Rename: OneMinusFidelity(...) -> MaximizeStateFidelity(...).loss_function

docs/apidocs/objective.rst

@@ -9,5 +9,5 @@ Objective function(s) (:mod:`qiskit_addon_aqc_tensor.objective`)
 
 .. currentmodule:: qiskit_addon_aqc_tensor.objective
 
-.. autoclass:: OneMinusFidelity
+.. autoclass:: MaximizeStateFidelity
    :special-members: __call__

docs/conf.py

@@ -104,7 +104,7 @@
 # ----------------------------------------------------------------------------------
 
 _inlined_apis = [
-    ("qiskit_addon_aqc_tensor.objective", "OneMinusFidelity"),
+    ("qiskit_addon_aqc_tensor.objective", "MaximizeStateFidelity"),
     ("qiskit_addon_aqc_tensor.simulation", "TensorNetworkState"),
     ("qiskit_addon_aqc_tensor.simulation", "TensorNetworkSimulationSettings"),
     ("qiskit_addon_aqc_tensor.simulation", "tensornetwork_from_circuit"),

docs/explanation/index.rst

@@ -120,7 +120,7 @@ For this reason, if you are attempting to experiment with AQC-Tensor on a toy pr
 Objective function
 ~~~~~~~~~~~~~~~~~~
 
-Currently, this addon provides one very simple objective function, :class:`.OneMinusFidelity`, which is equivalent to Eq. (7) in Ref. [1]_.  When an object of this class is called with an array of parameters, it will return both the value and the gradient of that objective function at that point in parameter space.
+Currently, this addon provides one very simple objective function, :class:`.MaximizeStateFidelity`, whose loss function is equivalent to Eq. (7) in Ref. [1]_.  When an object of this class is called with an array of parameters, it will return both the value and the gradient of that objective function at that point in parameter space.
 
 Gradient
 ~~~~~~~~

docs/how-tos/01_quimb_tnoptimizer.ipynb

@@ -153,9 +153,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit_addon_aqc_tensor.objective import OneMinusFidelity\n",
+    "from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity\n",
     "\n",
-    "objective = OneMinusFidelity(target_tns, None, None)"
+    "objective = MaximizeStateFidelity(target_tns, None, None)"
    ]
   },
   {

docs/tutorials/01_initial_state_aqc.ipynb

@@ -371,7 +371,7 @@
    "source": [
     "### Optimize the parameters of the ansatz using MPS calculations\n",
     "\n",
-    "Here, we minimize the simplest possible cost function, `OneMinusFidelity`, by using the L-BFGS optimizer from scipy.\n",
+    "Here, we minimize the simplest possible cost function, `MaximizeStateFidelity`, by using the L-BFGS optimizer from scipy.\n",
     "\n",
     "We choose a stopping point for the fidelity such that it will be above what the comparison circuit would have been, without using AQC.  Once this is reached, the compressed circuit has less Trotter error _and_ less depth than the original circuit.  Given more processing time, further optimization steps can be performed to bring the fidelity even higher."
    ]
@@ -417,9 +417,9 @@
    "source": [
     "from scipy.optimize import OptimizeResult, minimize\n",
     "\n",
-    "from qiskit_addon_aqc_tensor.objective import OneMinusFidelity\n",
+    "from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity\n",
     "\n",
-    "objective = OneMinusFidelity(aqc_target_mps, aqc_ansatz, simulator_settings)\n",
+    "objective = MaximizeStateFidelity(aqc_target_mps, aqc_ansatz, simulator_settings)\n",
     "\n",
     "stopping_point = 1 - comparison_fidelity\n",
     "\n",
@@ -432,7 +432,7 @@
     "\n",
     "\n",
     "result = minimize(\n",
-    "    objective,\n",
+    "    objective.loss_function,\n",
     "    aqc_initial_parameters,\n",
     "    method=\"L-BFGS-B\",\n",
     "    jac=True,\n",

qiskit_addon_aqc_tensor/objective.py

@@ -12,7 +12,7 @@
 
 """Code for building and evaluating objective functions used for AQC parameter optimization.
 
-Currently, this module provides the simplest possible objective function, :class:`.OneMinusFidelity`.
+Currently, this module provides the simplest possible objective function, :class:`.MaximizeStateFidelity`.
 """
 
 from __future__ import annotations
@@ -29,7 +29,7 @@
     )
 
 
-class OneMinusFidelity:
+class MaximizeStateFidelity:
     r"""Simplest possible objective function for use with AQC-Tensor.
 
     Its definition is given by Eq. (7) in `arXiv:2301.08609v6 <https://arxiv.org/abs/2301.08609v6>`__:
@@ -71,14 +71,19 @@ def __init__(
 
             self._preprocessed = _preprocess_for_gradient(self, settings)
 
-    def __call__(self, x: np.ndarray) -> tuple[float, np.ndarray]:
+    def loss_function(self, x: np.ndarray) -> tuple[float, np.ndarray]:
         """Evaluate ``(objective_value, gradient)`` of function at point ``x``."""
         from .simulation.abstract import _compute_objective_and_gradient
 
         return _compute_objective_and_gradient(
             self, self._simulation_settings, self._preprocessed, x
         )
 
+    # FIXME: deprecate
+    def __call__(self, x: np.ndarray) -> tuple[float, np.ndarray]:
+        """Evaluate ``(objective_value, gradient)`` of function at point ``x``."""
+        return self.loss_function(x)
+
     @property
     def target(self) -> TensorNetworkState:
         """Target tensor network."""
@@ -87,5 +92,5 @@ def target(self) -> TensorNetworkState:
 
 # Reminder: update the RST file in docs/apidocs when adding new interfaces.
 __all__ = [
-    "OneMinusFidelity",
+    "MaximizeStateFidelity",
 ]

qiskit_addon_aqc_tensor/simulation/aer/gradient.py

@@ -20,7 +20,7 @@
     QuantumCircuit,
 )
 
-from ...objective import OneMinusFidelity
+from ...objective import MaximizeStateFidelity
 from ..abstract import (
     apply_circuit_to_state,
     compute_overlap,
@@ -44,7 +44,7 @@ def _preprocess_for_gradient(objective, settings: Union[QiskitAerSimulationSetti
 
 @dispatch
 def _compute_objective_and_gradient(
-    objective: OneMinusFidelity,
+    objective: MaximizeStateFidelity,
     settings: Union[QiskitAerSimulationSettings, AerSimulator],
     preprocess_info,
     x: np.ndarray,

qiskit_addon_aqc_tensor/simulation/quimb/__init__.py

@@ -27,7 +27,7 @@
 from wrapt import register_post_import_hook
 
 from ...ansatz_generation import AnsatzBlock
-from ...objective import OneMinusFidelity
+from ...objective import MaximizeStateFidelity
 from ..abstract import TensorNetworkSimulationSettings
 from ..explicit_gradient import (
     compute_gradient_of_tensornetwork_overlap,
@@ -309,7 +309,7 @@ def __init__(self, objective, settings):
 
 @dispatch
 def _compute_objective_and_gradient(
-    objective: OneMinusFidelity,
+    objective: MaximizeStateFidelity,
     settings: QuimbSimulator,
     preprocess_info: _ExplicitGradientContext,
     x: np.ndarray,
@@ -347,7 +347,7 @@ def __init__(self, objective, settings):
 
 @dispatch
 def _compute_objective_and_gradient(
-    _: OneMinusFidelity,
+    _: MaximizeStateFidelity,
     __: QuimbSimulator,
     preprocess_info: _QuimbGradientContext,
     qiskit_parameter_values: np.ndarray,
@@ -371,7 +371,7 @@ def _compute_objective_and_gradient(
 
 
 @dispatch
-def tnoptimizer_objective_kwargs(objective: OneMinusFidelity, /) -> dict[str, Any]:
+def tnoptimizer_objective_kwargs(objective: MaximizeStateFidelity, /) -> dict[str, Any]:
     """Return keyword arguments for use with :func:`~quimb.tensor.TNOptimizer`.
 
     - ``loss_fn``
@@ -383,12 +383,12 @@ def tnoptimizer_objective_kwargs(objective: OneMinusFidelity, /) -> dict[str, An
     if isinstance(target, qtn.Circuit):
         target = target.psi
     return {
-        "loss_fn": oneminusfidelity_loss_fn,
+        "loss_fn": maximize_state_fidelity_loss_function,
         "loss_kwargs": {"target": target},
     }
 
 
-def oneminusfidelity_loss_fn(
+def maximize_state_fidelity_loss_function(
     circ: quimb.tensor.Circuit, /, *, target: quimb.tensor.TensorNetworkGenVector
 ):
     """Loss function for use with Quimb, compatible with automatic differentiation.

test/simulation/test_gradient.py

@@ -18,7 +18,7 @@
 from qiskit.circuit import Parameter
 from qiskit.circuit.library import EfficientSU2, TwoLocal
 
-from qiskit_addon_aqc_tensor.objective import OneMinusFidelity
+from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity
 from qiskit_addon_aqc_tensor.simulation import (
     apply_circuit_to_state,
     compute_overlap,
@@ -108,7 +108,7 @@ def vdagger_rhs(thetas):
         bound_qc = qc.assign_parameters(thetas)
         return apply_circuit_to_state(bound_qc.inverse(), rhs_mps, settings)
 
-    objective = OneMinusFidelity(rhs_mps, qc, settings)
+    objective = MaximizeStateFidelity(rhs_mps, qc, settings)
     preprocess_info = _preprocess_for_gradient(objective, settings)
     _, grad = _compute_objective_and_gradient(objective, settings, preprocess_info, thetas)
 

test/test_aqc_workflows.py

@@ -22,7 +22,7 @@
     AnsatzBlock,
     generate_ansatz_from_circuit,
 )
-from qiskit_addon_aqc_tensor.objective import OneMinusFidelity
+from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity
 from qiskit_addon_aqc_tensor.simulation import (
     compute_overlap,
     tensornetwork_from_circuit,
@@ -55,9 +55,9 @@ def test_basic_workflow(available_backend_fixture, circuit_pair):
     ansatz, initial_parameters = generate_ansatz_from_circuit(
         good_circuit, qubits_initially_zero=True
     )
-    objective = OneMinusFidelity(target_mps, ansatz, simulator_settings)
+    objective = MaximizeStateFidelity(target_mps, ansatz, simulator_settings)
     result = minimize(
-        objective,
+        objective.loss_function,
         initial_parameters,
         method="L-BFGS-B",
         jac=True,

test/test_star_imports.py

@@ -27,7 +27,7 @@ def test_star_imports():
     assert generate_ansatz_from_circuit.__name__ == "generate_ansatz_from_circuit"
     assert KAK.__name__ == "KAK"
     assert preprocess_circuit_for_backtracking.__name__ == "preprocess_circuit_for_backtracking"
-    assert OneMinusFidelity.__name__ == "OneMinusFidelity"
+    assert MaximizeStateFidelity.__name__ == "MaximizeStateFidelity"
     assert TensorNetworkState.__name__ == "TensorNetworkState"
     assert QiskitAerMPS.__name__ == "QiskitAerMPS"
     assert QuimbSimulator.__name__ == "QuimbSimulator"