The Race in Achieving Quantum Supremacy & Quantum Advantage

#QuantumComputer #QuantumComputing #ClassicalComputer #ArtificialIntelligence #AI #MachineLearning #ML #DeepLearning #DL #QuantumMachineLearning #IBMQ #IBMQISkit #IBMPOWER #IBMAC922 #IBMIC922 #IBMWMLA #IBMPowerAIVision #H2ODriverlessAI #IBMCloud #IBMWatsonStudio #IBMWatsonNeuralNetworkModeler #IBMCloudPakForData

For the introduction on Quantum Computing (local language: Indonesian), please refer to the previous article, posted by Andi Sama on June 27, 2019 “Meneropong Masa Depan: Quantum Computing”.

Artificial Intelligence, Machine Learning & Deep Learning — Today

Illustration-1: The evolution and advancements of Artificial Intelligence since the 1950s (Karen Hao, 2019 and Divyansh Dwivedi, 2018).
Illustration-2a: Advancements in Deep Learning, mostly in computer vision. The two photographs on the top-right & bottom-left are not the actual persons, they are generated by AI. The middle image is Tesla’s self-driving car. Bottom-middle is the robot that can pick-up strawberry automatically after determining it’s level of ripeness through camera processed by AI. The top-left picture is face-recognition associated by name while the top-middle is a competition between humans and machines in a non-moderated debate, the advancement in natural language processing. Lastly, the bottom-right shows a newly announced (April 2019) IoT device equipped with 128 GPU’s CUDA (Compute Unified Device Architecture) cores as one of the smallest edge-engine to run trained AI-model.
Illustration-2b: The top shows samples of image classification & object detection, considered as a matured approach using Deep Learning in computer vision. The bottom shows action detection — quite a practical approach also in doing Deep Learning for computer vision.
Illustration-3a: Generated 1000-random data.
Illustration-3b: The result of the k-means algorithm with k set to 2, to do clustering from the 1000-generated random data.
Illustration-4: The Code in Matlab to generate illustration-3a and 3b: generate random data, then use the k-nn algorithm to do clustering.

Quantum Computing

Illustration-5: An IBM Quantum Experience on IBM Cloud, available for Public Access since early 2016.
Illustration-6: The current snapshots of the status of IBM Quantum Computer in IBM New York (5-qubits, online) — left, and in IBM Melbourne (14-qubits, calibrating) — right, as of November 26, 2019.


A superposition is a weighted sum or difference of two or more states; for example, the state of the air when two or more musical tones are sounding at once. Ordinary, or “classical,” superpositions commonly occur in everyday phenomena involving waves.

- IBM Quantum Experience, January 2019 -


Entanglement is a property of most quantum superpositions and does not occur in classical superpositions. In an entangled state, the whole system is in a definite state, even though the parts are not. Observing one of two entangled particles makes it behave randomly but tells the observer exactly how the other particle would act if a similar observation were made on it.

- IBM Quantum Experience, January 2019 -

Quantum Volume

Quantum Volume (QV) is a hardware-agnostic metric that IBM defined to measure the performance of a real quantum computer. QV takes into account the number of qubits, connectivity, and gate and measurement errors.

- IBM Research blog, January 2020 -

“Big O” Notation

Wikipedia, 2019b: “Big O notation is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity.” In this article, we see “Big O” as the indicator the complexity of an algorithm.

Quantum Machine Learning

Table-1: Comparison of Big O in Principal Component Analysis, Fast Fourier Transform, and Eigenvalues & Eigenvectors both in Classical Computer and Quantum Computer (Seth Lloyd, 2016).
Illustration-7: Visualization of Comparison of Big O in Principal Component Analysis, Fast Fourier Transform, and Eigenvalues & Eigenvectors both in Classical Computer and Quantum Computer (Seth Lloyd, 2016). Please refer to table-1 for Big O notation. Looking at the three illustrations above, it is obvious that the improvement by using Quantum Computer is significant for all three algorithms overusing classical computers, although at a different scale. (a) and (b) are basically within a similar scale while in © it is seen that with high dimensions, Quantum Computer provides much better improvement over a classical computer.

IBM Solutions for Data Science, Machine Learning & Quantum Machine Learning

WMLA (or WML CE for the Community Edition) is available as On-Premise solution for modeling & inference, and comes with very scalable configuration equipped with multiple NVidia high-end Tesla V100 GPUs. In the latest release in late 2019, Inferencing at Edge is available with NVidia Jetson TX2 and Jetson Nano. Previously, Xilink Alveo U200 Field Programmable Gate Array (FPGA) has been supported. WMLA & Power AI Vision is good for general users & non-experienced developers.

IBM IC922 is also available starting in Q1 2020, as the IBM offering for Inferencing Platform (runtime) for Machine Learning trained-model.

There are set of tools provided for Data pre-processing for preparing data in IBM WDP (such as Data Manipulation/Cleansing, Data Augmentations) before being processed by Machine Learning algorithms, as well as Data Catalog for managing data sets used to train the data using Machine Learning algorithms.

[SW] IBM Cloud Pak for Data (available in On-Premises as well as in Cloud) is a container-ready solution that enables users to put lots of data to work quickly and efficiently to use the data to generate meaningful insights that can help users avoid problems and reach the goals, while maintaining the data privately. The best fit for experienced Data Scientists/Analysts & experienced developers.



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