Expose internal rust interface to DenseLayout (Qiskit#12104)

* Expose internal rust interface to DenseLayout

This commit makes a small change to the rust code for DenseLayout that
enables calling it more easily from rust. The primary obstacle was the
pyfunction used PyReadonlyArray2<f64> inputs which precludes calling it
with rust constructed Array2Views<f64>. This adds a new inner public
function which takes the array view directly and then the pyfunction's
only job is to convert the inputs and outputs to Python. The python side
of the function is still building a sparse matrix and then runs reverse
Cuthill–McKee to get a permutation of the densest subgraph so any rust
consumers will want to keep that in mind (and maybe use sprs to do the
same).

At the same time it corrects an oversight in the original implementation
where the returned numpy arrays of the densest subgraph are copied
instead of being returned as references. This should slightly improve
performance by eliminating 3 array copies that weren't needed.

* Remove PyResult

---------

Co-authored-by: Henry Zou <87874865+henryzou50@users.noreply.github.com>

crates/accelerate/src/dense_layout.rs

@@ -15,8 +15,8 @@ use ahash::RandomState;
 use hashbrown::{HashMap, HashSet};
 use indexmap::IndexSet;
 use ndarray::prelude::*;
+use numpy::IntoPyArray;
 use numpy::PyReadonlyArray2;
-use numpy::ToPyArray;
 use rayon::prelude::*;
 
 use pyo3::prelude::*;
@@ -108,10 +108,35 @@ pub fn best_subset(
     use_error: bool,
     symmetric_coupling_map: bool,
     error_matrix: PyReadonlyArray2<f64>,
-) -> PyResult<(PyObject, PyObject, PyObject)> {
+) -> (PyObject, PyObject, PyObject) {
     let coupling_adj_mat = coupling_adjacency.as_array();
-    let coupling_shape = coupling_adj_mat.shape();
     let err = error_matrix.as_array();
+    let [rows, cols, best_map] = best_subset_inner(
+        num_qubits,
+        coupling_adj_mat,
+        num_meas,
+        num_cx,
+        use_error,
+        symmetric_coupling_map,
+        err,
+    );
+    (
+        rows.into_pyarray_bound(py).into(),
+        cols.into_pyarray_bound(py).into(),
+        best_map.into_pyarray_bound(py).into(),
+    )
+}
+
+pub fn best_subset_inner(
+    num_qubits: usize,
+    coupling_adj_mat: ArrayView2<f64>,
+    num_meas: usize,
+    num_cx: usize,
+    use_error: bool,
+    symmetric_coupling_map: bool,
+    err: ArrayView2<f64>,
+) -> [Vec<usize>; 3] {
+    let coupling_shape = coupling_adj_mat.shape();
     let avg_meas_err = err.diag().mean().unwrap();
 
     let map_fn = |k| -> SubsetResult {
@@ -216,11 +241,7 @@ pub fn best_subset(
     let rows: Vec<usize> = new_cmap.iter().map(|edge| edge[0]).collect();
     let cols: Vec<usize> = new_cmap.iter().map(|edge| edge[1]).collect();
 
-    Ok((
-        rows.to_pyarray_bound(py).into(),
-        cols.to_pyarray_bound(py).into(),
-        best_map.to_pyarray_bound(py).into(),
-    ))
+    [rows, cols, best_map]
 }
 
 #[pymodule]