Introduce bootstrap functionality

circuit_knitting/utils/__init__.py

@@ -18,6 +18,12 @@
 
 .. automodule:: circuit_knitting.utils.bitwise
 
+=================================================================
+Bootstrapping (:mod:`circuit_knitting.utils.bootstrap`)
+=================================================================
+
+.. automodule:: circuit_knitting.utils.bootstrap
+
 =====================================================================
 Iteration utilities (:mod:`circuit_knitting.utils.iteration`)
 =====================================================================

circuit_knitting/utils/bootstrap.py

@@ -0,0 +1,52 @@
+# This code is a Qiskit project.
+
+# (C) Copyright IBM 2024.
+
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""
+Bootstrapping utilities.
+
+.. currentmodule:: circuit_knitting.utils.bootstrap
+
+.. autosummary::
+   :toctree: ../stubs/
+
+   bootstrap_sampler_result
+"""
+
+from __future__ import annotations
+
+import numpy as np
+from qiskit.primitives import PrimitiveResult, SamplerPubResult, DataBin, BitArray
+
+
+def _bootstrap_pubresult(pr: SamplerPubResult, /, rng: np.random.Generator):
+    num_shots = next(iter(pr.data.values())).num_shots
+    indices = rng.choice(range(num_shots), num_shots, replace=True)
+    new_data: dict[str, BitArray] = {}
+    for k, v in pr.data.items():
+        new_data[k] = BitArray(v.array[indices, ...], v.num_bits)
+    return SamplerPubResult(DataBin(**new_data))
+
+
+def bootstrap_sampler_result(
+    result: PrimitiveResult[SamplerPubResult],
+    /,
+    seed: int | np.random.Generator | None = None,
+):
+    """Construct a single synthetic dataset from a :class:`PrimitiveResult`."""
+    if isinstance(seed, np.random.Generator):
+        rng = seed
+    else:
+        rng = np.random.default_rng(seed)
+
+    return PrimitiveResult(
+        [_bootstrap_pubresult(pubresult, rng) for pubresult in result],
+        {**result.metadata, "bootstrapped": True},
+    )