/__summed_pauli_op.hpp
index 4a18bb0..56fb0d6 100644
--- a/include/__summed_pauli_op.hpp
+++ b/include/__summed_pauli_op.hpp
@@ -214,7 +214,6 @@ template <std::floating_point T> struct SummedPauliOp {
   }
 
   // TODO IMPLEMENT
-  // equivalent to get_state_iteration_unequal_weights in the python code
   template <std::floating_point data_dtype>
   void apply_parallel_weighted_data(
       Tensor<2> new_states, Tensor<2> states,
diff --git a/qcog_bench.py b/qcog_bench.py
deleted file mode 100644
index f2acf62..0000000
--- a/qcog_bench.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from typing import Any
-import numpy as np
-import time
-
-from qcog.src.classical.pauli_symbolic import ClassicalPauliSymbolic as PauliHSM
-from qcog.src.classical.common import OptimizationMethod, StateMethod
-from qcog.src.compiled.pauli.pauli_utils import get_state_iteration_unequal_weights
-
-config: dict[str, Any] = {
-    "hsm_type": "pauli",
-    "data_params": {"train_size": 10000, "test_size": 1000, "dataset": "mnist"},
-    "random_seed": 42,
-    "initialization_params": {
-        "qbits": 14,
-        "pauli_weight": 2,
-    },
-    "train_params": {
-        "weight_optimization_kwargs": {
-            "optimization_method": OptimizationMethod.ANALYTIC.value,
-        },
-        "batch_size": 10000,
-        "num_passes": 4,
-        "state_kwargs": {
-            "state_method": StateMethod.LOBPCG_FAST.value,
-            "iterations": 10,
-            "tolerance": 0.1,
-        },
-    },
-    "forecast_params": {
-        "state_method": StateMethod.LOBPCG_FAST.value,
-        "iterations": 10,
-    },
-}
-
-
-n_operators = 10000
-n_data = 1000
-operators = [str(i) for i in range(n_operators)]
-data = np.zeros((n_data, n_operators))
-states = np.zeros(
-    (n_data, 2 ** config["initialization_params"]["qbits"]), dtype=np.complex128
-)
-
-
-hsm = PauliHSM(operators=operators, **config["initialization_params"])
-
-ham_terms = hsm._generate_ham_components(data, operators)
-A2, xA_weights, data_term = ham_terms
-
-t0 = time.perf_counter()
-new_states = get_state_iteration_unequal_weights(
-    states, hsm._pauli_str_array, xA_weights
-)
-
-print(f"Time for x_kt * A_k |psi_t>: {time.perf_counter() - t0:.2f} s")