India’s Quantum Random Number Race: Securing the Unpredictable

The problems being solved

Randomness is the silent backbone of digital trust. Every encrypted message, every secure lottery draw, every cryptographic key relies on numbers that are truly unpredictable. Yet generating such numbers is harder than it seems. Classical pseudo-random generators can be reverse-engineered; even physical sources can harbour subtle biases or be manipulated. In India, innovators are zeroing in on four concrete problem clusters.

The first is trust. A random number is only as good as the proof that it hasn’t been tampered with. Researchers are tackling this head-on with self-testing quantum random number generators that estimate min-entropy in real time, and with loophole-free tests based on the Leggett-Garg inequality—a way to certify randomness without fully trusting the device that produced it. Another angle is generating verifiable randomness directly from untrusted shot-noise sources, so that even a compromised photodetector cannot rig the outcome.

Speed is the second frontier. As data centres and communication networks move to quantum-safe cryptography, they need randomness at gigabit-per-second rates. Indian work is exploring multi-bit generation using path-entangled single photons, where a single photon can deliver several random bits at once, and multi-channel architectures on programmable photonic chips that multiply throughput without multiplying hardware.

A third theme is the entropy source itself. Instead of the usual laser phase noise, inventors are harvesting multisource quantum shot noise and using tensor or permutation extraction to distil pure randomness. Others are tapping the quantum tunnelling current in ordinary transistors—an entropy source that could be built into standard CMOS chips. And some are deliberately avoiding post-processing altogether, letting the raw quantum output speak for itself.

Finally, application-specific needs are shaping the problems. A lottery operator needs verifiable, tamper-proof randomness that a third party can audit. A game developer wants a visual, intuitive representation of randomness evolution—think weather-inspired 2D models that show how entropy unfolds over time. These are not generic requirements; they demand tailored solutions.

How the field is solving it

The technical approaches emerging from Indian labs are as diverse as the problems. Photonic integrated circuits sit at the heart of the speed push. By fabricating programmable Mach-Zehnder interferometers on a chip and feeding them with path-entangled photons, researchers can generate multiple random bits per detection event. Dynamic phase control allows a single chip to operate as several independent random number generators in parallel, dramatically boosting the bit rate without a proportional increase in footprint or power.

Entropy source engineering is another active vein. Circuits that deliberately exploit quantum tunnelling current in a transistor turn a ubiquitous electronic component into a randomness well. Others harvest shot noise from multiple optical sources and then apply tensor or permutation extraction to remove any residual correlation. The goal is to get closer to the physical quantum process and reduce reliance on complex post-processing that can slow things down or introduce algorithmic artefacts.

Self-testing and verification protocols are where theory meets practice. Oracle-based verification, min-entropy estimation, and Leggett-Garg inequality tests are being embedded into QRNG designs so that the device can prove its own randomness continuously. This semi-device-independent approach means you don’t need to trust the manufacturer—the physics vouches for the output. In a country where hardware supply chains can be opaque, that’s a powerful selling point.

Post-processing itself is being rethought. Some designs use sophisticated extraction to enhance randomness from multiple weak sources; others deliberately avoid any digital clean-up to preserve the quantum purity of the signal. The choice depends on the application: a cryptographic key generator might want the raw quantum output, while a gaming system might prefer a whitened stream that passes statistical tests with flying colours.

Application-specific integration ties these threads together. A lottery verification system, for instance, combines a QRNG with a public audit trail so that anyone can check the draw was fair. A weather-inspired visualisation tool models the evolution of randomness as a 2D landscape, making entropy tangible for non-experts. These integrations show that the field is not just about faster or purer numbers—it’s about making randomness usable in the real world.

Where the market is heading

The broader true random number generator market, which includes both classical and quantum devices, was valued at roughly USD 4.7 billion globally in 2024 and is projected to grow at over 8% annually through 2030, according to Grand View Research. The quantum computing threat is a major accelerant: as organisations prepare for post-quantum cryptography, the demand for randomness that can withstand a quantum adversary is climbing fast.

A landmark moment came when a software-based quantum random number generator received NIST validation, signalling that QRNGs are moving from physics experiments to federally recognised cybersecurity tools. This opens the door for adoption in government, finance, and critical infrastructure. Meanwhile, the integration of QRNGs into IoT devices, cloud platforms, and blockchain networks is expanding the addressable market well beyond traditional encryption.

India’s own momentum is building. The National Quantum Mission, operational since early 2024, has put quantum technologies on a mission-mode footing with dedicated hubs at IISc Bengaluru, IIT Madras, IIT Bombay, and IIT Delhi. A rolling call for quantum startups, opened in mid-2025, specifically invites proposals in quantum communication and computing—areas where QRNGs are foundational. Sustained government investment and strategic partnerships are nurturing an ecosystem that can take lab prototypes to field-deployable products.

While a standalone QRNG market size for India isn’t yet carved out, the alignment of policy push, growing startup activity, and the global security imperative suggests that the country is positioning itself as a serious player in the quantum randomness supply chain.

The white space

Even with this momentum, large opportunity gaps remain—and they are framed positively as the next frontiers. Low-cost, miniaturised QRNGs are still missing from consumer electronics and the billions of IoT endpoints that need lightweight security. A QRNG that fits on a microcontroller and costs pennies would unlock entirely new markets in smart home devices, wearables, and edge computing.

Seamless integration with classical cryptographic systems is another open field. Today’s QRNGs often sit as standalone peripherals; the real prize is embedding them into standard security protocols and hardware security modules so that end-to-end quantum-safe encryption becomes plug-and-play. Indian IT services and system integrators are well placed to bridge this gap.

On the physics side, continuous-variable QRNGs using squeezed states or vacuum fluctuations are barely explored in India. These approaches could offer higher entropy rates and simpler optical setups than discrete-variable methods, yet they remain largely a white space. Similarly, error mitigation and fault tolerance in QRNG hardware—ensuring that a device keeps producing certified randomness for years without drift—is an engineering challenge that, once solved, would dramatically increase commercial viability.

Each of these gaps is an invitation. The combination of India’s semiconductor design talent, photonics research base, and a government actively de-risking deep-tech ventures creates a fertile ground for inventors willing to step into these uncharted areas.

Explore the innovators

The specific inventors, patent filings, and research groups working on quantum random number generation in India are as varied as the problems they tackle. From self-testing protocols that challenge our very notion of trust, to photonic chips that squeeze multiple random bits from a single photon, the ingenuity is striking. These innovators are spread across academic hubs, government labs, and a growing number of deep-tech ventures. To see exactly who is building what, and to trace the connections between ideas, explore the live landscape on Deeptech Navigator—where the patents, the people, and the possibilities come together.

In our data

Sources

• What Is a Quantum Random Number Generator (QRNG)? ↗
• Quantum Random Number Generator (QRNG) - Explained ↗
• Quantum Random Number Generation (QRNG) ↗
• Quantum Random Number Generator Market Size, Report ... ↗
• How manufacturing is harnessing quantum technologies ↗
• Quantum-Dot Quantum Random Number Generator Market ↗
• True Random Number Generator Market Size Report 2030 ↗
• Quantum Random Number Generator Market Size ↗