A Technologist’s Perspective

As someone working at NeGD under the Ministry of Electronics and Information Technology (MeitY), I’ve had the privilege of engaging with some of the most transformative technologies shaping our digital future. With a technical background and a deep curiosity for emerging tech, I’ve always found myself exploring the frontiers of innovation from blockchain and edge computing to artificial intelligence.

AI, in particular, captivated me early on. Its ability to learn, adapt, and generate insights has already begun reshaping industries. But one moment stood out in my journey: while reading a journal article on the phenomenon of “solidifying light” a discovery involving super solid states I was struck by how quantum mechanics could unlock entirely new dimensions of computation. That experience sparked a deeper interest in quantum technologies and their intersection with AI.

2. 2. The Classical Ceiling

Today’s AI systems are built on classical computers machines that process information using binary bits, limited to 0s and 1s. These systems have enabled remarkable progress, from natural language generation to predictive analytics. Yet, as AI models grow in complexity, the limitations of classical architectures become more apparent. Training large models, simulating nature, or solving optimization problems often demands computational resources that scale inefficiently.

To move beyond these constraints, we need a new computational paradigm one that doesn’t just calculate faster but thinks differently.

3. Quantum Computing: A New Language of Logic

Quantum computers operate on qubits, which differ fundamentally from classical bits. A qubit can exist in multiple states simultaneously (superposition) and can be linked with other qubits through entanglement allowing instantaneous correlations across distances.

This isn’t just a theoretical novelty. It changes how problems are approached. Classical systems evaluate possibilities one at a time. Quantum systems can explore many paths simultaneously, offering exponential speedups for certain tasks.

4. Where Quantum Meets AI

Image source: AI generated when quantum meets AI

The synergy between quantum computing and AI is not just promising it’s potentially transformative:

• Accelerated Learning: Quantum algorithms could streamline the training of AI models by efficiently navigating complex optimization landscapes. This could reduce the time and energy required to build intelligent systems.
• Natural Simulations: Classical computers struggle to simulate quantum systems like molecules or chemical reactions. Quantum computers, speaking the same physical language, can model these systems more accurately. AI, paired with quantum simulation, could revolutionize drug discovery, materials science, and climate modelling.
• Solving Complex Systems: Many real-world challenges like traffic routing, supply chain logistics, and financial modelling involve optimization problems. Quantum AI could uncover solutions that are currently out of reach, improving efficiency across sectors.

5. The Super solid Spark

One of the most fascinating discoveries I encountered was the observation of light behaving as a “super solid” a state that flows like a liquid but retains structure like a solid. Achieved through a Bose-Einstein Condensate at near-zero temperatures, this phenomenon was previously considered theoretical. Bose-Einstein Condensate is a state of matter formed when certain particles are cooled to near absolute zero, causing them to behave as a single quantum entity. The fact that quantum computers are built on similar principles makes them ideal for simulating such exotic states.

solid” something that flows like a liquid yet holds its shape like a solid.

Image Source: betteridia.com https://www.nature.com/articles/s41586-024-07075-y

This realization deepened my appreciation for quantum computing not just as a tool, but as a gateway to understanding nature itself. It’s this kind of breakthrough that underscores the potential of Quantum AI to collaborate with scientists in exploring new phases of matter, designing custom molecules, and pushing the boundaries of discovery.

6. Reality Check: The NISQ Era

We’re currently in the Noisy Intermediate-Scale Quantum (NISQ) era. Today’s quantum processors are sensitive to environmental noise and limited in scale. They’re not ready to replace classical machines, but they’re already being used in hybrid models where quantum processors handle specific sub-tasks within larger AI workflows.

While the potential of quantum computing is immense, it’s important to recognize where we currently stand. We are in what physicist John Preskill termed the Noisy Intermediate-Scale Quantum (NISQ) era a phase where quantum processors contain tens to hundreds of qubits but are still prone to noise, instability, and computational errors. These machines are not yet capable of full-scale fault-tolerant quantum computation, but they are powerful enough to begin exploring real-world applications.

As someone working in the emerging tech space, I see this era as a critical bridge between theory and practical deployment. Researchers are developing hybrid quantum-classical algorithms that allow quantum processors to handle specific sub-tasks such as optimization or simulation while classical systems manage the rest. This collaborative model is already being tested in areas like finance, logistics, and materials science.

The NISQ era is not about replacing classical computers overnight. It’s about learning how to work with quantum systems, understanding their limitations, and gradually building the infrastructure both technical and human that will support the next generation of intelligent machines.

Companies like IBM, Google, and startups worldwide are making steady progress in error correction, qubit stability, and algorithm development. The focus is on building practical systems that can deliver quantum advantage in targeted domains.

7. Challenges and the Road Ahead

Building scalable, fault-tolerant quantum computers is one of the most complex engineering challenges of our time. It requires not only hardware innovation, but also new software paradigms and a workforce trained in quantum principles.

India’s National Quantum Mission is a step in this direction, aiming to build quantum infrastructure and nurture talent. As someone working in the emerging tech space, I see this as a pivotal moment for our country to lead in quantum research and application.

8. Conclusion: A Shift in Computation

The convergence of quantum computing and AI isn’t just about faster machines it’s about rethinking how we simulate, understand, and interact with the world. For technologists like me, it represents a new frontier one that blends physics, computation, and intelligence in ways we’re only beginning to grasp.

As we move beyond binary logic, we open the door to AI systems that can reason more deeply, simulate more accurately, and discover more profoundly. The journey from bits to qubits is not just a technical evolution it’s a philosophical one, redefining what machines can know and what humanity can achieve.

Key References:

Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017). Quantum machine learning. Nature, 549(7671), 195-202.

https://www.nature.com/articles/nature23474

 Cao, Y., Romero, J., Olson, J. P., Degroote, M., Johnson, P. D., Kieferová, M., … & Aspuru-Guzik, A. (2018). Quantum chemistry in the age of quantum computing. Chemical reviews, 119(19), 10856-10915.

https://pubs.acs.org/doi/10.1021/acs.chemrev.8b00803

Biella, A., et al. (2024). Observation of a supersolid of light. Nature, 627, 58-62 (2024).

https://www.nature.com/articles/s41586-024-07075-y

Physics World. “Supersolid light discovery.” (2024, March 5).

https://physicsworld.com/a/supersolid-state-of-matter-created-using-light-and-matter/ (Note: This is an example; you can find summaries on many science news sites).

Preskill, J. (2018). Quantum Computing in the NISQ era and beyond. Quantum, 2, 79.

https://quantum-journal.org/papers/q-2018-08-06-79/.

Government of India, Cabinet Approval Press Release. (2023, April 19). National Quantum Mission.

https://pib.gov.in/PressReleasePage.aspx?PRID=1917744

Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., … & Kais, S. (2022). Noisy intermediate-scale quantum algorithms. Reviews of Modern Physics, 94(1), 015004.

https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.94.015004

(This article has been written by Dimpal Pandey, CB, National e-Governance Division. For any comments or feedback, please write to dimpal.pandey@digitalindia.gov.in and negdcb@digitalindia.gov.in)