My Journey to Quantum Computing

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.
A Quantum Gate: H brings a Qubit to a Superposition state. The illustration shows a |0> quantum state transformed to a |+> quantum state.
The illustration shows a typical flow of submitting a job from a classical computer to an IBM quantum computer. The Quantum computer then executes the job and returning quantum measurement results to the classical computer.
A screenshot shows an IBM Quantum Experience on IBM Cloud — accessed on May 18, 2020, and available for Public Access since May 2016. We can develop, run, and inspect the result in a drag-and-drop circuit model or do it within Jupyter Notebook with Phyton by including the Qiskit library.
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.

Agung Trisetyarso has a Ph.D. in Quantum Computing from Keio University, Japan, and currently holds a position as Head of Concentration at Department of Doctor of Computer Science, Bina Nusantara University.


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.

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.

Andi Sama, 2020a, “QTM1x: The Quantum Internet and Quantum Computers: How Will They Change the World?”, Delft University of Technology.
Andi Sama, 2020b, “QTM3x: Architecture, Algorithms, and Protocols of a Quantum Computer and Quantum Internet”, Delft University of Technology.
IBM Quantum Badges, which can be earned only by IBMers.
Imelda Muti, an IBMer, the Chief Operating Officer of IBM Indonesia, recently got her first IBM Quantum Badge, IBM Quantum Conversations: Foundational.
Jing Yi Chan, an IBMer, the IBM Quantum Ambassador in the Asia Pacific region, has achieved the highest status of badges available as an IBM Quantum Distinguished Ambassador.

Published Articles

I also published a few articles through, 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.

Quantum Machine Learning. A generic neural network model trained on the ImageNet dataset is used as the base for Transfer Learning on the Image Classification task (based on ResNet18). This pre-trained model's last layer is modified by quantum means through a quantum machine learning framework: The framework provides convenient access to multiple quantum simulators and real quantum computer backend, including IBM real Quantum Computer on IBM Quantum Computing Experience (IBM Cloud) through an open-source Qiskit API (Application Programming Interface) accessible by Python programming language.
A few examples of prediction during inference using trained hybrid classical-quantum machine learning model applied on the test dataset. Note that predictions are not 100% accurate.

Challenges Ahead

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.

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, combines Artificial Intelligence with Quantum Computer capability for developing a Hybrid Classical-Quantum Deep Learning model.

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.

Quantum Hardware

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.



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