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PHY 605 Quantum Programming

(Spring 2025) Days/Time: MW 09:30-10:50AM; Location: HUMANITIES 2094
3 credits, Letter graded (A, A-, B+, etc.)
This course will meet twice a week for 80 minutes per meeting.
Instructor:  Tzu-Chieh Wei  <tzu-chieh.wei[at]stonybrook[dot]edu>
Office hours: by appointment
TA: TBA <[at]stonybrook[dot]edu>

Course description :
The field of quantum information and computation has evolved to a stage where there are quantum devices that can be programmed and various tasks and algorithms can be tested on these devices. This course introduces various quantum programming frameworks. It aims to provide a more practical approach of learning quantum computing by programming, via softwares developed using Python. Important basic quantum algorithms will be reviewed and learned by programming them and simulating their action. Moreover, an emphasis will be paid to the so-called Variational Quantum Eigensolver that has already been used on many problems, from molecular energies and optimization to financial applications and quantum machine learning. Some illustration of quantum programming will be done on IBM's transmon-type cloud quantum computers. Beyond the circuit-based quantum computers, programming quantum annealers will provide an alternative approach to solve a wide family of optimization problems.

Prerequisite: PHY 568 Quantum Information Science (or other related courses, such as CSE 550 Quantum Computing and Applications) or permission by the instructor

This course is part of the curriculum in our Master's Program in Quantum Information Science and Technology (also here); see alo other courses PHY631 Quantum Information Physical Systems and Materials offered concurrently this semester.

For undergraduates: This course may be taken by upper-level undergraduates, who still need to complete the prerequsite. It needs the permission and the signature of the instructor in order to register for this course; permission form can be downloaded here.

Course objectives/Student learning outcomes:
Students who have completed this course
 • will be able to explain quantum computation of the standard circuit approach and analyze how basic quantum algorithms work
 • will be able to write Python codes to program these quantum algorithms and design ones based on existing algorithms
 • will be able to apply various quantum algorithms for applications, such as optimization problems and scientific problems

Textbooks and resources:
There is no required textbook. But there are recommended textbooks :
Quantum Computation and Quantum Information, M. Nielsen and I. Chuang (Cambridge University Press)
Learn Quantum Computation using Qiskit (free digital textbook) [update: which is not longer maintained by IBM, now at Github]
IBM Quantum Learning (website with various resources)
IBM Quantum Documentation
Further bibliographic resources will be provided as the course progresses.

Python programming: there are many courses, books, tutorials, videos, code examples; see the list in BeginnersGuide/Programmers - Python Wiki. Only basic understanding and programming experience of Python is needed in the beginning. We learn by reading and modifying other people's codes. It is also useful to know how to use iPython/Jupyter notebooks. If you really need to a book, I recommend a compact book "Python (2nd Edition): Learn Python in One Day and Learn It Well," by Jamie Chan, available on Amazon at a very reasonable price (e.g. Kindle version is $3.99). There is also a companion workbook: "Python Workbook: Learn Python in one day and Learn It Well." (Kindle version is $1.99.)
Another recommended free on-lne book is Python Programming and Numerical Methods - A Guide for Engineers and Scientists
Tutorials at: Python Tutorial

Assessment and Grading: Grades: (tentative) Course grades on a 100 point scale are: A (93-100); A- (90-92); B+ (87-89); B (83-86); B- (80-82); C+ (77-79); C (73-76); C- (70-72); F (69 or lower)
Note that the following grading system will be used for graduate students: A (4.0), A- (3.67), B+ (3.33), B (3.00), B- (2.67), C+ (2.33), C (2.00), C-(1.67), F (0.00). Graded/Pass/No Credit (G/P/NC) and grades of D are not approved grades for graduate students.

Homework 50% (need to submit the solutions in Brightspace), In-class presentation or midterm exam 20%, Final project 30%
The homework grading is based on the level of correctness and clarity of logical steps demonstrated in students’ solutions or whether the codes can be run successfully. Final project grading is based on the content, key concepts, organization, coding, analysis of results, and documentation.

Topics to be covered and tentative syllabus
(This is a tentative syllabus. Exam dates and homework due dates may change. The choice of topics may not exactly follow this tentative syllabus.)

(week 1)  Overview of this course and review of linear algebra, basics of quantum mechanics, quantum bits and quantum gates; overview of Python and the use of Jupyter Notebook
(week 2)  More quantum gates and circuit model of quantum computation; quantum teleportation, quantum algorithms including Deutsch, Deutsch-Josza, Simons, Vazirani-Berstein. IBM Quantum Composer.
(week 3) IBM Qiskit framework and programming algorithms of week 2
(week 4) Grover's quantum search algorithm and its programming and The variational quantum eigensolver (VQE) and its programming.
(week 5) Machine learning and Quantum Machine Learning; introducing PyTorch and PennyLane for programming quantum machine learning
(week 6) Quantum Fourier Transform, quantum phase estimation, Shor’s factoring algorithm and quantum linear system (such as the HHL algorithm), and their programming
(week 7) Advanced topic: quantum computing for molecular energies in Quantum Chemistry
(week 8) Overview of other software frameworks, such as D-Wave’s quantum annealers and programming on D-Wave's SDK Ocean and/or Amazon Braket
(week 9) Qutip: simulating quantum dynamics and the master's equation. Cavity and atoms.
(week 10) Qutip: case studies
(week 11) Quantum communication: Quantum key distribution, Ekert's protocol, quantum teleportation, entanglement swapping
(week 12) Overview on NetSquid and/or SeQUeNce for simulating quantum internet
(week 13) Establishing long range entanglement; realistic case study
(week 14)  (Student project presentation)
(week 15)  (Student project presentation)

There was a big jump of IBM's Qiskit from version 0.46 to version 1.x in 2024. The current version as of mid Jan 2025 is v1.3. Many earlier codes (including last year's) need to be modified to suit Qiskit v1.3. Still basic features such as circuits and gates remain largely stable. There has also been advocates from IBM on the "utility era", where they encourage running circuits and tasks that involve a large number of qubits, e.g., over 100, and they retired all small machines. Existing devices all have num_qubits > 100. This year we will mostly focus on Qiskit and we will also use PennyLane.

Students will need to install Python and Qiskit on their own laptops (unfortuantely, IBM retired their web-based Quantum Lab in May 2024). We will go over installation and students should bring their laptops to class and we will have time in class to play with Jupyter notebooks.

[update/actual progress]

(week 1) [1/27,1/29] 1/27: We gave an overview of this course; students intalled Python and Qiskit. We ran an example Overview notebook for writing qiskit codes. [see IBM researcher Derek Wang's videos on (1) introduction to qiskit/coding, (2) installing qiskit and (3) hello world (documentation link)]
1/29: We gave an overview of Python Programming (we learned basics of Python) and reviewed the Deutsch algorithm (for the latter, please refer back to PHY568 QIS), and used IBM Quantum Composer to implement some of the four possible instances.

next: we will explain the notebook for the Deutsch algorithm, and review gates and circuits (with notebooks).

(week 2) [2/3, 2/5] 2/3: We first went over the Deutsch algorithm notebook, then we went over tutorials on gates, circuits and visualization tools in qiskit. 2/7: We went into the Documentation on Qiskit Circuit Library, discussed general multi-qubit control gate decomposition (to elementary one- and two-qubit gates), returned to implementing two other basic quantum algorithms: Bernstein-Varzirani and Simon's. 

next: we plan to review Grover's search algorithm and illustrate its Qiskit implementation; then we will move on to VQE.

(week 3) [2/10, 2/12] 2/10: We discussed in detail the implementation of Simon's algorithm, and reviewed Grover's algorithm and went over its Qiskit implementation. 2/12: We discussed the framework of VQE and went over two notebooks on implementation. (We used qiskit_algorithm package in one notebook and the Session method in the other.)

(week 4) [2/17, 2/19] 2/17: We discussed Operator in Qiskit and then circuit transpilation.

(week 5) [2/24, 2/26]

(week 6) [3/3, 3/5]

(week 7) [3/10, 3/12]

(week spring recess) [3/17, 3/19] Spring Recess: No classes

(week 8) [3/24, 3/26]

(week 9) [3/31, 4/2]

(week 10) [4/7, 4/9]

(week 11) [4/14, 4/16]

(week 12) [4/21, 4/23]

(week 13) [4/28, 4/30]

(week 14) [5/5, 5/7]

(final exam period)

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