From 09394edc5b65b9428a0c1a9b6b2cd387068956fd Mon Sep 17 00:00:00 2001
From: SaashaJoshi <saashajoshi08@gmail.com>
Date: Fri, 3 Nov 2023 23:55:37 -0700
Subject: [PATCH 1/7] add init file for gate module

---
 .../models/tensor_network_circuits/__init__.py             | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/quantum_image_processing/models/tensor_network_circuits/__init__.py b/quantum_image_processing/models/tensor_network_circuits/__init__.py
index e69de29..6b8a71d 100644
--- a/quantum_image_processing/models/tensor_network_circuits/__init__.py
+++ b/quantum_image_processing/models/tensor_network_circuits/__init__.py
@@ -0,0 +1,7 @@
+from .ttn import TTN
+from .mera import MERA
+
+__all__ = [
+    "TTN",
+    "MERA",
+]
\ No newline at end of file

From 887b29223efd3d2276b4fbb7b51082e00c347b30 Mon Sep 17 00:00:00 2001
From: SaashaJoshi <saashajoshi08@gmail.com>
Date: Fri, 3 Nov 2023 23:56:57 -0700
Subject: [PATCH 2/7] add doc strings to ttn

---
 .../models/tensor_network_circuits/ttn.py     | 106 +++++++++++-------
 1 file changed, 68 insertions(+), 38 deletions(-)

diff --git a/quantum_image_processing/models/tensor_network_circuits/ttn.py b/quantum_image_processing/models/tensor_network_circuits/ttn.py
index 9660950..f148cdb 100644
--- a/quantum_image_processing/models/tensor_network_circuits/ttn.py
+++ b/quantum_image_processing/models/tensor_network_circuits/ttn.py
@@ -1,65 +1,64 @@
+"""Tree Tensor Network (TTN)"""
 from __future__ import annotations
 from typing import Callable
 import numpy as np
-from qiskit import QuantumCircuit
 from qiskit.circuit import QuantumCircuit, QuantumRegister, ParameterVector
 from quantum_image_processing.gates.two_qubit_unitary import TwoQubitUnitary
 
 
 class TTN:
     """
-    Implements a Tree Tensor Network (TTN) as given by
-    Grant et al. (2018).
+    Implements a Tree Tensor Network (TTN) structure class with
+    alternative unitary qubit parameterization.
 
-    The model architecture only consists of a hierarchical
-    TTN model. It cannot be classified as a QCNN since
-    there is no distinction between conv and pooling layers.
+    References:
+        [1] Grant et al. (2018).
     """
 
-    def __init__(self, img_dim):
+    def __init__(self, img_dim: int):
+        """
+        Initializes the TTN class with given input variables.
+
+        Args:
+            img_dim (int): product of dimensions of the input data.
+            For example,
+                for a 2x2 image, img_dim = 4.
+        """
         self.img_dim = img_dim
 
     def ttn_simple(self, complex_structure: bool = True) -> QuantumCircuit:
         """
-        Rotations here can be either real or complex.
-
-        For real rotations only RY gates are used since
-        the gate has no complex rotations involved.
-        For complex rotations, a combination of RZ and RY
-        gates are used.
+        Implements a TTN network with simple alternative
+        parameterization as given in [1].
 
-        I HAVE NO IDEA WHY I CHOSE THESE. THE SELECTION
-        OF UNITARY GATES IS COMPLETELY VOLUNTARY.
+        Args:
+            complex_structure (default=True): boolean marker
+            for real or complex gate parameterization.
 
-        PennyLane implements a TTN template with only RX gates.
-
-        :return:
+        Returns:
+            QuantumCircuit: quantum circuit with unitary gates
+            represented by simple parameterization.
         """
         param_vector = ParameterVector("theta", 2 * self.img_dim - 1)
         param_vector_copy = param_vector
         return self.ttn_backbone(
-            TwoQubitUnitary().simple_parameterization, param_vector_copy, complex_structure
+            TwoQubitUnitary().simple_parameterization,
+            param_vector_copy,
+            complex_structure,
         )
 
     def ttn_general(self, complex_structure: bool = True) -> QuantumCircuit:
         """
-        Two qubit gates built from referencing a paper by
-        Vatan et al. (2004).
-
-        As stated in the paper:
-        "A general two-qubit quantum computation, up to a global phase,
-        can be constructed using at most 3 CNOT gates and 15 elementary
-        one-qubit gates from the family {Ry , Rz}."
-        and
-
-        Theorem 3. Every two-qubit quantum gate in SO(4) (i.e. real gate)
-        can be realized by a circuit consisting of 12 elementary
-        one-qubit gates and 2 CNOT gates.
-
-        Theorem 4. Every two-qubit quantum gate in O(4) with determinant
-        equal to −1 can be realized by a circuit consisting of 12 elementary
-        gates and 2 CNOT gates and one SWAP gate
-        :return:
+        Implements a TTN network with general alternative
+        parameterization as given in [1].
+
+        Args:
+            complex_structure (default=True): boolean marker
+            for real or complex gate parameterization.
+
+        Returns:
+            QuantumCircuit: quantum circuit with unitary gates
+            represented by general parameterization.
         """
         # Check number of params here.
         if complex_structure:
@@ -70,12 +69,24 @@ def ttn_general(self, complex_structure: bool = True) -> QuantumCircuit:
             param_vector_copy = param_vector
 
         return self.ttn_backbone(
-            TwoQubitUnitary().simple_parameterization, param_vector_copy, complex_structure
+            TwoQubitUnitary().simple_parameterization,
+            param_vector_copy,
+            complex_structure,
         )
 
-    def ttn_with_aux(self):
+    def ttn_with_aux(self, complex_structure: bool = True):
         """
         TODO: Find the implementation procedure for this.
+        Implements a TTN network with alternative parameterization
+        that requires an auxiliary qubit, as given in [1].
+
+        Args:
+            complex_structure (default=True): boolean marker for
+            real or complex gate parameterization.
+
+        Returns:
+            QuantumCircuit: quantum circuit with unitary gates
+            represented by general parameterization.
         """
 
     def ttn_backbone(
@@ -84,6 +95,25 @@ def ttn_backbone(
         param_vector_copy: ParameterVector,
         complex_structure: bool = True,
     ) -> QuantumCircuit:
+        """
+        Lays out the TTN structure by progressively building layers
+        of unitary gates with their alternative parameterization.
+
+        Args:
+            gate_structure (Callable): a callable function that implements
+            either one of the three available unitary gate parameterization -
+            simple, general or auxiliary.
+
+            param_vector_copy (ParameterVector): copy of the parameter
+            vector list.
+
+            complex_structure (default=True): boolean marker for
+            real or complex gate parameterization.
+
+        Returns:
+            QuantumCircuit: quantum circuit with unitary gates
+            represented by general parameterization.
+        """
         ttn_qr = QuantumRegister(size=self.img_dim)
         ttn_circ = QuantumCircuit(ttn_qr)
 

From a288d23f8cafe38a126893dcffbfbdf9a439b77b Mon Sep 17 00:00:00 2001
From: SaashaJoshi <saashajoshi08@gmail.com>
Date: Sun, 5 Nov 2023 21:17:34 -0800
Subject: [PATCH 3/7] lint edits

---
 .../data_encoder/__init__.py                  |  9 +++
 .../data_encoder/amplitude_encoder.py         |  2 -
 .../data_encoder/angle_encoder.py             |  1 +
 .../image_representations/__init__.py         |  4 ++
 .../image_representations/frqi.py             | 70 +++++++++----------
 .../image_representations/neqr.py             | 70 +++++++++----------
 .../data_loader/__init__.py                   |  9 +++
 quantum_image_processing/gates/__init__.py    |  2 +-
 .../gates/two_qubit_unitary.py                |  1 +
 quantum_image_processing/models/__init__.py   |  5 ++
 .../models/neural_networks/__init__.py        |  5 ++
 .../neural_networks/convolutional/__init__.py |  4 ++
 .../generative_adversarial/__init__.py        |  4 ++
 .../models/neural_networks/neural_network.py  |  0
 .../tensor_network_circuits/__init__.py       |  6 +-
 .../models/tensor_network_circuits/mera.py    | 17 ++++-
 .../variational_classifiers/__init__.py       |  5 ++
 requirements.txt                              |  9 ++-
 18 files changed, 139 insertions(+), 84 deletions(-)
 create mode 100644 quantum_image_processing/models/neural_networks/neural_network.py

diff --git a/quantum_image_processing/data_encoder/__init__.py b/quantum_image_processing/data_encoder/__init__.py
index e69de29..9b71862 100644
--- a/quantum_image_processing/data_encoder/__init__.py
+++ b/quantum_image_processing/data_encoder/__init__.py
@@ -0,0 +1,9 @@
+"""
+Data Encoders (module: quantum_image_processing.data_encoder)
+"""
+
+from .angle_encoder import angle_encoding
+
+__all__ = [
+    "angle_encoding",
+]
diff --git a/quantum_image_processing/data_encoder/amplitude_encoder.py b/quantum_image_processing/data_encoder/amplitude_encoder.py
index 4348f98..e69de29 100644
--- a/quantum_image_processing/data_encoder/amplitude_encoder.py
+++ b/quantum_image_processing/data_encoder/amplitude_encoder.py
@@ -1,2 +0,0 @@
-def amplitude_encoding():
-    pass
diff --git a/quantum_image_processing/data_encoder/angle_encoder.py b/quantum_image_processing/data_encoder/angle_encoder.py
index fc48d9f..5a78222 100644
--- a/quantum_image_processing/data_encoder/angle_encoder.py
+++ b/quantum_image_processing/data_encoder/angle_encoder.py
@@ -1,3 +1,4 @@
+"""Angle Encoder"""
 from qiskit.circuit import QuantumCircuit, ParameterVector
 
 
diff --git a/quantum_image_processing/data_encoder/image_representations/__init__.py b/quantum_image_processing/data_encoder/image_representations/__init__.py
index bf3f9f3..dd2857b 100644
--- a/quantum_image_processing/data_encoder/image_representations/__init__.py
+++ b/quantum_image_processing/data_encoder/image_representations/__init__.py
@@ -1,3 +1,7 @@
+"""
+Quantum Image Representations (module: quantum_image_processing.data_encoder.image_representations)
+"""
+
 from .frqi import FRQI
 from .neqr import NEQR
 
diff --git a/quantum_image_processing/data_encoder/image_representations/frqi.py b/quantum_image_processing/data_encoder/image_representations/frqi.py
index 45085e6..814a0f4 100644
--- a/quantum_image_processing/data_encoder/image_representations/frqi.py
+++ b/quantum_image_processing/data_encoder/image_representations/frqi.py
@@ -1,9 +1,7 @@
 from __future__ import annotations
 import math
 import numpy as np
-from qiskit import Aer, execute
-from qiskit.circuit import QuantumCircuit, QuantumRegister, ClassicalRegister
-from qiskit.visualization import plot_histogram
+from qiskit.circuit import QuantumCircuit, QuantumRegister
 
 
 class FRQI:
@@ -51,20 +49,20 @@ def _color_info(self, pixel_pos: int) -> QuantumCircuit:
 
         return circ
 
-    # TODO: Delete this function from here. Make a separate file.
-    def _measure_circ(self, circ: QuantumCircuit) -> QuantumCircuit:
-        # Append measurement gates to the circuit
-        qr = QuantumRegister(self.feature_dim + 1)
-        cr = ClassicalRegister(self.feature_dim + 1)
-
-        meas_circ = QuantumCircuit(qr, cr)
-        meas_circ.measure(
-            list(range(self.feature_dim + 1)),
-            list(range(self.feature_dim + 1)),
-        )
-        meas_circ = meas_circ.compose(circ, range(self.feature_dim + 1), front=True)
-
-        return meas_circ
+    # # TODO: Delete this function from here. Make a separate file.
+    # def _measure_circ(self, circ: QuantumCircuit) -> QuantumCircuit:
+    #     # Append measurement gates to the circuit
+    #     qr = QuantumRegister(self.feature_dim + 1)
+    #     cr = ClassicalRegister(self.feature_dim + 1)
+    #
+    #     meas_circ = QuantumCircuit(qr, cr)
+    #     meas_circ.measure(
+    #         list(range(self.feature_dim + 1)),
+    #         list(range(self.feature_dim + 1)),
+    #     )
+    #     meas_circ = meas_circ.compose(circ, range(self.feature_dim + 1), front=True)
+    #
+    #     return meas_circ
 
     def frqi(self, measure=True) -> QuantumCircuit:
         """Builds the FRQI image representation on a circuit.
@@ -103,28 +101,28 @@ def frqi(self, measure=True) -> QuantumCircuit:
             )
             circ.barrier()
 
-        if measure:
-            circ = self._measure_circ(circ)
+        # if measure:
+        #     circ = self._measure_circ(circ)
 
         return circ
 
-    # TODO: Remove the following function from this file.
-    @staticmethod
-    def get_simulator_result(
-        circ: QuantumCircuit,
-        backend: str = "qasm_simulator",
-        shots: int = 1024,
-        plot_counts=True,
-    ):
-        backend = Aer.get_backend(backend)
-        job = execute(circ, backend=backend, shots=shots)
-        results = job.result()
-        counts = results.get_counts()
-
-        if plot_counts:
-            plot_histogram(counts)
-
-        return counts
+    # # TODO: Remove the following function from this file.
+    # @staticmethod
+    # def get_simulator_result(
+    #     circ: QuantumCircuit,
+    #     backend: str = "qasm_simulator",
+    #     shots: int = 1024,
+    #     plot_counts=True,
+    # ):
+    #     backend = Aer.get_backend(backend)
+    #     job = execute(circ, backend=backend, shots=shots)
+    #     results = job.result()
+    #     counts = results.get_counts()
+    #
+    #     if plot_counts:
+    #         plot_histogram(counts)
+    #
+    #     return counts
 
     def qic(self):
         pass
diff --git a/quantum_image_processing/data_encoder/image_representations/neqr.py b/quantum_image_processing/data_encoder/image_representations/neqr.py
index d4767f6..2449825 100644
--- a/quantum_image_processing/data_encoder/image_representations/neqr.py
+++ b/quantum_image_processing/data_encoder/image_representations/neqr.py
@@ -1,9 +1,7 @@
 from __future__ import annotations
 import math
 import numpy as np
-from qiskit import Aer, execute
-from qiskit.circuit import QuantumCircuit, QuantumRegister, ClassicalRegister
-from qiskit.visualization import plot_histogram
+from qiskit.circuit import QuantumCircuit, QuantumRegister
 
 
 class NEQR:
@@ -46,21 +44,21 @@ def _color_info(self, pixel_pos: int) -> QuantumCircuit:
 
         return circ
 
-    def _measure_circ(self, circ: QuantumCircuit) -> QuantumCircuit:
-        # Append measurement gates to the circuit
-        qr = QuantumRegister(self.feature_dim + self.q)
-        cr = ClassicalRegister(self.feature_dim + self.q)
-
-        meas_circ = QuantumCircuit(qr, cr)
-        meas_circ.measure(
-            list(range(self.feature_dim + self.q)),
-            list(range(self.feature_dim + self.q)),
-        )
-        meas_circ = meas_circ.compose(
-            circ, range(self.feature_dim + self.q), front=True
-        )
-
-        return meas_circ
+    # def _measure_circ(self, circ: QuantumCircuit) -> QuantumCircuit:
+    #     # Append measurement gates to the circuit
+    #     qr = QuantumRegister(self.feature_dim + self.q)
+    #     cr = ClassicalRegister(self.feature_dim + self.q)
+    #
+    #     meas_circ = QuantumCircuit(qr, cr)
+    #     meas_circ.measure(
+    #         list(range(self.feature_dim + self.q)),
+    #         list(range(self.feature_dim + self.q)),
+    #     )
+    #     meas_circ = meas_circ.compose(
+    #         circ, range(self.feature_dim + self.q), front=True
+    #     )
+    #
+    #     return meas_circ
 
     def image_encoding(self, measure=True) -> QuantumCircuit:
         qr = QuantumRegister(self.feature_dim + self.q)
@@ -91,27 +89,27 @@ def image_encoding(self, measure=True) -> QuantumCircuit:
                 range(self.feature_dim),
             )
 
-        if measure:
-            circ = self._measure_circ(circ)
+        # if measure:
+        #     circ = self._measure_circ(circ)
 
         return circ
 
-    @staticmethod
-    def get_simulator_result(
-        circ: QuantumCircuit,
-        backend: str = "qasm_simulator",
-        shots: int = 1024,
-        plot_counts=True,
-    ) -> list:
-        backend = Aer.get_backend(backend)
-        job = execute(circ, backend=backend, shots=shots)
-        results = job.result()
-        counts = results.get_counts()
-
-        if plot_counts:
-            plot_histogram(counts)
-
-        return counts
+    # @staticmethod
+    # def get_simulator_result(
+    #     circ: QuantumCircuit,
+    #     backend: str = "qasm_simulator",
+    #     shots: int = 1024,
+    #     plot_counts=True,
+    # ) -> list:
+    #     backend = Aer.get_backend(backend)
+    #     job = execute(circ, backend=backend, shots=shots)
+    #     results = job.result()
+    #     counts = results.get_counts()
+    #
+    #     if plot_counts:
+    #         plot_histogram(counts)
+    #
+    #     return counts
 
     def qic(self):
         pass
diff --git a/quantum_image_processing/data_loader/__init__.py b/quantum_image_processing/data_loader/__init__.py
index e69de29..5173f8d 100644
--- a/quantum_image_processing/data_loader/__init__.py
+++ b/quantum_image_processing/data_loader/__init__.py
@@ -0,0 +1,9 @@
+"""
+Data Loader (module: quantum_image_processing.data_loader)
+"""
+
+from .mnist_data_loader import load_mnist_data
+
+__all__ = [
+    "load_mnist_data",
+]
diff --git a/quantum_image_processing/gates/__init__.py b/quantum_image_processing/gates/__init__.py
index 715ee96..caa0d62 100644
--- a/quantum_image_processing/gates/__init__.py
+++ b/quantum_image_processing/gates/__init__.py
@@ -1,6 +1,6 @@
 """
 Gate implementations, including unitary gates and their alternative parameterization.
-(mod: quantum_image_processing.gates)
+(module: quantum_image_processing.gates)
 """
 
 from .unitary_block import Unitary
diff --git a/quantum_image_processing/gates/two_qubit_unitary.py b/quantum_image_processing/gates/two_qubit_unitary.py
index 7a6e6a2..2768b03 100644
--- a/quantum_image_processing/gates/two_qubit_unitary.py
+++ b/quantum_image_processing/gates/two_qubit_unitary.py
@@ -9,6 +9,7 @@ class TwoQubitUnitary(Unitary):
     """
     Implements two qubit unitary with alternative parameterizations.
     """
+
     def simple_parameterization(
         self,
         circuit: QuantumCircuit,
diff --git a/quantum_image_processing/models/__init__.py b/quantum_image_processing/models/__init__.py
index e69de29..7476b45 100644
--- a/quantum_image_processing/models/__init__.py
+++ b/quantum_image_processing/models/__init__.py
@@ -0,0 +1,5 @@
+"""
+Quantum algorithms and models, including neural networks, tensor network
+quantum circuits, variational classifiers, and more.
+(module: quantum_image_processing.models)
+"""
diff --git a/quantum_image_processing/models/neural_networks/__init__.py b/quantum_image_processing/models/neural_networks/__init__.py
index e69de29..5684eab 100644
--- a/quantum_image_processing/models/neural_networks/__init__.py
+++ b/quantum_image_processing/models/neural_networks/__init__.py
@@ -0,0 +1,5 @@
+"""
+Quantum Neural Networks, including convolutional and
+generative adversarial neural networks
+(module: quantum_image_processing.models.neural_networks)
+"""
diff --git a/quantum_image_processing/models/neural_networks/convolutional/__init__.py b/quantum_image_processing/models/neural_networks/convolutional/__init__.py
index e69de29..3a088a4 100644
--- a/quantum_image_processing/models/neural_networks/convolutional/__init__.py
+++ b/quantum_image_processing/models/neural_networks/convolutional/__init__.py
@@ -0,0 +1,4 @@
+"""
+Convolutional Neural Networks
+(module: quantum_image_processing.models.neural_networks.convolutional)
+"""
diff --git a/quantum_image_processing/models/neural_networks/generative_adversarial/__init__.py b/quantum_image_processing/models/neural_networks/generative_adversarial/__init__.py
index e69de29..14913a6 100644
--- a/quantum_image_processing/models/neural_networks/generative_adversarial/__init__.py
+++ b/quantum_image_processing/models/neural_networks/generative_adversarial/__init__.py
@@ -0,0 +1,4 @@
+"""
+Generative Adversarial Neural Networks
+(module: quantum_image_processing.models.neural_networks.generative_adversarial)
+"""
diff --git a/quantum_image_processing/models/neural_networks/neural_network.py b/quantum_image_processing/models/neural_networks/neural_network.py
new file mode 100644
index 0000000..e69de29
diff --git a/quantum_image_processing/models/tensor_network_circuits/__init__.py b/quantum_image_processing/models/tensor_network_circuits/__init__.py
index 6b8a71d..25b1d20 100644
--- a/quantum_image_processing/models/tensor_network_circuits/__init__.py
+++ b/quantum_image_processing/models/tensor_network_circuits/__init__.py
@@ -1,7 +1,11 @@
+"""
+Tensor Network Quantum Circuits (module: quantum_image_processing.models.tensor_network_circuits)
+"""
+
 from .ttn import TTN
 from .mera import MERA
 
 __all__ = [
     "TTN",
     "MERA",
-]
\ No newline at end of file
+]
diff --git a/quantum_image_processing/models/tensor_network_circuits/mera.py b/quantum_image_processing/models/tensor_network_circuits/mera.py
index 78e2b2a..a1662b0 100644
--- a/quantum_image_processing/models/tensor_network_circuits/mera.py
+++ b/quantum_image_processing/models/tensor_network_circuits/mera.py
@@ -26,7 +26,11 @@ def mera_simple(self, complex_structure: bool = True) -> QuantumCircuit:
             int(self.img_dim / 2 * (self.img_dim / 2 + 1)) + 3,
         )
         param_vector_copy = param_vector
-        return self.mera_backbone(TwoQubitUnitary().simple_parameterization, param_vector_copy, complex_structure)
+        return self.mera_backbone(
+            TwoQubitUnitary().simple_parameterization,
+            param_vector_copy,
+            complex_structure,
+        )
 
     # Check number of params here.
     def mera_general(self, complex_structure: bool = True) -> QuantumCircuit:
@@ -36,10 +40,17 @@ def mera_general(self, complex_structure: bool = True) -> QuantumCircuit:
         else:
             param_vector = ParameterVector("theta", 10 * self.img_dim - 1)
             param_vector_copy = param_vector
-        return self.mera_backbone(TwoQubitUnitary().general_parameterization, param_vector_copy, complex_structure)
+        return self.mera_backbone(
+            TwoQubitUnitary().general_parameterization,
+            param_vector_copy,
+            complex_structure,
+        )
 
     def mera_backbone(
-            self, gate_structure: Callable, param_vector_copy: ParameterVector, complex_structure: bool = True
+        self,
+        gate_structure: Callable,
+        param_vector_copy: ParameterVector,
+        complex_structure: bool = True,
     ) -> QuantumCircuit:
         mera_qr = QuantumRegister(size=self.img_dim)
         mera_circ = QuantumCircuit(mera_qr)
diff --git a/quantum_image_processing/models/variational_classifiers/__init__.py b/quantum_image_processing/models/variational_classifiers/__init__.py
index e69de29..b29a49f 100644
--- a/quantum_image_processing/models/variational_classifiers/__init__.py
+++ b/quantum_image_processing/models/variational_classifiers/__init__.py
@@ -0,0 +1,5 @@
+"""
+Quantum Variational Classifiers, including kernel estimator, and
+data re-uploader.
+(module: quantum_image_processing.models.variational_classifiers)
+"""
diff --git a/requirements.txt b/requirements.txt
index 8bb428b..4d13e0f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,4 @@
-matplotlib~=3.6.2
-qiskit~=0.39.4
-torchvision~=0.14.1
-torch~=1.13.1
-numpy~=1.24.1
\ No newline at end of file
+matplotlib==3.7.3
+qiskit==0.45.0
+qiskit_terra==0.45.0
+torchvision==0.16.0
\ No newline at end of file

From 0863a0297bd4cf3675c895cbc1759bfaac44968e Mon Sep 17 00:00:00 2001
From: SaashaJoshi <saashajoshi08@gmail.com>
Date: Thu, 16 Nov 2023 18:44:27 -0800
Subject: [PATCH 4/7] pip install requirements.txt to solve linting error

---
 .github/workflows/pylint.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml
index c9c962e..5c66d6a 100644
--- a/.github/workflows/pylint.yml
+++ b/.github/workflows/pylint.yml
@@ -18,6 +18,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install pylint
+        pip install -r requirements.txt
     - name: Analysing the code with pylint
       run: |
         pylint $(git ls-files '*.py')

From 08e52f804a1653329c78eaad0df4f45909f21bc0 Mon Sep 17 00:00:00 2001
From: SaashaJoshi <saashajoshi08@gmail.com>
Date: Thu, 16 Nov 2023 19:01:16 -0800
Subject: [PATCH 5/7] add doc strings for mera

---
 .../models/tensor_network_circuits/mera.py    | 66 ++++++++++++++++---
 1 file changed, 56 insertions(+), 10 deletions(-)

diff --git a/quantum_image_processing/models/tensor_network_circuits/mera.py b/quantum_image_processing/models/tensor_network_circuits/mera.py
index a1662b0..d5ad1e1 100644
--- a/quantum_image_processing/models/tensor_network_circuits/mera.py
+++ b/quantum_image_processing/models/tensor_network_circuits/mera.py
@@ -1,26 +1,42 @@
-import numpy as np
+"""Multiscale Entanglement Renormalization Ansatz (MERA) Tensor Network"""
 from typing import Callable
+import numpy as np
 from qiskit.circuit import QuantumCircuit, QuantumRegister, ParameterVector
 from quantum_image_processing.gates.two_qubit_unitary import TwoQubitUnitary
 
 
-# Can TTN be an ABC for MERA? Good thought!
 class MERA:
     """
-    Implements QCNN structure by Cong et al. (2019), replicating
-    the architecture described by MERA - Multiscale Entanglement
-    Renormalization Ansatz, given by Vidal et al. (2008).
-
-    The decomposition of MERA architecture takes from the paper
-    by Grant et al. (2018).
-
-    NOTE: Remember QCNN and MERA have opposite directions!!
+    Implements a Multiscale Entanglement Renormalization Ansatz
+    (MERA) tensor network structure as given by [2].
+
+    References:
+        [1] E. Grant et al., “Hierarchical quantum classifiers,”
+        npj Quantum Information, vol. 4, no. 1, Dec. 2018,
+        doi: https://doi.org/10.1038/s41534-018-0116-9.
+
+        [2] G. Vidal, “Class of Quantum Many-Body States That
+        Can Be Efficiently Simulated,” Physical Review Letters,
+        vol. 101, no. 11, Sep. 2008,
+        doi: https://doi.org/10.1103/physrevlett.101.110501.
     """
 
     def __init__(self, img_dim):
         self.img_dim = img_dim
 
     def mera_simple(self, complex_structure: bool = True) -> QuantumCircuit:
+        """
+        Builds a MERA circuit with a simple unitary gate
+        parameterization.
+
+        Args:
+            complex_structure (bool): If True, builds the MERA
+            structure with complex unitary gates (e.g. RY, etc.)
+
+        Returns:
+            circuit (QuantumCircuit): Returns the MERA circuit
+            generated with the help of the input arguments.
+        """
         param_vector = ParameterVector(
             "theta",
             int(self.img_dim / 2 * (self.img_dim / 2 + 1)) + 3,
@@ -34,6 +50,18 @@ def mera_simple(self, complex_structure: bool = True) -> QuantumCircuit:
 
     # Check number of params here.
     def mera_general(self, complex_structure: bool = True) -> QuantumCircuit:
+        """
+        Builds a MERA circuit with a general unitary gate
+        parameterization. Refer [1].
+
+        Args:
+            complex_structure (bool): If True, builds the MERA
+            structure with complex unitary gates (e.g. RY, etc.)
+
+        Returns:
+            circuit (QuantumCircuit): Returns the MERA circuit
+            generated with the help of the input arguments.
+        """
         if complex_structure:
             param_vector = ParameterVector("theta", 20 * self.img_dim - 1)
             param_vector_copy = param_vector
@@ -52,6 +80,24 @@ def mera_backbone(
         param_vector_copy: ParameterVector,
         complex_structure: bool = True,
     ) -> QuantumCircuit:
+        """
+        Lays out the backbone structure of a MERA circuit onto
+        which the unitary gates are applied.
+
+        Args:
+            gate_structure (Callable): calls the function with
+            the required unitary gate parameterization structure.
+
+            param_vector_copy (ParameterVector): list of unitary
+            gate parameters to be used in the circuit.
+
+            complex_structure (bool): If True, builds the MERA
+            structure with complex unitary gates (e.g. RY, etc.)
+
+        Returns:
+            circuit (QuantumCircuit): Returns the MERA circuit
+            generated with the help of the input arguments.
+        """
         mera_qr = QuantumRegister(size=self.img_dim)
         mera_circ = QuantumCircuit(mera_qr)
 

From 913ed5671b99f4f5d94d23c8f197855c5a08398a Mon Sep 17 00:00:00 2001
From: SaashaJoshi <saashajoshi08@gmail.com>
Date: Thu, 16 Nov 2023 19:07:10 -0800
Subject: [PATCH 6/7] update reference in ttn

---
 .../models/tensor_network_circuits/ttn.py                     | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/quantum_image_processing/models/tensor_network_circuits/ttn.py b/quantum_image_processing/models/tensor_network_circuits/ttn.py
index f148cdb..cf50a41 100644
--- a/quantum_image_processing/models/tensor_network_circuits/ttn.py
+++ b/quantum_image_processing/models/tensor_network_circuits/ttn.py
@@ -12,7 +12,9 @@ class TTN:
     alternative unitary qubit parameterization.
 
     References:
-        [1] Grant et al. (2018).
+        [1] E. Grant et al., “Hierarchical quantum classifiers,”
+        npj Quantum Information, vol. 4, no. 1, Dec. 2018,
+        doi: https://doi.org/10.1038/s41534-018-0116-9.
     """
 
     def __init__(self, img_dim: int):

From b6de7fba6173b3c17411b949045cb73139d5916a Mon Sep 17 00:00:00 2001
From: SaashaJoshi <saashajoshi08@gmail.com>
Date: Thu, 16 Nov 2023 19:32:11 -0800
Subject: [PATCH 7/7] update doc-strings for gate module

---
 .../gates/two_qubit_unitary.py                | 59 +++++++++++++++++++
 .../gates/unitary_block.py                    | 52 ++++++++++++++++
 2 files changed, 111 insertions(+)

diff --git a/quantum_image_processing/gates/two_qubit_unitary.py b/quantum_image_processing/gates/two_qubit_unitary.py
index 2768b03..8aa5153 100644
--- a/quantum_image_processing/gates/two_qubit_unitary.py
+++ b/quantum_image_processing/gates/two_qubit_unitary.py
@@ -1,3 +1,4 @@
+"""Two-Qubit Unitary Gate class"""
 from __future__ import annotations
 
 import numpy as np
@@ -49,6 +50,26 @@ def auxiliary_parameterization(
     def _real_simple_block(
         circuit: QuantumCircuit, qubits: list, parameter_vector: ParameterVector
     ) -> tuple[QuantumCircuit, ParameterVector]:
+        """
+        Builds a two-qubit unitary gate with simple parameterization,
+        consisting of real two single-unitary gates followed by a CNOT
+        gate as given in the following paper.
+
+        References:
+            [1] E. Grant et al., “Hierarchical quantum classifiers,”
+            npj Quantum Information, vol. 4, no. 1, Dec. 2018,
+            doi: https://doi.org/10.1038/s41534-018-0116-9.
+
+        Args:
+            circuit (QuantumCircuit): Circuit on which two-qubit
+            unitary gate needs to be applied.
+
+            qubits (list): list of qubits on which the gates need to
+            be applied.
+
+            parameter_vector (ParameterVector): list of parameters
+            of the unitary gates.
+        """
         block = circuit
         block.ry(parameter_vector[0], qubits[0])
         block.ry(parameter_vector[1], qubits[1])
@@ -62,6 +83,25 @@ def _real_simple_block(
     def _real_general_block(
         circuit: QuantumCircuit, qubits: list, parameter_vector: ParameterVector
     ) -> tuple[QuantumCircuit, ParameterVector]:
+        """
+        Builds a two-qubit unitary gate with a general parameterization,
+        consisting of real gates only, as given in the following reference paper.
+
+        Reference:
+            [1] F. Vatan and C. Williams, “Optimal quantum circuits for
+            general two-qubit gates,” Physical Review A, vol. 69, no. 3,
+            Mar. 2004, doi: https://doi.org/10.1103/physreva.69.032315.
+
+        Args:
+            circuit (QuantumCircuit): Circuit on which two-qubit
+            unitary gate needs to be applied.
+
+            qubits (list): list of qubits on which the gates need to
+            be applied.
+
+            parameter_vector (ParameterVector): list of parameters
+            of the unitary gates.
+        """
         block = circuit
 
         block.rz(np.pi / 2, qubits[0])
@@ -90,6 +130,25 @@ def _real_general_block(
     def _complex_general_block(
         circuit: QuantumCircuit, qubits: list, parameter_vector: ParameterVector
     ) -> tuple[QuantumCircuit, ParameterVector]:
+        """
+        Builds a two-qubit unitary gate with a general parameterization,
+        consisting of complex gates, as given in the following reference paper.
+
+        Reference:
+            [1] F. Vatan and C. Williams, “Optimal quantum circuits for
+            general two-qubit gates,” Physical Review A, vol. 69, no. 3,
+            Mar. 2004, doi: https://doi.org/10.1103/physreva.69.032315.
+
+        Args:
+            circuit (QuantumCircuit): Circuit on which two-qubit
+            unitary gate needs to be applied.
+
+            qubits (list): list of qubits on which the gates need to
+            be applied.
+
+            parameter_vector (ParameterVector): list of parameters
+            of the unitary gates.
+        """
         block = circuit
         block.rz(parameter_vector[0], qubits[0])
         block.ry(parameter_vector[1], qubits[0])
diff --git a/quantum_image_processing/gates/unitary_block.py b/quantum_image_processing/gates/unitary_block.py
index a8fc26d..b564b05 100644
--- a/quantum_image_processing/gates/unitary_block.py
+++ b/quantum_image_processing/gates/unitary_block.py
@@ -1,3 +1,4 @@
+"""Unitary Gate class"""
 from __future__ import annotations
 from abc import ABC, abstractmethod
 from qiskit.circuit import QuantumCircuit, ParameterVector
@@ -20,6 +21,23 @@ def simple_parameterization(
         parameter_vector: ParameterVector,
         complex_structure: bool = False,
     ):
+        """
+        Used to build a two-qubit unitary gate with real or complex
+        simple parameterization.
+
+        Args:
+            circuit (QuantumCircuit): Circuit on which two-qubit
+            unitary gate needs to be applied.
+
+            qubits (list): list of qubits on which the gates need to
+            be applied.
+
+            parameter_vector (ParameterVector): list of parameters
+            of the unitary gates.
+
+            complex_structure (bool): If True, builds the unitary gate
+            parameterization with complex unitary gates (e.g. RY, etc.)
+        """
         return NotImplementedError
 
     @abstractmethod
@@ -30,6 +48,23 @@ def general_parameterization(
         parameter_vector: ParameterVector,
         complex_structure: bool = True,
     ):
+        """
+        Used to build a two-qubit unitary gate with real or complex
+        general parameterization.
+
+        Args:
+            circuit (QuantumCircuit): Circuit on which two-qubit
+            unitary gate needs to be applied.
+
+            qubits (list): list of qubits on which the gates need to
+            be applied.
+
+            parameter_vector (ParameterVector): list of parameters
+            of the unitary gates.
+
+            complex_structure (bool): If True, builds the unitary gate
+            parameterization with complex unitary gates (e.g. RY, etc.)
+        """
         return NotImplementedError
 
     @abstractmethod
@@ -40,4 +75,21 @@ def auxiliary_parameterization(
         parameter_vector: ParameterVector,
         complex_structure: bool = True,
     ):
+        """
+        Used to build a two-qubit unitary gate parameterization
+        with the help of an auxiliary qubit.
+
+        Args:
+            circuit (QuantumCircuit): Circuit on which two-qubit
+            unitary gate needs to be applied.
+
+            qubits (list): list of qubits on which the gates need to
+            be applied.
+
+            parameter_vector (ParameterVector): list of parameters
+            of the unitary gates.
+
+            complex_structure (bool): If True, builds the unitary gate
+            parameterization with complex unitary gates (e.g. RY, etc.)
+        """
         return NotImplementedError