“Quantum AI is not about smarter machines; it’s about deeper computation.”
The intersection of quantum computing and artificial intelligence (AI) promises a technological revolution that could redefine industries, research, and innovation. While AI has already transformed sectors from healthcare to finance, integrating quantum computing into AI architectures opens unprecedented possibilities in computational efficiency, problem-solving, and energy optimization. This emerging domain, often referred to as Quantum AI, is still in its nascent stage but carries the potential to solve problems classical systems struggle with.
Mr. Aditya Yadav, founder of Automatski and a pioneer in quantum AI, has been at the forefront of this field. Drawing on decades of experience in neural networks, robotics, and quantum computing, his insights illuminate both the promise and challenges of this transformative technology.
The Journey to Quantum AI
Quantum AI is not just a technical concept; it is the result of a decades-long evolution of computing and AI research. Mr. Yadav’s journey offers a compelling lens into this evolution.
Born into a family with deep roots in computer science, Mr. Yadav developed an early fascination with technology, setting the stage for a future in AI and quantum computing. By the mid-1990s, the convergence of neural networks and quantum computing began to capture attention globally. Mr. Yadav explored quantum computing by learning from early systems such as the 1996 AT&T quantum computing setup in the U.S. By 1997, he demonstrated a system capable of performing quantum calculations, laying the foundation for his future ventures.
“Quantum computers are computers that can solve anything. ” Mr. Yadav explained, emphasizing their potential far beyond specialized optimization tasks.
In parallel, Mr. Yadav founded Automatski, a startup building quantum computers and quantum annealers, specialized machines designed to solve complex optimization algorithms — a precursor to full-scale quantum AI.
Understanding Hybrid Quantum-Classical AI
The global quantum AI market is entering a phase of rapid expansion, reflecting both technological maturity and accelerated enterprise adoption.
Valued at approximately USD $473.54 million in 2025, the market is projected to surge to nearly USD $6.96 billion by 2034, growing at a striking compound annual growth rate (CAGR) of 34.8% (source). This exponential growth is being fueled by the rising need for secure, high-performance AI systems, the increasing investment in quantum computing infrastructure, and the emergence of advanced quantum algorithms.
AI systems, particularly neural networks, involve layers of interconnected computations. Traditionally, these computations rely on GPUs, which excel at performing large matrix operations in parallel. This capability fueled the AI revolution, allowing for rapid development of deep learning models.
However, classical neural networks face limitations. They often struggle to capture complex, nonlinear relationships in data — relationships that are abundant in scientific, engineering, and real-world contexts. For example, earlier, we worked extensively on Google Reinforcement Learning, which is a learning paradigm where systems learn from feedback or rewards. Reinforcement learning was responsible for systems like Google’s quantum computers, which defeated professional players in games like chess and Go. That was around four or five years ago and marked a major milestone for reinforcement learning.
Enter Quantum Neural Networks (QNNs). Built on the principles of quantum mechanics, QNNs leverage qubits, which are inherently interconnected through quantum entanglement. This allows each qubit to interact with every other qubit in a network layer, which are made of different blocks, enabling a richer understanding of complex data relationships.
This evolution also encompasses the idea of large language models – from early neural networks to LLMs — and showcases how AI systems have become deeper and more data-driven. Now, when you introduce quantum computing into this mix, it changes how these networks can be trained and executed. The “quantum” component adds exponentially higher computational capacity and enables the processing of far more complex relationships within data than traditional GPUs or CPUs.
Classical Neural Network vs. Quantum Neural Network
| Feature | Classical Neural Network | Quantum Neural Network |
|---|---|---|
| Basic Unit | Neuron | Qubit |
| Relationship Modeling | Limited to simple correlations | Can model complex, interconnected relationships |
| Computation | Layered matrix multiplication on GPUs | Quantum circuits with entangled qubits |
| Scalability | Requires multiple GPUs for large networks | Requires fewer qubits for certain tasks due to quantum parallelism |
In practice, hybrid quantum-classical AI systems combine classical layers with smaller quantum layers, known as parameterized quantum circuits. Inputs are processed through classical layers, then through quantum layers where each qubit’s parameters (angles in the circuit) are adjusted iteratively to optimize outcomes. The process involves a train and execute cycle:
- Initialize quantum parameters randomly.
- Execute computations on quantum simulators or hybrid frameworks.
- Aggregate outputs and propagate results to subsequent layers.
- Adjust parameters based on results and iterate.
In simpler terms, in the case of classical neural networks, every neuron’s parameter multiplies with parameters in a structured, linear way. But in quantum systems, every qubit can interact with every other qubit directly within the quantum circuit. This interconnectedness enables the network to capture much more complex relationships.
“The word hybrid means that there are classical layers and the layers which are very big, are replaced by smaller Quantum layers,” Mr. Yadav elaborated.
Quantum Simulators and the Current State of Quantum AI Computing
Fully functional, large-scale quantum AI computers remain rare. Instead, researchers often rely on quantum simulators, capable of handling 30-35 qubits on classical servers from providers like AMD or Intel. Each quantum layer in a hybrid system can include blocks of these qubits, which collectively form a quantum circuit.
Despite these limitations, hybrid systems offer advantages in computational efficiency, energy consumption, and ability to model complex relationships. For instance, while classical AI models may require thousands of GPUs, quantum layers can achieve similar outputs with fewer resources:
| Metric | Classical AI | Hybrid Quantum AI |
|---|---|---|
| Energy Consumption | Hundreds of kW | ~30–70 kW per quantum system |
| Computational Efficiency | GPU-limited | Quantum parallelism enables faster convergence |
| Application Scope | Simple-to-moderate complexity | Complex optimization algorithms, molecular simulation, and quantum CFD |
Of course, we’re talking about actual quantum computers, not simulators. But when such systems become stable, the computational and energy efficiencies they provide will fundamentally redefine AI computation.
“Energy efficiency, computational efficiency, and the ability to solve complex problems are key advantages of quantum systems,” noted Mr. Yadav.
Challenges in Achieving Scalable Quantum Neural Networks
Scaling QNNs from experimental setups to functional, production-ready systems remains a formidable challenge today and is in its primal state. Key barriers to efficient and scalable quantum systems include:
Global Qubit Availability
The biggest bottleneck is the absence of large-scale qubit systems globally. Building a quantum computer with millions of reliable qubits is still a monumental challenge, limiting practical implementation and ROI for quantum AI applications. As of 2023, IBM’s Condor processor featured 1,121 qubits while its public fleet generally consists of a mere 127 qubits.
“The biggest bottleneck is the absence of a large scale of qubits, globally. It’s like trying to study the moon without having a moon,”
Coherence
With today’s computing power, cubits maintain their quantum state for mere microseconds, which is not enough for large computation. It is only with longer coherence / duration can organizations execute multi-step computations. Caltech built an array of 6,100 neutral-atom qubits in ~12,000 sites, with record coherence time of approximately 12.6 seconds.
Accuracy
Currently, quantum gate fidelity is at ~99.9%. While it may seem impressive, a 0.01% of infidelity compounded across thousands of gates and qubits, can manipulate and distort results to a great extent. In fact, a staggering 14-decimal accuracy is tallied and maintained to really call the system accurate.
Noise
Quantum elements are like electrons and protons, microscopic and yet powerful. Minor disturbances like sound waves, radiation, or temperature changes, even movement of hand and talking can disrupt quantum computations. Quantum AI research facilities around the world have built specialized vacuum chambers with stringent environmental controls to enable quantum systems to operate efficiently.
Cost and Operational Complexity in Quantum AI
Quantum computers are extremely expensive, often costing hundreds of millions of dollars, and require highly controlled environments to operate, including vacuum chambers, precise temperature regulation, etc. Operating them demands advanced expertise spanning physics, engineering, and computer science.
| Parameter | Classical Computing | Quantum Computing |
|---|---|---|
| Stability | Highly Stable | Extremely Sensitive |
| Processing Units | Transistors | Qubits |
| Accuracy | Deterministic | Probabilistic |
| Energy Efficiency | Moderate | High (once stable) |
| Failure Sources | Hardware Error | Quantum Noise, Decoherence |
For practical adoption, these systems must become both more affordable and easier to operate, as current cost and operational challenges significantly limit deployment and scalability.
Global and Geopolitical Perspectives in Quantum AI Research
Quantum computing is not just a technical challenge; it is also strategically important. Countries like the U.S. have invested billions over decades, while China is aggressively pursuing quantum supremacy with $14 billion investments.
India’s National Quantum Mission (NQM), launched in 2023 with a 6,400 crore budget, aims to build self-reliance in quantum technologies. It established four technology hubs across IIT Bombay, ISC, and other institutes, focusing on sensors, quantum algorithms, and cryptography.
“India is becoming self-reliant and is on its way to become the new quantum powerhouse across the globe,” Mr. Yadav emphasized.
Export controls also affect collaboration in India: quantum systems with more than 35 qubits cannot be freely exported from the U.S. or Europe, complicating international partnerships. In this context, India faces the dual challenge of developing infrastructure while nurturing skilled quantum researchers, with only 152 researchers across 43 institutes in the country.
Risks and Misconceptions in Quantum Computing and Artificial Intelligence
Will AI take over the world? Is artificial intelligence the end of humanity? Have we brought upon us our own demise?
Despite popular media narratives, current AI is not synthetic intelligence, that can think and feel and act on its own. It lacks true reasoning and consciousness. The risks associated with AI and quantum AI are more practical than existential. Of course, a quantum system can fail just like any other, but the risks associated are not meant to take a bigger shape than projected. They are more involved in the workflow or block they were associated with. Some of the risks of quantum computers are:
- Errors due to parameter disturbances or noise.
- Biased outputs from incomplete datasets.
- Operational failures if systems are mismanaged.
“The risk is always there. It is the user in control of all computations,” Mr. Yadav clarified.
In essence, there is no risk of AI “taking over the world” — the real challenge lies in deploying these systems responsibly and ensuring they complement human expertise.
Looking Ahead: Quantum AI in 2035
“The real benefit that this world can get from AI is from research and development and engineering and manufacturing… That is the fundamental problem – we have to solve physics problems, unknown physics problems to crack this code…There is no silver bullet. We’ll try hundreds of applications, and one of them — maybe in drug discovery or materials science — will click. That’s when the world will see the real impact.”.
What was less than half a billion dollars in market value in 2025 is expected to become a multibillion-dollar industry by 2034. The next decade could witness quantum AI transforming research, engineering, and manufacturing. As Mr. Yadav explained, high-impact applications of quantum AI include:
- Drug Discovery: Assisting in the development of treatments for diseases such as Alzheimer’s, Parkinson’s, and various cancers. With almost 4000 diseases in the world, curing even a 1% of them would be highly beneficial and life-changing.
- Material Science: Simulating molecular structures to design stronger, lighter, or more efficient materials.
- Battery and Energy Systems: Optimizing batteries with minimal reliance on rare elements, lithium-less batteries, sustainable combinations, etc.
- Quantum Computational Fluid Dynamics (CFD): Enhancing automobile, aerospace, and mechanical design.
| Industry | Quantum AI Application | Expected Impact |
|---|---|---|
| Pharmaceuticals | Quantum simulation of molecular interactions | 10× faster drug discovery, fewer failed trials |
| Energy | AI-assisted quantum optimization of battery materials | More efficient solid-state and lithium alternatives |
| Aerospace | Quantum CFD (Computational Fluid Dynamics) simulations | Safer, more aerodynamic designs |
| Finance | Quantum optimization and risk modeling | Improved forecasting, portfolio management |
| Manufacturing | Quantum AI-driven R&D automation | Rapid prototyping and product innovation |
The ROI of quantum AI will emerge from reductions in energy, time, and computational cost in training AI models, as well as enabling larger, more complex simulations that classical systems cannot handle.
With such an accelerated growth trajectory, innovators and tech studios are already positioning themselves to lead this wave.
Advice for Innovators and Startups
For innovators and entrepreneurs stepping into the Quantum AI space, Mr. Aditya Yadav offers pragmatic, experience-based guidance that blends realism with ambition, reflecting the mindset needed to navigate one of the most complex intersections of science and computation.
Continuous Learning:
True progress demands an endless curiosity. “Education does not end at graduation,” he notes — a reminder that success in Quantum AI comes from continuous learning across disciplines. Beyond the technical foundations of physics and computer science, understanding economics, design thinking, and even art can spark the kind of creativity that drives real breakthroughs.
Solve Small Problems First:
Focus on niche, well-defined challenges that create tangible impact. This incremental approach helps startups prove value early, attract collaborators, and steadily expand into larger opportunities once the groundwork is validated — a principle that ensures both agility and scalability.
Skill Development:
Success in Quantum AI requires more than coding or research — it calls for hybrid expertise in quantum physics, artificial intelligence, and systems integration. Building diverse teams that can bridge theoretical models with real-world execution will be key to unlocking practical applications. Quantum-first organizations like Qalpa Labs, which accelerate quantum adoption through education, skill-building programs, and infrastructure enablement for academia, industry, and government, are shaping the talent and ecosystem required for this transition.
“If you want to start up in quantum AI, figure out a small problem you can solve better and focus on that,” Mr. Yadav advised.
Automatski, the company founded by Yadav and a tech partner of Wow Labz further exemplifies this focused innovation model. By specializing in Quantum Computational Fluid Dynamics (CFD), it applies quantum methods to simulate and optimize fluid behavior in engineering systems in aerospace, defense, and automotive design, demonstrating how precision-focused innovation can lead to transformative industrial outcomes.
Conclusion
Quantum AI is at the frontier of technological innovation. Its promise lies in solving problems that classical AI and computing cannot, from drug discovery to engineering design. Yet, challenges remain: scaling QNNs, improving qubit coherence and accuracy, building robust software frameworks, and navigating geopolitical and financial constraints.
India’s journey in quantum AI — through the National Quantum Mission and organizations like Automatski and Wow Labz — aptly reflects both ambition and the recognition of a global technological race. The path forward requires strategic investment, skilled talent, and practical problem-solving, not just theoretical research.
In essence, quantum AI represents a fusion of two revolutionary domains: the computational power of quantum systems and the learning capabilities of AI. When effectively harnessed, it could accelerate innovation across medicine, engineering, energy, and scientific research, offering solutions to problems previously deemed intractable.
As Mr. Yadav aptly concludes – “The future belongs to those who can combine human ingenuity with the computational power of quantum AI. It’s not about replacing humans, but about amplifying our ability to solve the unsolvable.”
For businesses, researchers, and innovators, the message is clear: engage with quantum AI now, understand its capabilities and limitations, and start building solutions that will define the next era of technology. The era of hybrid intelligence — where quantum systems augment human decision-making — is no longer a distant dream; it is unfolding before our eyes.
