Qubo fault detection

.gitignore

@@ -1,4 +1,3 @@
-
 .vscode/
 node_modules/
 

README.md

@@ -1,11 +1,7 @@
-[<img src="https://qbraid-static.s3.amazonaws.com/logos/Launch_on_qBraid_white.png" width="150">](https://account.qbraid.com?gitHubUrl=https://github.com/qBraid/NYUAD-2023.git)
-
-# NYUAD Hackathon for Social Good in the Arab World: Focusing on Quantum Computing (QC)
+# NYUAD Hackathon for Social Good in the Arab World: Focusing on Quantum Computing (QC) and UN Sustainable Development Goals (SDGs).
 
 https://nyuad.nyu.edu/en/events/2022/march/nyuad-hackathon-event.html
 
-March 30 - April 1, 2022
-
 ## Technical challenge
 
 _Create a program that applies one or more quantum algorithms to a social good
@@ -18,48 +14,29 @@ problem of your choice._
 - Variational Quantum Eigensolver (VQE)
 - Quantum Approximate Optimization Algorithm (QAOA)
 
-**Social good topic examples**:
-
-- Healthcare
-- Science (e.g. AI, cryptography, biochemistry)
-- Environment (climate)
-- Education & Literacy
-- Food Securities
-- Crisis & Public Safety
-- Financial Modeling
-- Gaming
-
-**Implementation requirements**:
-
-- Must utilize quantum hardware available through preferably IBM. However, we
-  set also have AWS devices available as well.
-- You are also free to use any technology which allows you to solve the
-  challenge.
+### Qubo Fault Detection
+The program is used for fault detection and diagnosis of graph based networks. The implementation is based on qubo explained in [paper](https://arxiv.org/pdf/1406.7601.pdf).
 
-**Bonus requirements**:
+**Requirements:**
+- Install pyqubo using: pip install pyqubo
 
-- Incorporate noisy simulation through IBM
-- Incorporate a hybrid quantum-classical task through IBM
+**Input to the program:**
+- Sensor readings
+- Paths from source to sensor
 
-# qBraid Tutorials
-
-Here, we provide useful tutorials on how to use qBraid-Lab, along with tutorials
-on quantum computing, using IBM or Amazon Braket. The ladder were provided by
-the [qiskit-tutorials](https://github.com/qiskit/qiskit-tutorials) and the
-[amazon-braket-examples](https://github.com/aws/amazon-braket-examples) github
-repositories repsectively.
+---
 
-The repository is structured as follows:
+<p align="center">
+  <img width="460" height="460" src="https://github.com/qcswat/qatrah/blob/main/WDN%20animation.gif">
+</p>
 
-- [Setting up Qiskit environment](qbraid_qiskit_setup/accessing_ibm_hardware.ipynb)
+## Replacing Classical Pressure Sensors with Optimized Quantum Sensors
 
----
+Compared to classical pressure sensors, quantum sensors are not invasive. They are also tolerant to the changes in the environment around it while also being more accurate. This improves the ability to detect pipe leakage.
 
-## <a name="qbraid">Setting up Qiskit environment in qBraid</a>
+## Leak Detection and Localization
 
-- [**Install Qiskit in qBraid-Lab**](qbraid_qiskit_setup/accessing_ibm_hardware.ipynb)
+### Using Quantum Machine Learning
+Existing classical literature, suggests the use of machine learning to predict leakage and localise it to a particular pipe using the data from pressure sensors in the WDN at any given point of time. We attempt to solve the same using a quantum machine learning based model. 
 
-- [**Enable Qiskit QPU access through qBraid-CLI**](qbraid_qiskit_setup/accessing_ibm_hardware.ipynb)
-- [**Example environent setup on qBraid Youtube video**](https://www.youtube.com/watch?v=LyavbzSkvRo) (Please use the code EHNU6626)
-
----
+Specifically, we collect the pressure data from the optimally-placed sensors in a water distribution network to predict leakage in the WDN using a quantum neural network. It is implemented in the Pennylane framework using Jax. The data is fed into the model using Angle encoding. The model is composed of a parametrised quantum circuit with RY, RZ and CNOT gates which are trained over a total of 500 epochs. We use a train to test-set ratio of 4:1 and optimise the model using Rectified adam over the binary cross-entropy loss. At the end we obtain a test accuracy of 87.02% over the dataset of size 650.

docs/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/_includes/qml_debugging.rst

@@ -0,0 +1,7 @@
+Debugging module
+================
+
+.. automodule:: qml_debugging
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/conf.py

@@ -0,0 +1,68 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = "QatraH"
+copyright = "2023, QatraH Dev Eng"
+author = "QatraH Dev Eng"
+release = "0.0.1"
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = []
+
+templates_path = ["_templates"]
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+language = "pyhton, jupyter, quantum computing, qiskit, pennylane, jax, qbraid"
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = "alabaster"
+html_static_path = []
+
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath(".."))
+
+# The full version, including alpha/beta/rc tags
+release = "0.0.1"
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+
+autosummary_generate = True
+extensions = ["sphinx.ext.autodoc", "sphinx.ext.napoleon"]
+
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = "sphinx_material"
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = []
+html_show_sourcelink = False

docs/index.rst

@@ -0,0 +1,23 @@
+.. QatraH documentation master file, created by
+   sphinx-quickstart on Sun Apr 30 14:32:59 2023.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to QatraH's documentation!
+==================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   _includes/qml_debugging
+
+
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`

docs/make.bat

@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

fault_detection.py

@@ -0,0 +1,103 @@
+from pyqubo import Binary
+import neal
+
+
+
+
+def read_input():
+    sensor_file = open("sensor_readings.txt", "r")
+    sensor_readings = []
+    sensor_readings.append(1)
+    sensor_readings.extend([int(x) for x in next(sensor_file).split(',')])
+
+    paths_file = open("paths.txt", "r")
+    number_of_nodes = int(next(paths_file))
+    paths = [[]]
+    for line in paths_file:
+        paths.append([int(x) for x in line.split(',')])
+
+    return sensor_readings, paths, number_of_nodes
+
+
+def create_optimization_function(sensor_readings, paths, number_of_nodes):
+    H = 0
+    number_of_sensors = len(sensor_readings) - 1
+    lambdaPath = 1
+    # Add H consistent
+    for y in range(1, number_of_sensors+1):
+        l = sensor_readings[y]
+        # for y in range(1, number_of_sensors+1):
+        if l == 1:
+            H+= (lambdaPath * Binary('y'+str(y)) * len(paths[y]))
+            for x in paths[y]:
+                H+= (-lambdaPath * Binary('y'+str(y)) * Binary('x'+str(x)))
+        else:
+            H+= (lambdaPath*Binary('y'+str(y))*((1-len(paths[y]))**2))
+            for x in paths[y]:
+                H+= (lambdaPath*Binary('y'+str(y))*(Binary('x'+str(x))**2) + 2*lambdaPath*Binary('y'+str(y))*(1-len(paths[y]))*Binary('x'+str(x)))
+                for j in paths[y]:
+                    if x<j:
+                        H+= (2*lambdaPath*Binary('y'+str(y))*Binary('x'+str(x))*Binary('x'+str(j)))
+
+    # Add H numFaults
+    for y in range(1, number_of_sensors+1):
+        H+= (1-Binary('y'+str(y)))
+
+    for x in range(1, number_of_nodes+1):
+        H+= (1-Binary('x'+str(x)))
+
+    return H
+
+
+def print_variables(best_sample):
+    Nodes = {}
+    Sensors = {}
+    for key in best_sample:
+        if "*" in key:
+            if best_sample[key] == 0:
+                pass
+            key1, key2 = [x for x in key.split(" * ")]
+            if "x" in key1:
+                Nodes[key1] = best_sample[key]
+            else:
+                Sensors[key1] = best_sample[key]
+            if "x" in key2:
+                Nodes[key2] = best_sample[key]
+            else:
+                Sensors[key2] = best_sample[key]
+        else:
+            if "x" in key:
+                Nodes[key] = best_sample[key]
+            else:
+                Sensors[key] = best_sample[key]
+
+    print("Node Values:")
+    for key in Nodes:
+        print(key, ": ", Nodes[key])
+
+    print("Sensor Values:")
+    for key in Sensors:
+        print(key, ": ", Sensors[key])
+
+
+if __name__ == '__main__':
+    # Read the input
+    sensor_readings, paths, number_of_nodes = read_input()
+
+    # Create optimization function
+    H = create_optimization_function(sensor_readings, paths, number_of_nodes)
+
+    # Create model
+    model = H.compile()
+    bqm = model.to_bqm()
+
+    # Get the best samples
+    sa = neal.SimulatedAnnealingSampler()
+    sampleset = sa.sample(bqm, num_reads=10)
+    decoded_samples = model.decode_sampleset(sampleset)
+    best_sample = min(decoded_samples, key=lambda x: x.energy)
+
+    # Print the binary variables values
+    # print(best_sample.sample)
+    print_variables(best_sample.sample)
+