Plans for the week of April 6-10
Dear all, welcome back after the break.? We hope you had a great break and have recharged your batteries.?
The plans for this week are (with tentative plans for the rest of the semester as well):
Plans for the week of April 6-10, 2026
Discussions of the QPE algorithm and implementations
The HHL algorithm and links with the QPE algorithm
Material this week:
a. PDF slides at?https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/pub/week11/pdf/week11slides.pdf
b. Jupyter-notebooks at?https://github.com/CompPhysics/QuantumComputingMachineLearning/tree/gh-pages/doc/pub/week11/ipynb
Project two suggestions, see?https://github.com/CompPhysics/QuantumComputingMachineLearning/tree/gh-pages/doc/Projects/2026/Project2. Note that these suggestions will be revised till April 7
What is the HHL algorithm
The HHL algorithm—named after Aram Harrow, Avinatan Hassidim, and Seth Lloyd—is one of the central quantum algorithms for solving linear systems of equations.
The core idea is to solve a linear system
What is the QAOA algorithm?
The next week, our plan is to discuss the QAOA algorithm.?QAOA?stands for the Quantum Approximate Optimization Algorithm.
Each part of the name reflects something essential about the method:
Quantum: it runs on a quantum computer, using qubits and quantum gates.
Approximate: it typically finds near-optimal solutions rather than exact ones.
Optimization: it is designed to solve optimization problems (like MaxCut, scheduling, portfolio selection).
Algorithm: it is a well-defined hybrid quantum–classical procedure.
QAOA is a variational quantum algorithm that uses a parameterized quantum circuit to search for good solutions to hard optimization problems.
Codes and plans for the coming two weeks (April 13-24)
This week (April 8), with respect to codes, we focus on the?implementation of the QPE algorithm and the QFT algorithm. For the HHL algorithm our main focus, as discussed in the slides at?https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/pub/week11/pdf/week11slides.pdf, will be on the theoretical aspects of the algorithm.
Next week we will discuss codes for the HHL algorithm applied to linear algebra problems and the solution of differential equations like the Poisson equation (week of April 13-17). We will also discuss the implementation and the theory behind the QAOA algorithm next week. The subsequent week we will link the HHL and QAOA algorithms to quantum machine learning and start with discussions quantum machine learning, with an emphasis on quantum support vector machines and quantum neural networks. These topics will be the main topics for the rest of the semester.
Suggestions for project 2 will be presented April 8.
Plans for the rest of the semester, April
April 6-10, 2026
Finalizing the QPE discussions, with codes and formalism
The HHL algorithm for solving linear algebra problems
April 13-17, 2026
Summing up the HHL discussions, codes and algorithms
The QAOA algorithm
April 20-24, 2026 Quantum Machine Learning
Linking the HHL and the QAOA algorithm with quantum machine learning (QML)
Basics of QML
Quantum Support vector machines (QSVM) and classical SVMs
April 27-May 1, 2026 Quantum machine learning
QSVM, algorithms and codes
Quantum neural networks (QNN)
Plans for the rest of the semester, May
May 4-8, 2026 Quantum Machine Learning
Quantum neural networks, codes and implementations
May 11-15, 2026
Quantum neural networks and quantum Boltzmann machines
May 18-22, 2026
Summary of course and discussion of and work on project 2
Best wishes to you all,
Carl Fredrik and Morten