Quantum machine learning is a subject of huge interest today as it promises to solve the more complex problems we face. Open source is helping it cross global boundaries so that teams across the world can collaborate to improve lives. What’s more, India has the potential to be a leader in this field.
Quantum computing and machine learning are transforming the way we work, learn and solve problems. Together they create quantum machine learning (QML), a rapidly growing technology that allows advanced data processing and improved decision making. Unlike classical machine learning, which powers recommendation engines, fraud detection systems, voice assistants and so many other products and services we see around us, quantum machine learning can tackle problems that are far too complex even for the fastest conventional computers.
Quantum computers use qubits (unlike binary bits), which can be 0, 1, or both simultaneously. They can process multiple possibilities at once, which helps them solve problems much faster than classical computers that use sequential methods. For instance, think about how to arrange the optimal routes for hundreds of delivery trucks in a city. A traditional computer would evaluate each route one at a time. Even with the best processors and resources, this takes a significant amount of time. A quantum computer can analyse numerous paths at once and get answers much more quickly.
Open source platforms such as Qiskit, Cirq and PennyLane provide users with accessible quantum machine learning capabilities. A student in Bengaluru can run quantum neural network simulations on a simulator, researchers in Zurich can use cloud-based quantum hardware to validate results and Toronto-based startups can work on financial models. This open participation facilitates worldwide collaboration between researchers engaging in joint experimental work and innovative projects.
Quantum machine learning is no longer only a research curiosity. It has progressed into a practical discipline that attracts worldwide participation.
The rise of quantum machine learning
Data and pattern recognition are the building blocks of machine learning. The more data a model gets, the better it becomes. But as data grows, so do the challenges. Classical computers struggle when it comes to very complex tasks like large scale optimisation or simulating molecules at the molecular level. There are limits to even the fastest supercomputers. This is where quantum computing comes into play.
Quantum computing emerged as a concept during the 1980s. Richard Feynman and David Deutsch, among other prominent physicists, demonstrated that quantum mechanics could help computers perform calculations that classical systems would never be able to achieve. Theoretical development of quantum computing remained limited because scientists struggled to build stable quantum systems. The development of qubits, error correction methods and quantum gates in recent years has enabled scientists to turn theoretical concepts into operational technology. The last decade has seen the development of prototype quantum processors from IBM, Google, and Rigetti, which have become accessible through cloud platforms.
QML research began in the mid-2010s when scientists investigated how quantum systems could improve machine learning algorithms. By combining the pattern recognition strengths of ML with the parallelism of quantum computing, they felt they could process extensively complex datasets at extraordinary speeds. Quantum computing parallelism enables systems to evaluate numerous possibilities simultaneously (rather than one by one), which results in faster information retrieval. For instance, the analysis of molecular interactions for new drugs through classical models requires weeks of processing time. Research at the University of Toronto shows that quantum inspired methods immensely accelerate protein structure prediction, a key step in drug design.
Quantum machine learning research continues to expand across the world. In India, research institutions such as IIT Madras and the Indian Institute of Science lead the country’s quantum technology efforts. The CQuICC (Centre for Quantum Information, Communication and Computing) at IIT Madras is involved in the research and development of quantum communication systems. The IISc Quantum Technology Initiative (IQTI) was started in 2020. The program promotes quantum research and unites physicists with computer scientists, engineers and material scientists to work on crucial research areas such as quantum computation and simulations, quantum communication and cryptography, quantum sensing and metrology, and quantum materials and devices.
Indian startups are developing practical quantum machine learning systems that can solve real world problems. QpiAI in Bengaluru has secured US$ 32 million (approximately ` 279 crore) from Avataar Ventures and India’s National Quantum Mission in its Series A funding round. The funding will help QpiAI expand its full stack quantum computing system, which serves the healthcare and manufacturing industries. Bengaluru-based BosonQ Psi has joined IBM’s Quantum Network to create quantum algorithms for engineering simulations. The platform enables users to build fast and accurate models of intricate problems faced in the aerospace, automotive and biotech fields. Scientists can perform experiments and proof-of-concept projects using cloud-based quantum tools, which remove the need for costly hardware infrastructure. These startups showcase India’s evolution from basic quantum research to practical technology development.
Open source ecosystem for QML
The rapid advancement of quantum machine learning (QML) stems primarily from the open source movement. Open source technology enables cost reduction and fosters worldwide participation in its development. The open nature of quantum computing enables developers, researchers and students to test innovative concepts, share their findings, and build on existing work. Today, innovation in quantum computing is not limited to a few top research labs. It is happening every day on GitHub, and in hackathons and educational institutions.
Multiple worldwide platforms serve as the foundation for this transformation. The quantum computing community relies on three main platforms, which include IBM’s Qiskit, Google’s Cirq and PennyLane from Canada-based Xanadu. Qiskit is a leading framework for quantum circuit development and simulation. Cirq provides Python-based quantum algorithm development capabilities. PennyLane, which is quite popular with developers these days, connects quantum computing with machine learning. Besides, it also works well with PyTorch and TensorFlow. These platforms demonstrate how open tools make quantum computing available to all users.
India is investing heavily in quantum computing development. The academic programs and research initiatives at IIT Madras and IISc Bangalore include quantum computing as part of their curriculum. The SWAYAM platform hosts IIT Madras’s quantum algorithms course, which is also supported by IBM. The students at IISc participate in workshops that teach them to work with tools like PennyLane. Apart from the classroom, communities such as Qiskit India and Quantum Computing India (QCI) are also educating enthusiasts to get involved through hackathons, fellowships and projects. QCI is providing training programs, governance structures and employment opportunities.
The Indian government started the National Quantum Mission (NQM) in April 2023 to encourage the use of quantum computing. The mission’s goal is to build quantum computers with 50 to 1000 qubits by 2031, at a budget of `60,030 million.
A major breakthrough came in April 2025, when Bengaluru-based startup QpiAI launched QpiAI Indus, a 25-qubit superconducting quantum computer. QpiAI is one of the eight startups supported under the National Quantum Mission. Indus is India’s first full stack quantum system. It combines advanced hardware with scalable controls and AI driven quantum software. The system has applications across life sciences, materials science, logistics, climate action and several other fields. Bengaluru serves as home to India’s first operational quantum computer and a Quantum Research Park, making the city a key hub for quantum innovation.
The mix of open source platforms, government programs and startup innovation is building a strong Indian ecosystem for quantum technologies. National missions and local collectives such as QCI ensure that training, industry use and homegrown research move forward together. This transformation is being driven by open collaboration, which is turning today’s learners into the next generation of quantum experts.
Key open source platforms for QML
| Platform | Function | Applications |
| Qiskit (IBM) | Quantum circuit development and simulation | Widely used for research, education and prototyping |
| Cirq (Google) | Python-based quantum algorithm development | Suitable for algorithm testing and experimental QML |
| PennyLane (Xanadu) | Integrates quantum computing with machine learning | Works with PyTorch, TensorFlow; popular for QML research |
Key quantum machine learning algorithms
| Algorithm | Purpose | Applications |
| Quantum SVM | Fast classification by evaluating multiple possibilities simultaneously | Fraud detection, medical diagnosis |
| VQE (Variational Quantum Eigensolver) | Finds optimal solutions for complex optimisation problems | Molecular behaviour, chemistry simulations |
| QAOA (Quantum Approximate Optimization Algorithm) | Solves large scale optimisation tasks | Routing, scheduling, logistics planning |
Core algorithms and applications
Any machine learning system depends on algorithms as its core foundation. The process of teaching computers to learn from data requires sequential instructions, which form the basis of these algorithms. Quantum machine learning (QML) uses quantum mechanics to develop new methods for solving problems.
The Quantum Support Vector Machine is a good example in this field of research. Let’s take one example to understand it intuitively. A crowded hall requires a method to distinguish between two distinct groups of people. A classical computer performs individual assessments of all possibilities while creating a boundary line. The quantum system performs multiple possibility tests at once to discover the optimal boundary in a very short time. This system proves highly effective for fast classification operations, which include fraud detection and medical diagnosis.
The Variational Quantum Eigensolver or VQE functions as a core algorithm that determines the best solution from multiple optimisation problem choices. The VQE algorithm provides chemists with various ways to study molecular behaviour.
The Quantum Approximate Optimization Algorithm (QAOA) solves complex problems that include routing and scheduling operations. The system produces flawless delivery routes for thousands of trucks.
Today quantum machine learning applications are transitioning from laboratory environments to real-world deployment. Roche and Cambridge Quantum are working together to apply QML for initial drug development through their research activities. Goldman Sachs and JPMorgan Chase apply QML to perform portfolio risk assessments. Volkswagen leverages QML to forecast traffic movement patterns and Airbus employs quantum techniques to develop lightweight aircraft materials. The Tata Institute of Fundamental Research in India is advancing materials science research through quantum algorithm development.
What’s holding QML back?
Despite the excitement, QML is facing numerous obstacles. The most obvious is hardware. The current quantum computers are noisy and their qubit capacity remains limited. Running large scale algorithms becomes challenging because of these constraints. Most QML research will depend on simulators and small quantum devices until hardware development advances further.
Algorithms are another challenge. Scientists have proposed multiple practical quantum algorithm solutions, yet most of these approaches fail to outperform classical methods when solving problems in the real world.
Open source development also faces multiple obstacles. Different platforms and frameworks lead to platform fragmentation. The process of tool integration becomes challenging because developers struggle to find suitable ecosystems that meet their needs.
In India, one of the challenges is the availability of trained talent. The number of experts in IITs and IISc continues to grow but remains insufficient to meet global market needs. The search for engineers who possess both machine learning expertise and quantum computing abilities proves challenging for most startups. Even in countries with advanced programs, there aren’t enough professionals with skills in both domains.
However, quantum hardware companies are actively working to develop stable qubits. Universities have established new interdisciplinary programs that combine physics with computer science and artificial intelligence. Open source communities are uniting various platforms to create enhanced learning experiences. The path to progress is difficult but is showing signs of significant development.
Opportunities on the horizon
The upcoming years will see open source platforms serving as the primary testing ground for developers to test new ideas in QML. India has the potential to establish itself as a top research and development hub for QML. The combination of experienced software developers with expanding quantum research funding will help India establish itself as the leading centre for QML development.
The advancement of quantum machine learning also depends on global partnerships. No single country or company can dominate this field alone. Open source technology serves as the essential link which will transform theoretical QML into operational applications that will drive growth.
