Guide to Quantum Networking, QEC & QML
This blog is geared toward quantum computing buffs who already have some familiarity with the field and want to explore different sub-fields in detail. This blog comprises a list of resources that can help you in getting started in the domains of Quantum Networking & Internet, Quantum Error Correction (QEC), and Quantum Machine Learning (QML).
Section 1: Quantum Internet
(1.1) Prerequisites:
A basic understanding of quantum computing along with knowledge about quantum links, Bell basis measurements, entanglement swapping, and purification is necessary to get started.
- For a sound understanding of these topics along with an overview of quantum networks and quantum internet, follow the lectures by Dr. Michal Hajdusek and Prof. Rod Van Meter. (Source: Q-Leap Edu Quantum Communications)
- Chapter 2 & 3 of the Master’s thesis of Takaki Matsuo is also a very good resource.
(1.2) Going deeper:
To go deeper into the domain one can use the following resources.
- Quantum Networking by Prof. Rod Van Meter is a very good resource.
- To learn about 1G, 2G and 3G quantum networks refer to this paper by Sreraman Muralidharan, Linshu Li, Jungsang Kim, Norbert Lütkenhaus, Mikhail D. Lukin & Liang Jiang
(1.3) Knowing about Devices and Simulators:
To know about protocol design, software architecture and devices involved in quantum networking, refer to the following.
- Chapter 4 & 5 of the Master’s thesis of Takaki Matsuo gives a solid understanding of the design of rule-set based quantum communication protocols and various quantum networking devices.
- Recursive quantum repeater networks - This gives a sound explanation of recursive quantum networking architecture.
- QuISP and NetSquid are two popular quantum network simulators.
Section 2: Quantum Error Correction
(1.1) Prerequisites:
To understand the techniques and codes used in quantum error correction, a basic understanding of quantum computing, quantum circuits, Bell basis measurement and linear algebra is necessary.
- For a good introduction to the various aspects of quantum error correction, you can go through the lecture by Prof. Raymond Laflamme.
- Chapter 5.1 of the Qiskit textbook can also be referred to.
(1.2) Going Deeper:
To get a more mathematical overview of the error-correcting codes one can use the following resources:
- An in-depth overview of the theory and implementation of quantum error correction codes. A good starting point to understand surface code and its workings.
- To get a stronger theoretical background of the prominent techniques, you can follow Chapter 7 of the notes by Prof. John Preskill.
Section 3: Quantum Machine Learning
(1.1) Prerequisites:
For understanding Quantum Machine Learning, basic knowledge about quantum circuits and gates is required as well as some basic understanding of machine learning.
- A good theoretical introduction is given in the edX course by Dr. Peter Wittek.
- For hands-on coding and implementation of basic Quantum Machine Learning algorithms follow optimization and QML tutorials by PennyLane.
(1.2) Going Deeper:
To understand more about Quantum Machine Learning, one can refer to the following resources:
- Quantum Machine Learning: What Quantum Computing Means to Data Mining by Dr. Peter Wittek is a good resource.
- One can refer to the Musty Thoughts blogs to get to know about variational algorithms like VQE and QAOA.
- VQLS, Quantum Image Processing, and Quantum Edge Detection can be learnt from chapters 4.2.1, 4.2.2, and 4.2.3 of the Qiskit Textbook.
- The videos of Qiskit Summer School 2021 are also a good resource for QML.
There are many other wonderful resources out there that the reader might find interesting. So we suggest after gaining some understanding, explore the domains on your own to go even deeper.
Written by — Tanmay Sarkar and Tanya Garg.
