Adjust API to be more consistent in specifying # spin electrons and target hamming weights

docs/tutorials/01_chemistry_hamiltonian.ipynb

@@ -315,7 +315,11 @@
    "id": "851bc98e-9c08-4e78-9472-36301abc11d8",
    "metadata": {},
    "source": [
-    "First, we will transform the counts into a bitstring matrix and probability array for post-processing. Each row in the matrix represents one unique bitstring. Since qubits are normally indexed from the right of a bitstring, column ``0`` of the matrix represents qubit ``N``, and column ``N`` represents qubit ``0``."
+    "First, we will transform the counts into a bitstring matrix and probability array for post-processing.\n",
+    "\n",
+    "Each row in the matrix represents one unique bitstring. Since qubits are normally indexed from the right of a bitstring, column ``0`` represents qubit ``N-1``, and column ``N-1`` represents qubit ``0``, where ``N`` is the number of qubits.\n",
+    "\n",
+    "The alpha particles are represented in the column range ``[N / 2, N]``, and the beta particles are represented in the column range ``[0, N / 2)``."
    ]
   },
   {

qiskit_addon_sqd/configuration_recovery.py

@@ -30,16 +30,16 @@
 
 
 def post_select_by_hamming_weight(
-    bitstring_matrix: np.ndarray, hamming_left: int, hamming_right: int
+    bitstring_matrix: np.ndarray, hamming_right: int, hamming_left: int
 ) -> np.ndarray:
     """
     Post-select bitstrings based on the hamming weight of each half.
 
     Args:
         bitstring_matrix: A 2D array of ``bool`` representations of bit
             values such that each row represents a single bitstring
-        hamming_left: The target hamming weight of the left half of bitstrings
         hamming_right: The target hamming weight of the right half of bitstrings
+        hamming_left: The target hamming weight of the left half of bitstrings
 
     Returns:
         A mask signifying which samples were selected from the input matrix.
@@ -60,8 +60,8 @@ def recover_configurations(
     bitstring_matrix: np.ndarray,
     probabilities: Sequence[float],
     avg_occupancies: np.ndarray,
-    hamming_left: int,
-    hamming_right: int,
+    num_elec_a: int,
+    num_elec_b: int,
     *,
     rand_seed: int | None = None,
 ) -> tuple[np.ndarray, np.ndarray]:
@@ -78,15 +78,15 @@ def recover_configurations(
         avg_occupancies: A 1D array containing the mean occupancy of each orbital. It is assumed
             that ``avg_occupancies[i]`` corresponds to the orbital represented by column
             ``i`` in ``bitstring_matrix``.
-        hamming_left: The target hamming weight used for the left half of the bitstring
-        hamming_right: The target hamming weight used for the right half of the bitstring
+        num_elec_a: The number of spin-up electrons in the system.
+        num_elec_b: The number of spin-down electrons in the system.
         rand_seed: A seed to control random behavior
 
     Returns:
         A corrected bitstring matrix and an updated probability array
     """
-    if hamming_left < 0 or hamming_right < 0:
-        raise ValueError("Hamming weights must be non-negative integers.")
+    if num_elec_a < 0 or num_elec_b < 0:
+        raise ValueError("The numbers of electrons must be specified as non-negative integers.")
 
     # First, we need to flip the orbitals such that
 
@@ -95,8 +95,8 @@ def recover_configurations(
         bs_corrected = _bipartite_bitstring_correcting(
             bitstring,
             avg_occupancies,
-            hamming_left,
-            hamming_right,
+            num_elec_a,
+            num_elec_b,
             rand_seed=rand_seed,
         )
         bs_str = np.array2string(bs_corrected.astype(int), separator="")[1:-1]
@@ -170,8 +170,8 @@ def _p_flip_1_to_0(ratio_exp: float, occ: float, eps: float = 0.01) -> float:
 def _bipartite_bitstring_correcting(
     bit_array: np.ndarray,
     avg_occupancies: np.ndarray,
-    hamming_left: int,
     hamming_right: int,
+    hamming_left: int,
     rand_seed: int | None = None,
 ) -> np.ndarray:
     """
@@ -180,8 +180,8 @@ def _bipartite_bitstring_correcting(
     Args:
         bit_array: A 1D array of ``bool`` representations of bit values
         avg_occupancies: A 1D array containing the mean occupancy of each orbital.
-        hamming_left: The target hamming weight used for the left half of the bitstring
         hamming_right: The target hamming weight used for the right half of the bitstring
+        hamming_left: The target hamming weight used for the left half of the bitstring
         rand_seed: A seed to control random behavior
 
     Returns:

qiskit_addon_sqd/counts.py

@@ -90,8 +90,8 @@ def generate_counts_uniform(
 def generate_counts_bipartite_hamming(
     num_samples: int,
     num_bits: int,
-    hamming_left: int,
     hamming_right: int,
+    hamming_left: int,
     rand_seed: None | int = None,
 ) -> dict[str, int]:
     """
@@ -100,8 +100,8 @@ def generate_counts_bipartite_hamming(
     Args:
         num_samples: The number of samples to draw
         num_bits: The number of bits in the bitstrings
-        hamming_left: The hamming weight on the left half of each bitstring
         hamming_right: The hamming weight on the right half of each bitstring
+        hamming_left: The hamming weight on the left half of each bitstring
         rand_seed: A seed for controlling randomness
 
     Returns:
@@ -128,17 +128,17 @@ def generate_counts_bipartite_hamming(
     sample_dict: dict[str, int] = {}
     for _ in range(num_samples):
         # Pick random bits to flip such that the left and right hamming weights are correct
-        up_flips = np.random.choice(np.arange(num_bits // 2), hamming_left, replace=False).astype(
+        up_flips = np.random.choice(np.arange(num_bits // 2), hamming_right, replace=False).astype(
             "int"
         )
-        dn_flips = np.random.choice(np.arange(num_bits // 2), hamming_right, replace=False).astype(
+        dn_flips = np.random.choice(np.arange(num_bits // 2), hamming_left, replace=False).astype(
             "int"
         )
 
         # Create a bitstring with the chosen bits flipped
         bts_arr = np.zeros(num_bits)
-        bts_arr[up_flips] = 1
-        bts_arr[dn_flips + num_bits // 2] = 1
+        bts_arr[dn_flips] = 1
+        bts_arr[up_flips + num_bits // 2] = 1
         bts_arr = bts_arr.astype("int")
         bts = np.array2string(bts_arr, separator="")[1:-1]
 

qiskit_addon_sqd/subsampling.py

@@ -32,8 +32,8 @@
 def postselect_and_subsample(
     bitstring_matrix: np.ndarray,
     probabilities: np.ndarray,
-    hamming_left: int,
     hamming_right: int,
+    hamming_left: int,
     samples_per_batch: int,
     num_batches: int,
     rand_seed: int | None = None,
@@ -52,8 +52,8 @@ def postselect_and_subsample(
         bitstring_matrix: A 2D array of ``bool`` representations of bit
             values such that each row represents a single bitstring.
         probabilities: A 1D array specifying a probability distribution over the bitstrings
-        hamming_left: The target hamming weight for the left half of sampled bitstrings
         hamming_right: The target hamming weight for the right half of sampled bitstrings
+        hamming_left: The target hamming weight for the left half of sampled bitstrings
         samples_per_batch: The number of samples to draw for each batch
         num_batches: The number of batches to generate
         rand_seed: A seed to control random behavior

releasenotes/notes/subsample-hamming-76674dbaf6f411c2.yaml

@@ -0,0 +1,56 @@
+---
+prelude: >
+    This is a patch release which introduces a couple of small, but important, breaking changes to to the API. These changes allow for a more consistent pattern in specifying the number of alpha and beta electrons throughout both the chemistry and non-chemistry functions in the API.
+
+upgrade:
+  - |
+
+    The :func:`qiskit_addon_sqd.counts.generate_counts_bipartite_hamming`, :func:`qiskit_addon_sqd.subsampling.postselect_and_subsample`, and :func:`qiskit_addon_sqd.configuration_recovery.post_select_by_hamming_weight` now take the ``hamming_right`` positional argument before the ``hamming_left`` argument to better match the rest of the workflow.
+
+    To upgrade
+
+    .. code-block:: python
+
+        from qiskit_addon_sqd.configuration_recovery import post_select_by_hamming_weight
+        from qiskit_addon_sqd.subsampling import postselect_and_subsample
+        from qiskit_addon_sqd.counts import generate_counts_bipartite_hamming
+
+        counts = generate_counts_bipartite_hamming(num_samples, num_bits, num_elec_b, num_elec_a)
+
+        ...
+
+        bs_mat = post_select_by_hamming_weight(bs_mat_full, num_elec_b, num_elec_a)    
+
+        ...
+
+        batches = postselect_and_subsample(
+            bs_mat,
+            probs_arr,
+            num_elec_b,
+            num_elec_a,
+            samples_per_batch,
+            n_batches,
+        )
+
+    should be changed to
+
+    .. code-block:: python
+
+        from qiskit_addon_sqd.configuration_recovery import post_select_by_hamming_weight
+        from qiskit_addon_sqd.subsampling import postselect_and_subsample
+        from qiskit_addon_sqd.counts import generate_counts_bipartite_hamming
+
+        counts = generate_counts_bipartite_hamming(num_samples, num_bits, num_elec_a, num_elec_b)
+
+        bs_mat = post_select_by_hamming_weight(bs_mat_full, num_elec_a, num_elec_b)
+
+        ...
+
+        batches = postselect_and_subsample(
+            bs_mat,
+            probs_arr,
+            num_elec_a,
+            num_elec_b,
+            samples_per_batch,
+            n_batches,
+        )