Quantum computing is a disruptive technology with the potential to revolutionize fields such as computing, artificial intelligence, machine learning, data science, and analytics. Quantum devices are our best bet for moving away from CPU and GPU cores to optimize high-performance computing and gain insights from the ever-expanding big data. The domain’s market cap is expected to reach $1,765 million by 2026 from $472 million in 2021, at a CAGR of 30.2% over the forecast period. Big companies like Google and IBM are devoting enormous resources to developing their quantum capabilities.
Analytics India Magazine spoke to Srinjoy Ganguly, Senior Data Scientist at Fractal to understand the scope and future of quantum computing.
AIM: What is the current status of quantum computing?
Srinjoy Ganguly: The field of quantum computing was popularized by Richard Feynman in 1982 when he mentioned it in a heated speech. He said that nature works in non-classical ways and we need to understand quantum mechanics to understand how electrons, protons, neutrons and everything beyond work.
Quantum computing has seen a big boom in the last 10-15 years. Companies around the world are investing in various quantum technologies and manufacturing their quantum hardware. D-Wave is a large pure-play quantum computing company enabling quantum-based processors. Today we are in the nisq (noisy intermediate scale quantum) era, working on 100-qubit quantum systems. While they don’t give perfect results (noisy and erratic reads), you can still work with them. However, we are still a long way from reaching the level of maturity of a fully fault-tolerant quantum computer.
Major consulting firms such as Boston Consulting Group, Gartner and McKinsey have estimated that the nisq era would last for the next five years. Only then can we enter the fault-tolerant quantum computing age and have 1000-qubit quantum systems. After that, it would take another 15 years to reach the million-qubit milestone. However, there is a lot of research and development going on in both the algorithmic and hardware areas, which is a good sign.
AIM: How long before we see mass adoption of quantum computing?
Srinjoy Ganguly: Currently we cannot perform operations on a qubit that last longer than a few microseconds. As a result, the quantum data is lost, making it difficult to use for AI or other general tasks. Researchers are working on QRAM, a computing unit that will make it possible to store quantum states for several hours – a major challenge we are facing today. However, it may take at least a decade to achieve this. Only then can we expect broader acceptance of quantum computing. For a quantum computer to be general purpose, the quantum computer designs must be scalable to hundreds of thousands or millions of logical qubits.
AIM: What are the biggest barriers to innovation in quantum computing?
Srinjoy Ganguly: Skills shortages are not unique to our company or India, they are widespread around the world. For example, some companies have already built their quantum hardware. But running this technology itself requires niche skills in areas like control electronics that not many may possess.
However, many resources have been launched by leading companies like IBM, Google, Xanadu, etc. In addition, various conferences and hackathons are held to bring academia and industry together to collaborate, form teams and solve real-world applications.
I’m part of IBM’s Quantum Educators Network, where educators like me mentor students around the world. In addition, I developed my own Udemy course, which is attended by 30,000 students.
On the hardware side, companies like IBM offer hardware open source packages. This helps independent researchers and smaller companies to simulate hardware in their own computers and opens up a world of possibilities for hardware enthusiasts.
The IISc Bangalore has announced a Masters program in Quantum Computing and other prestigious institutions are expected to follow. We may soon offer MBA courses in quantum computing, similar to the management courses specifically for analytics-related jobs. Students would be trained in leading quantum teams and how this sophisticated technology works.
AIM: Tell us about Fractal’s current work in the field of quantum computing.
Srinjoy Ganguly: We focus roughly on two areas. One of them is quantum chemistry and its application in drug discovery and materials science. The second domain is quantum finance.
In the case of quantum chemistry, we take the molecular simulation point of view. We performed experiments and benchmarking results to simulate the molecular interactions between a subset of HIV and hypothetical antiretroviral molecules. Currently, we are also conducting research and experience on protein folding prediction on the D wave. We are also working on simulating exosite-binding peptides, for example Alzheimer’s, as an enzyme using different variational quantum algorithms using open-source quantum systems and libraries and Qiskit. We also examine and compare results for larger chain neuropeptides in their simulations and experiments.
We conducted a case study of predicting asset prices for quantum finance. We have obtained fruitful results in terms of turnaround time. However, it is still a major challenge as the available quantum processors are still very small and noisy.
AIM: What’s next for Fractal in terms of quantum computing?
Srinjoy Ganguly: Our company’s current goal is to accelerate cutting-edge research in the field of quantum computing and its various applications in the quantum ML and AI space. We mainly focus on research, publishing articles, technical reports and building intellectual property to contribute to the global ecosystem of quantum research by exploring and innovating some novel algorithmic approaches. Furthermore, we want to contribute to building scalable quantum systems, and once we have these novel approaches and scalable quantum systems, they can also be applied to various other applications. We also consider quantum NLP, which has the ability to decipher the meaning of sentences through the technique of compositionality and category theory.
We are also in talks with various academic institutes in India and abroad. The merger or collaboration of industry and science will accelerate the quantum field. We already have a committed collaboration with Amazon Braket for large-scale quantum applications and the future.
We want to use novel algorithms to generate results related to molecular simulation predictions of protein folding and apply new techniques of quantum mechanical learning to quantum chemical problems such as density functional theory. So this is the future we envision.
