My Journey to Quantum Computing
Exploring here and there “simultaneously,” like being in the Quantum Superposition
TL;DR: Getting trustable sources from leading Industry Experts and Universities is important. Getting certified is a way to prove that we have reached certain milestones in learning. Clarifying challenging and complex concepts by consulting with different experts would be invaluable in building our understanding gradually.
My interest in Quantum Computing started in May 2016 when IBM launched IBM Quantum Experience, accessible from the IBM Cloud. My curiosity was tickling. This new area of advanced and emerging technology seemed exciting and worth it to explore.
I was quickly exploring the platform for just a few weeks when it was still based on QASM, the Quantum Assembly language, then leave it hanging untouched until restarted again in early 2020. This time, it had improved so much as Qiskit, the higher-level quantum library can be used within the Python programming language. Qiskit started to be available as open-source since its introduction in 2017.
IBM has always been somewhere in my life, both directly and indirectly. When I was just in my 4th year completing my bachelor’s degree, I started from campus recruitment and then spent ten years in IBM Indonesia & Asia Pacific. For the next ten years, I worked for an IBM Business Partner. And now, since 2010, for Sinergi Wahana Gemilang, an IBM Value Added Distributor in Indonesia.
Early exposure to advanced & emerging technologies has been one of my realized passion with IBM and exposure to open sources in the last ten years. Cloud, big data, Internet of Things (IoT), blockchain, and Artificial Intelligence have been the key drivers in the 2010s. Quantum Computing is an exciting new area of emerging technology, promising a leap forward to 2030 and beyond.
Since restarting to learn Quantum Computing in early 2020, I felt that Quantum Computing is hard. My educational background in computer engineering (bachelor's degree) and computer science & business administration (master's degrees) do not seem to provide enough foundation knowledge for learning Quantum Computing.
I think it is safe to say that “the more I think I understand the quantum mechanics three basic concepts: superposition, entanglement and interference,” the more I do not understand the concept.
It’s like being in a superposition state, in which when not being measured, the state can be in the probability of “50% understand” and “50% does not understand” at the same time.
Experts in the Field
It’s good to have someone we can ask for when exploring new things outside of our focus areas. Their expertise and experiences in the field can guide us back to the right path, especially when we feel lost and struggling to understand some of the basic concepts, both theoretically and practically.
Inspired by the fact when I started learning Deep Learning, I started doing the same for Quantum Computing. Deep Learning is a subfield of Machine Learning within Artificial Intelligence in Computer Science.
For Deep Learning, in addition to exploring leading open source frameworks such as Tensorflow, Keras, fast.ai, and Pytorch, I followed a few graduate online courses from leading Universities like Stanford and MIT. Include consulting a friend who has a Ph.D. in this area and following local graduate courses in Indonesia, like Binus University.
Sure, forums and group discussions on the Internet helps. However, getting help and feedback from experts in the field proves to be invaluable to our learning journey. A friend of mine, Suryadiputra Liawatimena, recently introduced me to one of his doctoral supervisors, Agung Trisetyarso.
One of Agung’s doctoral supervisors was Professor Rod Van Meter at Keio University, Japan. His current research focuses on distributed quantum computing and post-Moore's Law computer architecture. In May 2018, Keio University was declared as the IBM Q Hub for Asia, authorized to access current and future IBM Q Systems as ones available.
For Quantum Computing, I started with the basic ones as I do not have an educational background in Classical Physics. To understand specific topics from different perspectives, I explored many online sources related to Quantum Mechanics & Quantum Computing.
I have not always completed all the lecture materials sequentially from the beginning to the end. Occasionally, I have just quickly watched the specific materials (videos) that I need clarification on, then moved to the next. Once in a while, revisiting the same materials a few times helped increase my understanding of the specific sub-topics.
A few notable references for lectures are as follows:
- Fernandez-Combarro Alvarez, Elias, 2020, “A practical introduction to quantum computing: from qubits to quantum machine learning and beyond,” CERN, November-December 2020.
- IBM, 2020, “Qiskit Global Summer School,” July 20–31 2020.
- John Preskill, 2018, “Quantum Computing in the NISQ era and beyond,” Cornell University.
- Qiskit Community Team, 2020, “Learn Quantum Computation using Qiskit,” Qiskit.org, IBM.
- Qiskit YouTube Channel, 2020, “Quantum Information Science Kit,” Qiskit.org, IBM.
- Michael Nielsen, 2011, “Quantum Computing for the Determined.”
- Ryan O’Donnell, 2018, “Quantum Computation and Quantum Information,” Carnegie Mellon Course 15–859BB, Fall 2018.
Certifications & Digital Badges
It is not my original intention to get certifications. However, during the journey throughout 2020, recent research discussions were provided through certification. In my opinion, Delft University in Netherland seemed to offer a good one, so I took their two current courses that are provided through edX and got myself certified in Quantum Internet.
IBM has a Digital Quantum Badge program. Unfortunately, the program is not available externally, other than for IBM Employees.
The following are snapshots of a few IBMers who have acquired the IBM Quantum badges, pushing forward to learn about the future's exciting emerging technology at early stages. Imelda Muti of IBM Indonesia, and Jing Yi Chan of IBM Regional (Asia Pacific).
I also published a few articles through medium.com, sharing what I have been learning to whoever wants to start the journey while also waiting for constructive comments and critiques as I move forward.
Recent ones include exploring a hybrid Classical-Quantum Machine Learning (QML) to train deep learning neural network layers by quantum means. The selected use-case is the classic Image Classification, whether a person is wearing a mask or not.
SWG Insight quarterly editions have included the following articles. The purpose has always been to spread the knowledge and experience for IBM Business Partners and customers and prepare for the arrival of the Quantum Computing era in Indonesia.
- Andi Sama, 2021, “Star Trek and The No-Cloning Theorem.”
- Andi Sama, 2020a, “Meneropong Masa Depan Quantum Computing.”
- Andi Sama, 2020b, “Hello Tomorrow — I am a Hybrid QML.”
- Andi Sama, 2020c, “Quantum Random Number Generator (QRNG).”
- Andi Sama, 2020d, “Quantum Teleportation: Demonstrate Quantum Information Teleportation with Qiskit on IBM Q.”
- Andi Sama, 2020e, “Hello Many Worlds in Quantum Computer — Demonstrate 2-Qubits Entanglement with Qiskit on IBM Q”.
- Andi Sama, 2020f, “The Race in Achieving Quantum Supremacy & Quantum Advantage.”
Exploring and learning Quantum Computing requires a Paradigm Shift. It will eventually come to be implemented in practical applications to solve some of the world’s most challenging problems that are tough to solve within a reasonable time, even with the most sophisticated Classical Supercomputers.
Becoming exposed to and having hands-on practical experience in emerging technologies such as Quantum Computing or related open-source tools would be invaluable in being relevant in the future.
In addition to understanding fundamental quantum computing concepts like Superposition, Entanglement, and Interference, mastering a few leading developing software frameworks and software tools is also essential.
Quantum Software, Frameworks and Libraries
Framework encapsulates many lower-level details so we can focus on the higher-level problems to solve. The framework for Quantum Machine Learning or QML, like the one provided by Xanadu’s Pennylane.ai, combines Artificial Intelligence with Quantum Computer capability for developing a Hybrid Classical-Quantum Deep Learning model.
Qiskit Open Source by IBM consists of rich quantum libraries for building a variety of quantum applications.
Algorithms and Tools
Generic tools to master include programming languages such as Python and C++. Programming languages are indispensable in working with quantum algorithms such as the two famous Grover’s and Shor’s algorithms. Grover’s quantum algorithm is used for searching unsorted data with quadratic speedup. Shor’s quantum algorithm is used for factoring large prime numbers with polynomial speedup.
More inventions of novel quantum algorithms with polynomial or exponential speedup would be something we may see in the coming years.
On the hardware side, scalable quantum computers' construction towards thousands and millions of qubits is still in active research either in Universities or Industries. We may expect to see substantial advancements towards 2030 and beyond. We are now in the “Early Industrial Era for Quantum Computing,” according to Prof. John Preskill, an American theoretical physicist and the Richard P. Feynman Professor of Theoretical Physics at the California Institute of Technology.
In NISQ-era (Noisy Intermediate-Scale Quantum Computer) — IBM, with its 1,000+ logical qubits’ plan, expects that 2023 could be the inflection point for having practical applications of quantum computing. Google, Xanadu, IonQ, and a few others have also been racing towards FTQC (Fault-Tolerant Quantum Computer). The big challenge that a logical qubit needs to be built from many physical qubits to be fault-tolerant. As many as 1,000 physical qubits are needed to build one logical qubit.