Need for Quantum Era!

Today, we live in an era where technology is growing at an exponential rate never before seen. We can say technology that took ten years to evolve will now take three to four years to develop. One of the most significant technologies that can revolutionize the entire advanced technology stack by the middle of 2021 is quantum computers. In a classical computer, we represent data as bits, where each bit can either be a one or a zero. But, in a quantum computer, we describe data as qubits. Each qubit can be both zero and one at the same time due to the wonders of superposition. So, in a classical system of 4 bits, there are 16 possible variations of 0 and 1, and the bits can only represent one variation at a time. In a quantum system, four qubits can be in all 16 of these possible variations at the same time. Take that to just 20 qubits, and your system could simultaneously hold over 1 million values. It is in the real sense, parallel computing at its best and revolutionizes the way that we can solve problems.

Today, classical computers are not able to catch up with the rate at which we are expanding in every space. For example, data generation is growing at a very rapid pace. The image shown below represents the exponential data growth.

In the coming future, classical computers won’t be able to process data and deliver results in a few hours or on the same day. The computation may require days. In such a scenario, quantum computers would be able to process the data within an hour and generate the result. Quantum computers will make it possible to handle the enormous amounts of data we’re producing in this age of big data. We have two options here either to use more GPUs for computation or to think of a new design alternative.

In today’s age, we are all aware of how every industry is applying machine learning and artificial intelligence. Many people are unaware of the hardware that is needed to support the increasing complexity and scalability of AI/ML applications. AI/ML/DL training runs have been increasing at the rate of 2X every 3.4 months, whereas Moore’s law is doubling every 24 months, which is 7x slower. This insight indicates that we need to have a new design of hardware that can keep up with the growing AI/ML/DL related technologies. Quantum computers can be a possible solution. Below is the image representing this information:

Today, engineers are looking for some specialized advanced hardware that can compute the data locally without sending it to the cloud. An example depicting this would be a self-driving car, engineers are looking for a machine learning model that can continuously learn, there shouldn’t be a case when the model doesn’t know what to do or how to respond. The model should continuously learn the input and make a decision based on it. For this, they need supercomputing hardware that can compute the result locally, and the model can learn, and act based on the computation. Classical computers seem to work very hard to maintain Moore’s law. However, we need some new hardware design to make this possible. Quantum computing can help this by providing a rapid computation result without sending any data to the cloud. By this local computation property, self-driving cars can learn the way of driving rather than collecting data and then sending it to the cloud and waiting for the model to learn and then respond. Thus, we can say that Quantum computing can be a crucial enabler.

Every commercial application right now from the automotive industry to the delivery marketplace would benefit from quantum computing. While there is still much to be done in terms of stabilizing and developing quantum systems, every stage of development and progress is getting us closer to the reality of using quantum in real applications. Researchers and the industry is working towards Quantum computing, and soon we can see its working in every aspect of the tech industry.

Image Credits:

[1] https://thequantumdaily.com/wp-content/uploads/2020/01/1567529683_leadspace-background.jpg

[2] John Shen. “Emerging Innovation” Modern Computer Architecture, 4 Oct. 2019, Carnegie Mellon University, Silicon Valley. Slide 3.

[3] John Shen. “Neuromorphic Computing Systems” Modern Computer Architecture, 16 Nov. 2019, Carnegie Mellon University, Silicon Valley. Slide 15.