When computers first came, we had a very small volume of data. A tiny computer program would be written, as a step-wise procedure, to solve a problem. And computers did not require much computational power to solve the problem. However, in today's world, we have huge volumes of data, and data has become the new oil, and hence, the computational power needed to process the same has increased tremendously.

Other challenges with classical computing

Earlier, the computer program would reside in a particular location and the data in another location. Now, owing to the volume of data, transmitting all that data is not feasible. Hence, both program and data need to be in the same location.

Also, the concept of storing data physically has almost vanished. Today, logical or virtual storage has become popular with domains, such as cloud computing. To process data, it becomes really difficult to partition the data into small data sets and instructions into small task sets. We can consider using a Map-Reduce approach (that breaks a big problem into a set of unique and small problems) to combine the partially processed results. However, despite this approach, the smallest possible data and task set still requires huge computational power, which classical hardware cannot provide, as the smallest possible core has power limitations. Considering all these challenges, there is a need to move towards quantum computing.

What is quantum computing?

Normally, classical computing works on using bits (binary digits of 0 and 1 combinations), and so has a smaller storage capacity. The current electronic circuits have the fundamental active lumped elements, such as diode and transistor, which rely on semiconductors that conduct partially, when doped. However, what we need today is superconductivity. This is where quantum computing can help, as it uses qubits or quantum bits, which have properties of what's called superposition and entanglement.

Quantum computing is a blend of data science and quantum mechanics (the branch of physics that deals with the behaviour of matter and light on a subatomic and atomic level). It makes use of the quantum states of subatomic particles to store information. For us to handle real Al and ML problems, we need quantum computing hardware for processing, as it provides high processing speeds and excellent data security.

KEY APPLICATIONS

Quantum computing can bring transformative applications.

In battery power: Quantum batteries have dielectric reflectors, which reflect optical beams to generate power, and they can be an uninterrupted source of power. Applications would be in electric vehicles, which require a lot of power or frequent charging, in household appliances.

In medicine: In the drug industry, quantum computing can be used to decide the optimum quantity of chemicals to ensure that the drugs are safe. It could also be used to formulate chemicals that are less hazardous.

In security: Quantum computing relies on optical light pulses, which make it difficult for attackers to gain access to information.

In human organ design: Quantum computing can help in organ designing by constructing a digital twin of the organ. Quantum machine learning can help identify the organ's critical and dependent parameters, and help to decide the optimal value for these parameters.

In education: Education applications can use quantum Al to arrive at acceptable solutions within a fraction of seconds, something that would require humans and classical computers trillions of years to solve.

SKILLS REQUIRED

Quantum computing requires optical principles for the design of quantum materials. Quantum technologies are one category of devices to process optical signals using superconducting technology. So, the traditional way of looking at disciplines as watertight containers, such as computer science, electronics, and electrical, cannot help anymore. The quantum workforce will need to have multidisciplinary knowledge of physics, specifically on optics, superconductivity, and nano-electronics.

Software skills: Quantum technology requires a combination of data science and quantum programming skills. Quantum computing based simulation tools, which analyse data sets and use statistical tools to provide inferences, are available. Al-based algorisms leverage machine learning techniques to provide valuable inferences. The processing speed of these algorithms could be enhanced using quantum algorithms, which use several mathematical operations to speed up processing and handle several number crunching operations. The main skills required are mathematics and data handling algorithms.

Hardware skills: The crucial areas for hardware are physics optics and nano-electronics. Nano-electronics leverages the use of nanotechnology in electronic components, and aims at improving their capabilities, while reducing their size and power consumption.