diff --git a/docs/how_tos/choose_subspace_dimension.ipynb b/docs/how_tos/choose_subspace_dimension.ipynb
index 93ee394..e3d5d91 100644
--- a/docs/how_tos/choose_subspace_dimension.ipynb
+++ b/docs/how_tos/choose_subspace_dimension.ipynb
@@ -113,7 +113,7 @@
    "source": [
     "from qiskit_addon_sqd.configuration_recovery import recover_configurations\n",
     "from qiskit_addon_sqd.fermion import (\n",
-    "    bitstring_matrix_to_sorted_addresses,\n",
+    "    bitstring_matrix_to_ci_strs,\n",
     "    flip_orbital_occupancies,\n",
     "    solve_fermion,\n",
     ")\n",
@@ -176,8 +176,8 @@
     "        int_occs = np.zeros((n_batches, 2 * num_orbitals))\n",
     "        cs = []\n",
     "        for j in range(n_batches):\n",
-    "            addresses = bitstring_matrix_to_sorted_addresses(batches[j], open_shell=open_shell)\n",
-    "            int_d[j] = len(addresses[0]) * len(addresses[1])\n",
+    "            ci_strs = bitstring_matrix_to_ci_strs(batches[j], open_shell=open_shell)\n",
+    "            int_d[j] = len(ci_strs[0]) * len(ci_strs[1])\n",
     "            energy_sci, coeffs_sci, avg_occs, spin = solve_fermion(\n",
     "                batches[j],\n",
     "                hcore,\n",
diff --git a/docs/how_tos/integrate_dice_solver.ipynb b/docs/how_tos/integrate_dice_solver.ipynb
index 9ce1e57..12b23a4 100644
--- a/docs/how_tos/integrate_dice_solver.ipynb
+++ b/docs/how_tos/integrate_dice_solver.ipynb
@@ -29,7 +29,7 @@
     "from qiskit_addon_sqd.configuration_recovery import recover_configurations\n",
     "from qiskit_addon_sqd.counts import counts_to_arrays, generate_counts_uniform\n",
     "from qiskit_addon_sqd.fermion import (\n",
-    "    bitstring_matrix_to_sorted_addresses,\n",
+    "    bitstring_matrix_to_ci_strs,\n",
     "    flip_orbital_occupancies,\n",
     ")\n",
     "from qiskit_addon_sqd.subsampling import postselect_and_subsample\n",
@@ -100,9 +100,9 @@
     "    int_e = np.zeros(n_batches)\n",
     "    int_occs = np.zeros((n_batches, 2 * num_orbitals))\n",
     "    for j in range(n_batches):\n",
-    "        addresses = bitstring_matrix_to_sorted_addresses(batches[j], open_shell=open_shell)\n",
+    "        ci_strs = bitstring_matrix_to_ci_strs(batches[j], open_shell=open_shell)\n",
     "        energy_sci, wf_mags, avg_occs = solve_dice(\n",
-    "            addresses,\n",
+    "            ci_strs,\n",
     "            active_space_path,\n",
     "            os.path.abspath(\".\"),\n",
     "            spin_sq=spin_sq,\n",
diff --git a/docs/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb b/docs/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb
index 1d00e7f..131c0fd 100644
--- a/docs/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb
+++ b/docs/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb
@@ -26,7 +26,7 @@
     "This package provides two tools to perform this projection:\n",
     "\n",
     "- ``qiskit_addon_sqd.qubit.matrix_elements_from_pauli()``: is a lower-level function\n",
-    "that returns the non-zero matrix elements of a Pauli string and the corresponding addresses\n",
+    "that returns the non-zero matrix elements of a Pauli string and the corresponding indices\n",
     "of the non-zero elements.\n",
     "\n",
     "- ``qiskit_addon_sqd.qubit.project_operator_to_subspace()``: is a higher-level function that \n",
@@ -92,7 +92,7 @@
    "id": "5707b93c",
    "metadata": {},
    "source": [
-    "### First method: nonzero matrix elements and addresses."
+    "### First method: nonzero matrix elements and indices."
    ]
   },
   {
@@ -142,11 +142,9 @@
     "operator_from_matrix_elements = coo_matrix((d, d), dtype=\"complex128\")\n",
     "\n",
     "for pauli in hamiltonian.paulis:\n",
-    "    matrix_elements, row_addresses, col_addresses = matrix_elements_from_pauli(\n",
-    "        bitstring_matrix, pauli\n",
-    "    )\n",
+    "    matrix_elements, row_indices, col_indices = matrix_elements_from_pauli(bitstring_matrix, pauli)\n",
     "    operator_from_matrix_elements += coo_matrix(\n",
-    "        (matrix_elements, (row_addresses, col_addresses)), (d, d)\n",
+    "        (matrix_elements, (row_indices, col_indices)), (d, d)\n",
     "    )"
    ]
   },
diff --git a/docs/how_tos/select_open_closed_shell.ipynb b/docs/how_tos/select_open_closed_shell.ipynb
index 7a36df4..133a1e2 100644
--- a/docs/how_tos/select_open_closed_shell.ipynb
+++ b/docs/how_tos/select_open_closed_shell.ipynb
@@ -182,10 +182,10 @@
     }
    ],
    "source": [
-    "from qiskit_addon_sqd.fermion import bitstring_matrix_to_sorted_addresses\n",
+    "from qiskit_addon_sqd.fermion import bitstring_matrix_to_ci_strs\n",
     "\n",
-    "addresses = bitstring_matrix_to_sorted_addresses(batches[0], open_shell=open_shell)\n",
-    "print(addresses)"
+    "ci_strs = bitstring_matrix_to_ci_strs(batches[0], open_shell=open_shell)\n",
+    "print(ci_strs)"
    ]
   },
   {
@@ -277,15 +277,14 @@
     }
    ],
    "source": [
-    "addresses_up = addresses[0]\n",
-    "addresses_dn = addresses[1]\n",
+    "ci_strs_up, ci_strs_dn = ci_strs\n",
     "\n",
     "print(\"Basis elements of the subspace:\")\n",
     "\n",
-    "for address_up in addresses_up:\n",
-    "    for address_dn in addresses_dn:\n",
+    "for ci_str_up in ci_strs_up:\n",
+    "    for ci_str_dn in ci_strs_dn:\n",
     "        format_name = \"{0:0\" + str(num_orbitals) + \"b}\"\n",
-    "        print(\"|\" + format_name.format(address_up) + format_name.format(address_dn) + \">\")"
+    "        print(\"|\" + format_name.format(ci_str_up) + format_name.format(ci_str_dn) + \">\")"
    ]
   },
   {
@@ -436,13 +435,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(array([1, 4], dtype=int64), array([2, 8], dtype=int64))\n"
+      "(array([2, 8], dtype=int64), array([1, 4], dtype=int64))\n"
      ]
     }
    ],
    "source": [
-    "addresses = bitstring_matrix_to_sorted_addresses(batches[0], open_shell=open_shell)\n",
-    "print(addresses)"
+    "ci_strs = bitstring_matrix_to_ci_strs(batches[0], open_shell=open_shell)\n",
+    "print(ci_strs)"
    ]
   },
   {
@@ -484,23 +483,22 @@
      "output_type": "stream",
      "text": [
       "Basis elements of the subspace:\n",
-      "|00010010>\n",
-      "|00011000>\n",
-      "|01000010>\n",
-      "|01001000>\n"
+      "|00100001>\n",
+      "|00100100>\n",
+      "|10000001>\n",
+      "|10000100>\n"
      ]
     }
    ],
    "source": [
-    "addresses_up = addresses[0]\n",
-    "addresses_dn = addresses[1]\n",
+    "ci_strs_up, ci_strs_dn = ci_strs\n",
     "\n",
     "print(\"Basis elements of the subspace:\")\n",
     "\n",
-    "for address_up in addresses_up:\n",
-    "    for address_dn in addresses_dn:\n",
+    "for ci_str_up in ci_strs_up:\n",
+    "    for ci_str_dn in ci_strs_dn:\n",
     "        format_name = \"{0:0\" + str(num_orbitals) + \"b}\"\n",
-    "        print(\"|\" + format_name.format(address_up) + format_name.format(address_dn) + \">\")"
+    "        print(\"|\" + format_name.format(ci_str_up) + format_name.format(ci_str_dn) + \">\")"
    ]
   },
   {
diff --git a/docs/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb b/docs/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb
index 8920504..741f9fb 100644
--- a/docs/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb
+++ b/docs/how_tos/use_oo_to_optimize_hamiltonian_basis.ipynb
@@ -114,11 +114,7 @@
    ],
    "source": [
     "from qiskit_addon_sqd.configuration_recovery import recover_configurations\n",
-    "from qiskit_addon_sqd.fermion import (\n",
-    "    bitstring_matrix_to_sorted_addresses,\n",
-    "    flip_orbital_occupancies,\n",
-    "    solve_fermion,\n",
-    ")\n",
+    "from qiskit_addon_sqd.fermion import flip_orbital_occupancies, solve_fermion\n",
     "from qiskit_addon_sqd.subsampling import postselect_and_subsample\n",
     "\n",
     "# SQSD options\n",
@@ -213,7 +209,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x600 with 2 Axes>"
       ]
@@ -306,9 +302,9 @@
    "source": [
     "#### Setup of the subspace\n",
     "\n",
-    "To define the subspace, we will take the addresses of the batch with the lowest energy\n",
+    "To define the subspace, we will take the CI strings of the batch with the lowest energy\n",
     "from the last configuration recovery step. Other strategies may be used, like taking the union \n",
-    "of the addresses of the batches in the last configuration recovery iteration."
+    "of the CI strings of the batches in the last configuration recovery iteration."
    ]
   },
   {
@@ -321,15 +317,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Subspace dimension: 216225\n",
-      "Energy of that batch from SQD: -109.00098768512053\n"
+      "Subspace dimension: 213444\n",
+      "Energy of that batch from SQD: -108.99034160888203\n"
      ]
     }
    ],
    "source": [
+    "from qiskit_addon_sqd.fermion import bitstring_matrix_to_ci_strs\n",
+    "\n",
     "best_batch = batches[np.argmin(e_hist[-1])]\n",
-    "addresses = bitstring_matrix_to_sorted_addresses(best_batch, open_shell=open_shell)\n",
-    "print(f\"Subspace dimension: {len(addresses[0]) * len(addresses[1])}\")\n",
+    "ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs(best_batch, open_shell=open_shell)\n",
+    "print(f\"Subspace dimension: {len(ci_strs_up) * len(ci_strs_dn)}\")\n",
     "print(f\"Energy of that batch from SQD: {e_hist[-1, np.argmin(e_hist[-1])]}\")\n",
     "\n",
     "# Union strategy\n",
@@ -337,10 +335,10 @@
     "# batches_union = np.concatenate((batches[0], batches[1]), axis = 0)\n",
     "# for i in range(n_batches-2):\n",
     "#    batches_union = np.concatenate((batches_union, batches[ i+ 2]))\n",
-    "# addresses = bitstring_matrix_to_sorted_addresses(\n",
+    "# ci_strs_up, ci_strs_dn = bitstring_matrix_to_ci_strs(\n",
     "#            batches_union, open_shell=open_shell\n",
     "#            )\n",
-    "# print (f\"Subspace dimension: {len(addresses[0]) * len(addresses[1])}\")"
+    "# print (f\"Subspace dimension: {len(ci_strs_up) * len(ci_strs_dn)}\")"
    ]
   },
   {
@@ -389,7 +387,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "improved_energy in the new basis: -109.00175824366396\n"
+      "improved_energy in the new basis: -108.98995482831833\n"
      ]
     }
    ],
diff --git a/qiskit_addon_sqd/fermion.py b/qiskit_addon_sqd/fermion.py
index 3c7b119..ef36c3a 100644
--- a/qiskit_addon_sqd/fermion.py
+++ b/qiskit_addon_sqd/fermion.py
@@ -19,7 +19,7 @@
    :toctree: ../stubs/
    :nosignatures:
 
-   bitstring_matrix_to_sorted_addresses
+   bitstring_matrix_to_ci_strs
    enlarge_batch_from_transitions
    flip_orbital_occupancies
    solve_fermion
@@ -36,6 +36,7 @@
 from jax import numpy as jnp
 from jax.scipy.linalg import expm
 from pyscf import fci
+from qiskit.utils import deprecate_func
 from scipy import linalg as LA
 
 config.update("jax_enable_x64", True)  # To deal with large integers
@@ -69,7 +70,7 @@ def solve_fermion(
         hcore: Core Hamiltonian matrix representing single-electron integrals
         eri: Electronic repulsion integrals representing two-electron integrals
         open_shell: A flag specifying whether configurations from the left and right
-            halves of the bitstrings should be kept separate. If ``False``, addresses
+            halves of the bitstrings should be kept separate. If ``False``, CI strings
             from the left and right halves of the bitstrings are combined into a single
             set of unique configurations and used for both the alpha and beta subspaces.
         spin_sq: Target value for the total spin squared for the ground state.
@@ -83,9 +84,6 @@ def solve_fermion(
                 - SCI coefficients
                 - Average orbital occupancy
                 - Expectation value of spin-squared
-
-    Raises:
-            ValueError: The input determinant ``addresses`` must be non-empty, sorted arrays of integers.
     """
     if isinstance(bitstring_matrix, tuple):
         warnings.warn(
@@ -93,15 +91,15 @@ def solve_fermion(
             DeprecationWarning,
             stacklevel=2,
         )
-        addresses = bitstring_matrix
+        ci_strs = bitstring_matrix
     else:
         # This will become the default code path after the deprecation period.
-        addresses = bitstring_matrix_to_sorted_addresses(bitstring_matrix, open_shell=open_shell)
-        addresses = addresses[::-1]
-    addresses = _check_addresses(addresses)
+        ci_strs = bitstring_matrix_to_ci_strs(bitstring_matrix, open_shell=open_shell)
+        ci_strs = ci_strs[::-1]
+    ci_strs = _check_ci_strs(ci_strs)
 
-    num_up = format(addresses[0][0], "b").count("1")
-    num_dn = format(addresses[1][0], "b").count("1")
+    num_up = format(ci_strs[0][0], "b").count("1")
+    num_dn = format(ci_strs[1][0], "b").count("1")
 
     # Number of molecular orbitals
     norb = hcore.shape[0]
@@ -115,7 +113,7 @@ def solve_fermion(
         eri,
         norb,
         (num_up, num_dn),
-        ci_strs=addresses,
+        ci_strs=ci_strs,
         verbose=verbose,
         max_cycle=max_davidson,
     )
@@ -174,7 +172,7 @@ def optimize_orbitals(
             to be of shape (# orbitals, # orbitals) before being used as a
             similarity transform operator on the orbitals. Thus ``len(k_flat)=# orbitals**2``.
         open_shell: A flag specifying whether configurations from the left and right
-            halves of the bitstrings should be kept separate. If ``False``, addresses
+            halves of the bitstrings should be kept separate. If ``False``, CI strings
             from the left and right halves of the bitstrings are combined into a single
             set of unique configurations and used for both the alpha and beta subspaces.
         spin_sq: Target value for the total spin squared for the ground state
@@ -193,20 +191,20 @@ def optimize_orbitals(
     """
     if isinstance(bitstring_matrix, tuple):
         warnings.warn(
-            "Passing a length-2 tuple of sorted addresses to define the subspace is deprecated. Users "
+            "Passing a length-2 tuple of base-10 determinants to define the subspace is deprecated. Users "
             "should instead pass in the bitstring matrix defining the subspace.",
             DeprecationWarning,
             stacklevel=2,
         )
-        addresses = bitstring_matrix
+        ci_strs = bitstring_matrix
     else:
-        # Flip the output so the alpha addresses are on the left with [::-1]
-        addresses = bitstring_matrix_to_sorted_addresses(bitstring_matrix, open_shell=open_shell)
-        addresses = addresses[::-1]
-    addresses = _check_addresses(addresses)
+        # Flip the output so the alpha CI strs are on the left with [::-1]
+        ci_strs = bitstring_matrix_to_ci_strs(bitstring_matrix, open_shell=open_shell)
+        ci_strs = ci_strs[::-1]
+    ci_strs = _check_ci_strs(ci_strs)
 
-    num_up = format(addresses[0][0], "b").count("1")
-    num_dn = format(addresses[1][0], "b").count("1")
+    num_up = format(ci_strs[0][0], "b").count("1")
+    num_dn = format(ci_strs[1][0], "b").count("1")
 
     # TODO: Need metadata showing the optimization history
     ## hcore and eri in physicist ordering
@@ -227,7 +225,7 @@ def optimize_orbitals(
             eri_rot_chem,
             num_orbitals,
             (num_up, num_dn),
-            ci_strs=addresses,
+            ci_strs=ci_strs,
             max_cycle=max_davidson,
         )
 
@@ -303,6 +301,12 @@ def flip_orbital_occupancies(occupancies: np.ndarray) -> np.ndarray:
     return occ_out
 
 
+@deprecate_func(
+    removal_timeline="no sooner than qiskit-addon-sqd 0.8.0",
+    since="0.6.0",
+    package_name="qiskit-addon-sqd",
+    additional_msg="Use the bitstring_matrix_to_ci_strs function.",
+)
 def bitstring_matrix_to_sorted_addresses(
     bitstring_matrix: np.ndarray, open_shell: bool = False
 ) -> tuple[np.ndarray, np.ndarray]:
@@ -324,8 +328,8 @@ def bitstring_matrix_to_sorted_addresses(
             and right bitstrings.
 
     Returns:
-        A length-2 tuple of sorted, unique base-10 determinant addresses representing the left
-        and right halves of the bitstrings, respectively.
+        A length-2 tuple of sorted, unique base-10 determinant addresses representing the
+        left (spin-down) and right (spin-up) halves of the bitstrings, respectively.
     """
     num_orbitals = bitstring_matrix.shape[1] // 2
     num_configs = bitstring_matrix.shape[0]
@@ -350,6 +354,53 @@ def bitstring_matrix_to_sorted_addresses(
     return addresses_left, addresses_right
 
 
+def bitstring_matrix_to_ci_strs(
+    bitstring_matrix: np.ndarray, open_shell: bool = False
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Convert bitstrings (rows) in a ``bitstring_matrix`` into base-10 integer representations of determinants.
+
+    This function separates each bitstring in ``bitstring_matrix`` in half, flips the
+    bits and translates them into integer representations, and finally appends them to
+    their respective (spin-up or spin-down) lists. Those lists are sorted and output
+    from this function.
+
+    Args:
+        bitstring_matrix: A 2D array of ``bool`` representations of bit
+            values such that each row represents a single bitstring
+        open_shell: A flag specifying whether unique configurations from the left and right
+            halves of the bitstrings should be kept separate. If ``False``, configurations
+            from the left and right halves of the bitstrings are combined into a single
+            set of unique configurations. That combined set will be returned for both the left
+            and right bitstrings.
+
+    Returns:
+        A length-2 tuple of sorted, unique base-10 determinants representing the
+        right (spin-up) and left (spin-down) halves of the bitstrings, respectively.
+    """
+    num_orbitals = bitstring_matrix.shape[1] // 2
+    num_configs = bitstring_matrix.shape[0]
+
+    ci_str_left = np.zeros(num_configs)
+    ci_str_right = np.zeros(num_configs)
+    bts_matrix_left = bitstring_matrix[:, :num_orbitals]
+    bts_matrix_right = bitstring_matrix[:, num_orbitals:]
+
+    # For performance, we accumulate the left and right CI strings together, column-wise,
+    # across the two halves of the input bitstring matrix.
+    for i in range(num_orbitals):
+        ci_str_left[:] += bts_matrix_left[:, i] * 2 ** (num_orbitals - 1 - i)
+        ci_str_right[:] += bts_matrix_right[:, i] * 2 ** (num_orbitals - 1 - i)
+
+    ci_strs_right = np.unique(ci_str_right.astype("longlong"))
+    ci_strs_left = np.unique(ci_str_left.astype("longlong"))
+
+    if not open_shell:
+        ci_strs_left = ci_strs_right = np.union1d(ci_strs_left, ci_strs_right)
+
+    return ci_strs_right, ci_strs_left
+
+
 def enlarge_batch_from_transitions(
     bitstring_matrix: np.ndarray, transition_operators: np.ndarray
 ) -> np.ndarray:
@@ -376,17 +427,17 @@ def enlarge_batch_from_transitions(
     return np.array(bitstring_matrix_augmented)
 
 
-def _check_addresses(
-    addresses: tuple[np.ndarray, np.ndarray],
+def _check_ci_strs(
+    ci_strs: tuple[np.ndarray, np.ndarray],
 ) -> tuple[np.ndarray, np.ndarray]:
     """Make sure the hamming weight is consistent in all determinants."""
-    addr_up, addr_dn = addresses
+    addr_up, addr_dn = ci_strs
     addr_up_ham = format(addr_up[0], "b").count("1")
     for i, addr in enumerate(addr_up):
         ham = format(addr, "b").count("1")
         if ham != addr_up_ham:
             raise ValueError(
-                f"Spin-up address in index 0 has hamming weight {addr_up_ham}, but address in "
+                f"Spin-up CI string in index 0 has hamming weight {addr_up_ham}, but CI string in "
                 f"index {i} has hamming weight {ham}."
             )
     addr_dn_ham = format(addr_dn[0], "b").count("1")
@@ -394,7 +445,7 @@ def _check_addresses(
         ham = format(addr, "b").count("1")
         if ham != addr_dn_ham:
             raise ValueError(
-                f"Spin-down address in index 0 has hamming weight {addr_dn_ham}, but address in "
+                f"Spin-down CI string in index 0 has hamming weight {addr_dn_ham}, but CI string in "
                 f"index {i} has hamming weight {ham}."
             )
 
diff --git a/qiskit_addon_sqd/qubit.py b/qiskit_addon_sqd/qubit.py
index e9f773c..bd9401f 100644
--- a/qiskit_addon_sqd/qubit.py
+++ b/qiskit_addon_sqd/qubit.py
@@ -73,7 +73,7 @@ def solve_qubit(
     if bitstring_matrix.shape[1] > 63:
         raise ValueError("Bitstrings (rows) in bitstring_matrix must have length < 64.")
 
-    # Projection requires the bitstring matrix be sorted in accordance with its address representation
+    # Projection requires the bitstring matrix be sorted in accordance with its base-10 representation
     bitstring_matrix = sort_and_remove_duplicates(bitstring_matrix)
 
     # Get a sparse representation of the projected operator
@@ -171,7 +171,7 @@ def matrix_elements_from_pauli(
 
     .. note::
        The bitstrings in the ``bitstring_matrix`` must be sorted and unique according
-       to their base-10 address representation. Otherwise the projection will return wrong
+       to their base-10 CI string representation. Otherwise the projection will return wrong
        results. This function does not explicitly check for uniqueness and order because
        this can be rather time consuming. See :func:`qiskit_addon_sqd.qubit.sort_and_remove_duplicates`
        for a simple way to ensure your bitstring matrix is well-formatted.