Practical Quantum Computing for Scientists
Quantum computers are here, the next big challenge: A quantum skills shortage. The quantum computing industry will be a $65 billion market by 2030; others anticipate that up to 20% of organizations could be budgeting for quantum computing in 2023, up from 1% in 2018.
Why will you need Quantum computing as a scientist?
Our current understanding of nature is quantum mechanical; therefore, it is natural to argue that science should use the power of quantum computers to understand Nature. This lecture aims to introduce science students with practical, handson, current skills working with commercial quantum computers and quantum simulators in a scientific context. The course will introduce topics such as Quantum Circuit diagrams, Complexity theory, “The Canon” algorithms, Quantum Fourier transform, Hamiltonian Simulation, Common error channels, FaultTolerant Quantum Computation, Quantum Machine Learning, and other paradigms in Quantum computing such as bosonic sampling.
Nature isn't classical, dammit, and if you want to make a simulation of nature, you'd better make it quantum mechanical, and by golly it's a wonderful problem, because it doesn't look so easy.
Course content
Chapter 1: Review & Mathematical Foundations
 Linear Algebra
 Review of the four postulates of quantum mechanics
 Postulate 1: Individual quantum systems
 Postulate 2: Quantum operations
 Postulate 3: Composite quantum systems
 Postulate 4: Measurement
 No cloning theorem
 Quantum entanglement
 Density matrices
 The partial trace operation
 Using partial trace to detect entanglement
 How the postulates of quantum mechanics apply to density operators
Chapter 2: Quantum Circuit Diagrams
 Quantum Circuit Diagrams
 Quantum operators
 Unary
 Binary
 Ternary
 Comparison with classical gates
 The universality of Quantum operators
 The Bloch Sphere
Chapter 3: Complexity Theory; Entropy and Entanglement Distillation
 Complexity Theory
 Time Complexity
 Complexity Classes
 Entropy
 Shannon entropy
 Von Neumann Entropy
 Quantifying entanglement in composite quantum systems
 Entanglement distillation
Chapter 4: The DeutschJosza and BersteinVazirani algorithms
 Functions as oracles
 The problem: Is f constant or balanced?
 The algorithm
 A naive idea
 Deutsch’s algorithm
 The phase kickback trick
 The DeutschJosza algorithm
 The BersteinVazirani algorithm
Chapter 5: Strategies of Input Encoding
 Basis Encoding
 Amplitude Encoding
 TimeEvolution Encoding
 Hamiltonian Encoding
Chapter 6: Simon’s algorithm and applications to cryptography
 Simon’s algorithm
 Birthdays and a naive classical algorithm
 Simon’s algorithm
 Application to cryptography
Chapter 7: The Quantum Fourier Transform
 From Vandermonde matrices to the Discrete Fourier Transform
 The Quantum Fourier Transform (QFT)
 Quantum Phase Estimation (QPE)
 Applications of QPE
 Quantum algorithms for QPE
Chapter 8: Shor’s quantum factoring algorithm
 The integer factorization problem
 The factoring algorithm
 Reducing FACTOR to orderfinding
 Sampling via QPE
 Postprocessing via continued fractions
 Application: Breaking RSA
Chapter 9: Noise
Chapter 10: CV algorithms
Chapter 11: Variational Circuits as Machine Learning Models (time permitting)
 How to Interpret a Quantum Circuit as a Model
 Deterministic Quantum Models
 Probabilistic Quantum Models
 An Example: Variational Quantum Classifier
 An Example: Variational Generator
 Which Functions Do Variational Quantum Models Express?
 Quantum Models as Linear Combinations of Periodic Functions
 An Example: The PauliRotation Encoding
 Training Variational Quantum Models
 Gradients of Quantum Computations
 ParameterShift Rules
 Barren Plateaus
 Generative Training
 Quantum Circuits and Neural Networks
 Emulating Nonlinear Activations
 Variational Circuits as Deep Linear Neural Networks
 TimeEvolution Encoding as an Exponential Activation
Syllabus
Course Title 
PHYS437 Practical Quantum Computing for Scientists 

Lecturers 
Barış Malcıoğlu 
Grading 
Midterm %20, Term project %40, Handson sessions & homework %40 
HandsOn sessions

Attendance to all of the handson sessions, and submitting the assigned handson work is mandatory. Any missed handson session, or assigned handson work will result in N/A grade. Only officially documented cases (such as medical reports) will be considered for exemption.
Midterm Exam
 The midterm exam will involve a theory part and a programming part.
 The theory part should be answered using a Latex/Word processor, converted to pdf
 The programming part must be an ASCII text file containing python code (*.py).
 The files should be uploaded to supplied Turnitin interface. Any incompatible input will be disregarded.
Term projects

The term project is the final exam.

Participants are expected to present a project involving Quantum Computation, Quantum Communication, or Quantum hardware.
 The term project consists of these parts:
 A 1page abstract describing the project
 Presentation (~20 minutes), Q&A session after the talk (~10 minutes)
 (Optional) A final report

The presenter will be graded according to the scientific quality of the presentation

The audience will be graded according to their participation in the Q&A session.

The term projects will be presented in the last 34 weeks

Attendance to the term project presentations is mandatory. The first missed week will result in a reduction of your final grade to %65. The second missed week will result in a reduction of your final grade to %35. If you miss three weeks, you will receive N/A grade.

Only one missed week might be allowed with a valid official excuse.
Textbooks
Theory Content:
 "Quantum Computing for the Quantum Curious" Ciaran Hughes, Joshua Isaacson, Anastasia Perry, Ranbel F. Sun, Jessica Turner https://doi.org/10.1007/9783030616014 (open Access)
 "Quantum Computing: Lecture Notes" Ronald de Wolf arXiv:1907.09415
 "Introduction to Quantum Computation" Sevag Gharibian (Can be obtained from his course page here)
Lab Content:
 Qiskit Textbook
 Xanadu Quantum Codebook
 "Quantum Computing: An Applied Approach" Jack D. Hidary https://doi.org/10.1007/9783030239220
Optional content (time permitting)
 "Lectures on Quantum Tensor Networks" Jacob Biamonte (for a systematic connection between circuit diagrams and CV systems)
 "Machine Learning with Quantum Computers" Maria Schuld, Francesco Petruccione https://doi.org/10.1007/9783030830984
More material
Students are required to open an account in IBM Quantum Cloud