Update QCML.do.txt

Author: mhjensen

doc/src/QuantumComputingMachineLearning/QCML.do.txt

@@ -1,6 +1,6 @@
 TITLE: Quantum Machine Learning
 AUTHOR: Master of Science thesis project
-DATE: today
+DATE: May 4, 2025
 
 
 
@@ -9,25 +9,22 @@ DATE: today
 
 
 _Quantum Computing and Machine Learning_ are two of the most promising
-approaches for studying complex physical systems where several length
-and energy scales are involved.  
+approaches for studying complex systems with many degrees of freedom.
 
 Quantum computing is an emerging area of computer science that
 leverages the principles of quantum mechanics to perform computations
 beyond the capabilities of classical computers. Unlike classical
-computers, which use bits to represent data as 0s or 1s, quantum
+computers, which use bits to represent data as bits $0$ or $1$, quantum
 computers use quantum bits, or qubits. Qubits can exist in multiple
 states simultaneously (superposition) and can be entangled with one
 another, allowing quantum computers to process vast amounts of
 information in parallel.
 
-
 These unique properties enable quantum computers to tackle problems
 that are currently intractable for classical systems, such as complex
 simulations in chemistry and physics, optimization problems, and
 large-scale data analysis.
 
-
 Quantum machine learning (QML) is an interdisciplinary field that
 combines quantum computing with machine learning techniques. The goal
 is to enhance the performance of machine learning algorithms by
@@ -40,7 +37,7 @@ reduction. By exploiting quantum phenomena, QML has the potential to
 accelerate machine learning processes and handle larger datasets more
 effectively.
 
-Quantum computing and QML hold promise for various applications, including:
+Quantum computing and QML hold promise for many different types of  applications, including:
 
 o Drug Discovery: Simulating molecular structures to expedite the development of new medications.
 o Financial Modeling: Optimizing portfolios and detecting fraudulent activities through complex data analysis.
@@ -61,7 +58,7 @@ o Neural networks and quantum neural networks and possibly (if time allows)
 o Classical and quantum Boltzmann machines
 
 The data sets will span both regression and classification problems,
-with an emphasis on simulating time series of relevance for financial
+with an emphasis on simulating time series, in particular  of relevance for financial
 problems. The thesis will be done in close collaboration with Norges
 Bank Invenstment Management, Simula Research laboratory and the
 University of Oslo.
@@ -70,7 +67,7 @@ University of Oslo.
 
 A central model in classical
 supervised learning is the support vector machine (SVM), which is a
-max-margin classifier.  SVMs are widely used for binary classification
+maximal-margin classifier.  SVMs are widely used for binary classification
 and have extensions to regression problems as well.
 They build on statistical learning
 theory and are known for finding decision boundaries with maximal
@@ -98,7 +95,7 @@ Quantum Neural Network (QNN): data are encoded into quantum states, a
 parameterized circuit is applied, and measurements yield outputs.
 For example, it has been shown recently that certain QNNs can exhibit higher
 effective dimension (and thus capacity to generalize) than comparable
-classical networks , suggesting a potential quantum advantage.
+classical networks, suggesting a potential quantum advantage.
 
 
 
@@ -127,15 +124,24 @@ biochemistry, social networks, signal processing and finance
 Quantum Boltzmann Machines (QBMs) are a natural adaption of BMs to the
 quantum computing framework. Instead of an energy function with nodes
 being represented by binary spin values, QBMs define the underlying
-network using a Hermitian operator, normally a parameterized Hamiltonian, see reference [1] below.
+network using a Hermitian operator, normally a parameterized Hamiltonian.
 
 === Specific tasks and milestones  ===
 
-The aim of this thesis is to study the implementation and development of codes for
-several quantum machine learning methods, including quantum support vector machines, quantum neural networks and possibly  Boltzmann machines, and possibly other classical machine learning algorithms, on a quantum computer. The thesis consists of three basic steps:
+The aim of this thesis is to study the implementation and development
+of codes for several quantum machine learning methods, including
+quantum support vector machines, quantum neural networks and possibly
+Boltzmann machines. The results will be compared with those from their
+counterparts from classical machine learning algorithms.  The final
+aim is to study data from finance with both classical and quantum
+Machine Learning algorithms.  In setting up the algorithms, existing
+software libraries like Scikit-Learn, PennyLane, Qiskit and other will
+be used.
+
+The thesis consists of three basic steps:
 
-o Develop a classical machine code for studies of classification and regression problems.
-o Compare the results from the classical Boltzmann machine with other deep learning methods.
+o Develop a classical machine framework for studies of supervised classification and regression problems, with an emphasis on data from finance. The main emphasis rests on deep learning methods (neural networks, Boltzmann machines and recurrent neural networks) and support vector machines.
+o Compare and evaluate the results from the classical machine learning methods and assess their relevance for financial data.
 o Develop an implementation of a quantum Boltzmann machine code to be run on existing quantum computers and classical computers. Compare the performance of the quantum Boltzmann machines with exisiting classical deep learning methods.
 
 The milestones are: