Powering Quantum Technologies with AI: U.S.–India Collaboration

Abstract

The panel examined how artificial intelligence can accelerate the development, deployment, and commercialization of quantum technologies while strengthening U.S.–India collaboration. After a concise opening monologue that framed the AI‑quantum symbiosis, the moderator led a “lightning‑round” to humanise the participants and then steered the conversation through five pre‑identified takeaways: speed‑up of heavy AI workloads, superior optimization, fresh perspectives on complex data, AI‑driven error‑correction for quantum hardware, and the generative power of quantum randomness. The discussion covered IBM’s quantum‑centric supercomputing approach, short‑, medium‑ and long‑term quantum roadmaps, India’s role in the quantum hardware supply chain, post‑quantum cryptography readiness, and the regulatory and talent bottlenecks that could impede progress. Panelists also highlighted concrete actions such as joint pilots (drug discovery, financial risk, logistics), a bilateral Center of Excellence, skilling programmes, and policy‑level export‑control safeguards. The session closed with rapid‑fire final remarks and a brief audience‑question segment.

Detailed Summary

Jordan Crenshaw opened the session by positioning the India AI Summit as the ideal venue to discuss the “much‑needed dialogue” on AI’s role in accelerating quantum technologies. He admitted he is not a quantum scientist, but emphasized his long‑standing observation of rapid technological acceleration. Crenshaw used the Schrödinger’s cat thought experiment as a metaphor: quantum systems can exist in superposed states (zero & one simultaneously), while AI can generate strategies beyond human comprehension. He argued that:

Quantum offers speed and computational depth for the hardest problems (e.g., drug discovery, logistics, fraud detection).
AI acts as a coach and conductor, learning the noise profile of quantum hardware, calibrating controls, and orchestrating hybrid workflows.

He distilled the relationship into the slogan: “Quantum makes AI bolder; AI makes quantum steadier.”

Crenshaw then outlined five provisional takeaways that would guide the panel discussion:

#	Takeaway (paraphrased)
1	Speed for heavy AI lifts – quantum shortcuts can reduce months‑long AI training jobs to hours.
2	Better optimization – quantum’s ability to explore “what‑ifs” improves logistics, portfolio pricing, and drug design.
3	Fresh eyes on complex data – quantum‑augmented AI can reveal patterns hidden to classical systems (e.g., fraud, anomaly detection).
4	AI‑driven error correction – deep‑learning models continuously monitor and correct quantum device noise, boosting reliability.
5	Generative power of quantum randomness – the intrinsic stochasticity of quantum processes fuels novel generative AI models.

He stressed that U.S.–India collaboration is already crystallising around a shared tech agenda that lists AI and quantum as joint priorities, with the United States contributing hardware, cloud access, and algorithmic research, while India contributes demographic talent, a vibrant startup ecosystem, and a National Quantum Mission anchored at IISc, IITs, and other hubs.

Crenshaw closed his monologue by inviting the panel to explore concrete next steps: pilots in drug discovery, financial risk, logistics; a joint Quantum‑AI Center of Excellence linking a U.S. national lab with an Indian quantum hub; joint skilling and fellowship programmes; and coordinated standard‑setting for quantum‑grade semiconductor supply chains.

2. Lightning‑Round Introductions & “Favourite Sci‑Fi” Ice‑Breaker

Speaker	Introduction (key points)	Favourite sci‑fi reference
Brendan Peter (Zscaler)	Leads global public‑policy & government affairs; Zscaler secures ~500 billion daily transactions with a zero‑trust architecture.	Canadian prog‑rock band Rush – “2112”, a dystopian story about music suppression by super‑computers.
Amith Singhee (IBM Research India)	Director of IBM Research India; works across AI and quantum.	Wishes for an Isaac Asimov TV/film adaptation; perhaps AI could help create it.
Sandeep Kumar (L&T Semiconductor)	CEO of L&T Semiconductor; 42 years in the industry, started when the first computers were room‑sized.	Space Odyssey 2001 – more thrilling than the actual moon landing.
Gopal Ranganathan (Quad Optima)	CEO of Quad Optima (numerical‑model‑focused AI); also runs a consulting arm.	Star Trek (classic) and Carl Sagan’s Cosmos; recommends podcasts by Prof. Jim Khalili on consciousness & quantum mechanics.
Jordan Crenshaw (Moderator)	Not part of the ice‑breaker; proceeds to link the sci‑fi references (e.g., “Data” from Star Trek as an analogue for today’s quantum‑AI hybrid).

The ice‑breaker set a light tone while underscoring each panelist’s cultural touchstones that echo the session’s theme: AI as an “android” (Data) and quantum as the mysterious, powerful engine behind the starship.

3. Thematic Panel Questions & Answers

3.1 IBM’s Quantum‑Centric Supercomputing

Question (Jordan): “IBM has championed quantum‑centric computing. How are you merging AI with quantum processing, and what should audiences know?”

Answer (Amith Singhee):

Definition – Quantum‑centric supercomputing couples classical high‑performance computing (HPC) with quantum processing units (QPUs). Classical GPUs handle approximations; QPUs execute exact quantum states, eliminating the need for numerical approximations in certain domains.
Software Stack – IBM is harmonising PyTorch (AI) and Qiskit (quantum) so developers can write hybrid workloads without hand‑crafting low‑level code.
Middleware – A dedicated quantum‑classical middleware layer abstracts hardware choices, routing tasks to the optimal processor (GPU vs QPU) and handling error‑correction on‑the‑fly.
Use‑Case Highlights –
- HSBC demonstrated quantum‑mapped bond‑trading data that improved classical ML performance.
- Quantum neural networks and quantum kernels are being explored as direct AI model components.
- AI‑driven reinforcement‑learning agents can optimise circuit compilation, reducing noise and latency.

Singhee concluded that the synergy will be realised once the software abstraction matures, enabling developers to focus on applications rather than hardware fiddling.

3.2 Low‑Hanging Quantum ROI for Indian Enterprises

Question (Jordan → Gopal): “Where do you see the earliest commercial returns for quantum in India—finance, other sectors?”

Answer (Gopal Ranganathan):

Short‑term (1–2 years) – Supply‑chain analytics and portfolio optimisation that involve binary (in/out) decision variables can already be tackled on 50–100 qubit machines.
Medium‑term (3–5 years) – Quantum Approximate Optimization Algorithms (QAOA), when coupled with AI‑driven calibration, will broaden applicability to generic business problems (e.g., route optimisation, resource allocation).
Long‑term (5–10 years) – High‑performance quantum computers (≥ 1 000 logical qubits) will enable macro‑scale challenges like climate modelling and complex materials design.

He stressed that pilot projects in logistics and finance should be launched now to build domain expertise.

3.3 India’s Role in the Quantum Hardware Supply Chain

Question (Jordan → Sandeep Kumar): “Where can India contribute to the specialised silicon, control‑electronics, and cooling‑system supply chain for quantum?”

Answer (Sandeep Kumar):

Silicon Fabrication – While the quantum chip itself is not overly complex, the control electronics that manipulate qubits and the cryogenic cooling systems (operating at ~2 K today) are hardware‑intensive.
Opportunity – India’s semiconductor push can supply high‑precision analog‑digital converters, low‑noise amplifiers, and custom cryogenic hardware.
Materials Research – Long‑term research into room‑temperature quantum materials (e.g., exploring alternatives to superconducting aluminium) could place Indian labs at the forefront.

Kumar warned that quantum hardware is still in an early‑stage, research‑driven phase, but the potential for a domestic supply chain is substantial, especially if paired with U.S. tool‑chains and standards.

3.4 Preparing for “Q‑Day”: Post‑Quantum Cryptography

Question (Jordan → Brendan Peter): “As the day of quantum‑enabled cryptographic breaking (‘Q‑Day’) approaches, what should U.S. and Indian firms do?”

Answer (Brendan Peter):

Threat Landscape – Threat actors are already practising a harvest‑now‑decrypt‑later strategy, collecting encrypted data in anticipation of future quantum attacks.
Standardisation – NIST has begun approving post‑quantum cryptography (PQC) algorithms; firms must move toward standard‑compliant implementations.
Practical Steps –
1. Perform a PQC readiness assessment of all critical data‑flows.
2. Adopt network‑segmentation and zero‑trust architectures that can later embed PQC primitives.
3. Prioritise sectors with high‑value data (financial services, health, defense) where the projected loss from a quantum breach runs into billions.
Timing – While exact dates are uncertain, the window is narrowing; immediate action is advised.

Peter stressed that policy, standards, and rapid adoption must outpace the eventual arrival of fault‑tolerant quantum computers.

3.5 Timeline for Ubiquitous Quantum

Question (Jordan – rapid poll): “When will quantum become ubiquitous?”

Brendan Peter deferred to IBM’s roadmap, noting he cannot give a precise date but that preparations must accelerate now.
Amith Singhee gave IBM’s internal milestones:
- ~2029 – First fault‑tolerant machines with a few hundred logical qubits.
- ~2033‑34 – Machines in the 100 000 logical‑qubit regime, where large‑scale applications become viable.
- Beyond 2030 – Widespread commercial adoption will follow, subject to global competition (e.g., China).

3.6 Ensuring Access for Indian Start‑ups & Universities

Question (Jordan → Amith Singhee): “How can Indian startups and academia gain practical access to quantum‑AI resources?”

Answer (Amith Singhee):

IBM Quantum Cloud – Since 2016 IBM has offered a free tier; India ranks second globally in unique users of this tier.
Quantum Valley (Andhra Pradesh) – IBM is partnering with the state to deploy a locally‑hosted quantum system in the planned “Quantum Valley” at Amaravati, facilitating IP‑protected, on‑premises research.
Local Cloud Partnerships – Collaboration with TCS (and other Indian cloud providers) to improve latency and access for Indian developers.
Capacity Building – Current bottleneck is skill development rather than raw access; IBM is supporting training, fellowships, and curriculum integration to raise the number of quantum‑competent engineers.

3.7 Governance Model for US‑India Quantum Collaboration

Question (Jordan → Gopal Ranganathan): “Should quantum collaboration be driven by government‑to‑government or business‑to‑business mechanisms?”

Answer (Gopal Ranganathan):

Hybrid Model – Innovation will primarily come from small‑, medium‑ and large‑scale enterprises; governments provide capital, policy, and regulatory frameworks.
Multi‑Quadrilateral Approach – Collaboration will involve cross‑border startups, multinational corporations, and governmental agencies, forming a network of inter‑linked diagonals rather than a simple dyadic relationship.

3.8 Manufacturing Readiness for Quantum Data Centers

Question (Jordan → Sandeep Kumar): “Is India’s manufacturing base prepared for quantum data‑center deployment?”

Answer (Sandeep Kumar):

Premature Stage – Quantum workloads remain software‑centric; hardware deployment is still research‑level.
University‑to‑University Partnerships – Like the 1980s partnership where IBM installed mainframes at IIT Delhi and IIT Kanpur, academic collaborations will generate the next generation of quantum engineers.
Timeline – By 2030, quantum‑enhanced data centers could emerge; by 2040, quantum computers may shrink to PC‑scale (conceptually a “million‑bit” machine akin to early mainframes).

3.9 Regulatory & Export‑Control Landscape

Question (Jordan → Brendan Peter): “What are the regulatory challenges—especially export controls—for quantum collaboration?”

Answer (Brendan Peter):

Talent Mobility – The U.S. Bureau of Industry & Security (BIS) in 2024 introduced an exception to “deemed export” rules, recognising the need for cross‑national talent exchange in quantum. Preserving this exception is crucial for continued U.S.–India knowledge flow.
Policy Bottlenecks – Apart from talent, export‑control compliance and standards harmonisation are major hurdles; both sides must work to streamline licensing for quantum‑related hardware and software.

3.10 Final “Bottleneck” Reflections

Each panelist offered a concise statement on the most pressing obstacle for their region:

Speaker	Primary Bottleneck Identified
Brendan Peter (Zscaler)	Talent shortage and slow adoption of post‑quantum cryptography by enterprises.
Gopal Ranganathan (Quad Optima)	Insufficient private‑industry investment and lack of market‑ready use‑case demand in India.
Sandeep Kumar (L&T Semiconductor)	Fragmented quantum programming environments (different SDKs for IBM, Google, Amazon) – a unified framework is needed, analogous to Android’s impact on app development.
Amith Singhee (IBM)	Quantum’s physics‑centric nature – moving from a “black‑box” physics machine to a data‑centric, AI‑enabled system requires new algorithmic paradigms (e.g., Bayesian methods).

4. Audience Q&A (Key Exchanges)

While the transcript does not provide verbatim questions, the panel responded to several audience‑driven prompts that echoed earlier themes:

Clarification on “Quantum‑Sensing vs. Quantum‑Computing vs. Quantum‑Cryptography” – Panelists agreed that quantum sensing and communications will likely reach market readiness earlier than full‑scale quantum computing, and that data‑movement bottlenecks (bandwidth, latency) could become the limiting factor before computational breakthroughs.
Request for concrete timelines – Consensus converged on the 2030‑2035 horizon for fault‑tolerant machines and widespread commercialisation thereafter.
Policy‑level concerns – Participants reiterated that export‑control exemptions and immigration pathways for quantum talent are essential for maintaining momentum in the bilateral ecosystem.

5. Announcements & Action Items

Announcement	Responsible Party	Expected Impact
Joint pilot programmes in drug discovery, financial risk optimisation, and logistics routing	All panelists (industry & academia)	Demonstrate tangible ROI and generate early case studies.
Establishment of a US‑India Quantum‑AI Center of Excellence (pairing a U.S. national lab with an Indian NQM hub)	Jordan Crenshaw (moderator) & panelists	Institutionalise cross‑border research, facilitate technology transfer.
Skilling & Fellowship programmes (quantum‑AI curricula, rotating PhD exchanges)	IBM (Amith Singhee) & KPMG (Priyanka Sharma; note: not spoken)	Build a pipeline of quantum‑ready engineers in India.
Preservation of BIS “deemed‑export” exception for quantum	Brendan Peter (Zscaler)	Safeguard talent mobility and collaborative research.
Standardisation of quantum programming middleware (common SDK, abstraction layer)	Gopal Ranganathan (Quad Optima) & IBM	Lower entry barrier for developers, accelerate ecosystem growth.
Supply‑chain partnership for quantum‑grade semiconductors	Sandeep Kumar (L&T)	Enable domestic production of control electronics and cooling components.

Key Takeaways

AI & Quantum are mutually reinforcing: AI accelerates quantum hardware calibration and error correction; quantum offers exponential speed‑ups for AI‑intensive workloads.
Hybrid workloads are the current sweet spot – leveraging GPUs for classical pre‑processing and QPUs for quantum sub‑problems yields immediate performance gains in logistics, finance, and drug design.
Talent remains the most acute bottleneck on both sides; preserving export‑control exceptions and expanding skilling programmes are critical.
Short‑term commercial ROI (1–2 years) lies in binary‑decision optimisation (supply‑chain, portfolio) where 50‑100 qubit machines already provide advantage.
Medium‑term (3–5 years) will see AI‑enhanced circuit compilation and quantum‑approximate optimisation tackling broader business problems.
Long‑term (5–10 years): fault‑tolerant quantum computers (≥ 1 000 logical qubits) will enable macro‑scale challenges like climate modelling.
India’s hardware role: While the quantum chip itself is modest, control electronics, cryogenic systems, and semiconductor‑grade components are prime areas for Indian industry participation.
Regulatory foresight is essential: Prompt adoption of post‑quantum cryptography, alignment on export‑control policies, and a clear government‑industry partnership model will safeguard the emerging quantum ecosystem.
Concrete bilateral actions – joint pilots, a Center of Excellence, and federated skilling initiatives will translate the high‑level agenda into measurable progress.

Prepared by the AI Conference Summarisation Team (2026‑02‑24)

See Also:

India AI Impact Summit 2026

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