Feeders Before Qubits: What India's DISCOMs Should Actually Do About Quantum Optimisation

Executive Summary

I get asked about quantum computing in almost every strategy conversation now. The question is usually some version of: when does this hit my distribution business?

My answer has hardened over the last year. Quantum optimisation for distribution grids is credible research. It is not a 2026 procurement decision, and anyone selling it as one is selling something else. A preprint accepted for the CIRED 2026 Brussels Workshop put hard numbers on the gap: a real 309-node distribution network needs an estimated 61,172 logical qubits and roughly 494 million logical rotation gates for a single optimisation layer, with runtimes reaching 10⁷ seconds. Even the textbook IEEE-33 benchmark needs 667 logical qubits.

That is not a near-term machine. But it is also not a reason to do nothing. The preparation that makes a DISCOM quantum-ready — clean network models, formally stated optimisation objectives, measured baselines — is the same preparation that unlocks value from classical solvers today. That is the honest arbitrage. Do the boring work now; the hardware question answers itself later.

The Gap Is Four Orders of Magnitude, and That Is Useful Information

In July 2025, IonQ and Oak Ridge National Laboratory demonstrated a hybrid quantum-classical approach to the unit commitment problem on a 36-qubit Forte Enterprise system, producing generation schedules across 24 time periods and 26 generators. IonQ's own framing is instructive: it expects systems with 100–200 high-fidelity qubits to reach grid-scale unit commitment.

Hold that against the distribution-side estimate of 61,172 logical qubits. These are different problems — generation scheduling is smaller and better structured than feeder reconfiguration on a real network — but the spread tells you where to look. Transmission and generation optimisation will meet useful quantum hardware years before distribution does.

For a North Bihar DISCOM, that ordering matters. Our hardest combinatorial problems sit downstream: feeder reconfiguration under time-varying load, DT-level loss allocation, phase rebalancing across thousands of service points, crew and asset routing across scattered subdivisions. These are exactly the problems that scale worst in the qubit estimates.

Where the Serious Money Is Actually Going

Utility interest is real and it is being funded, which is a different signal from vendor noise.

EDF is working with Pasqal on renewable forecasting, grid integration and EV charging schedule optimisation. Iberdrola is testing quantum methods for optimal energy storage siting — balancing cost, voltage control and reliability. In the US, the Department of Energy's Office of Technology Commercialization has partnered with Connected DMV on a quantum algorithm competition targeting grid planning: resource adequacy, interconnection, transmission expansion, storage deployment and microgrid placement.

Notice the pattern. Every one of those is a planning problem, not an operations problem. Nobody credible is proposing quantum in the loop for real-time switching. The value case sits where you can afford a slow answer that is meaningfully better than the one you have.

India's own position is stronger than most people assume. The National Quantum Mission received ₹900 crore in Budget 2026 — a 50% increase over the ₹600 crore allocated in Budget 2025 — against a total mission outlay of ₹6,003.65 crore running to FY2030–31. The capability is being built domestically. The question for utilities is whether we will have problems stated well enough to hand to it.

The Readiness Framework I Use

I assess quantum readiness on four layers. Only one of them involves quantum.

The sequence is strictly bottom-up. A DISCOM that cannot state its feeder reconfiguration objective as a constrained optimisation problem cannot benefit from a quantum solver, a classical solver, or a consultant. And a DISCOM that has done layers one through three is already capturing value — loss reduction, better DT sizing, smarter capex sequencing — regardless of what happens to the hardware.

This is the part leaders miss. Quantum readiness is not a quantum programme. It is a data and decision-discipline programme with a quantum option attached.

The Honest Risk: Buying a Narrative Instead of a Capability

There is a failure mode I want named, because I expect to see it in Indian utilities within eighteen months. It looks like a quantum MoU, a press release, a pilot with no baseline, and a report that says the technology is "promising." Nothing in the distribution business changes.

The tell is always the same: no classical control arm. If a proof-of-concept does not report what a well-tuned classical solver achieved on the identical problem with the identical data, it has measured nothing. The CIRED preprint is refreshingly candid on this — its authors note the formulation was not explicitly solved and should be validated on smaller networks first. That is what scientific honesty looks like, and it is the standard a utility board should demand of any vendor deck.

Separately — and this one is urgent — post-quantum cryptography migration for metering and SCADA infrastructure runs on its own clock and should not be bundled into an optimisation conversation. Different risk, different timeline, different owner.

What This Means for Leaders

1. Fund model fidelity, not qubits. Budget the GIS-to-field reconciliation of your LT network this financial year. Every downstream optimisation, classical or quantum, is capped by it.
2. Write down three optimisation problems formally. Pick feeder reconfiguration for loss minimisation, DT capacity augmentation sequencing, and crew routing. Objective, constraints, cost function, data sources. One page each.
3. Establish classical baselines before any pilot. Run a commercial or open-source solver on those three problems. Record the result. This becomes the control arm for every future claim.
4. Engage the National Quantum Mission ecosystem as a problem-provider, not a buyer. Indian quantum startups and academic groups need real utility problems far more than they need our procurement budgets. That trade is cheap and it builds domestic capability.
5. Keep PQC migration on a separate track with a named owner. Cryptographic exposure in the metering fleet is a 2026–2030 risk with a firm deadline. Optimisation is a 2030+ opportunity. Do not let one delay the other.

Key Takeaways

• Distribution-scale quantum optimisation needs an estimated 61,172 logical qubits for a 309-node network; today's demonstrations run at 36 qubits. Plan accordingly.
• Utility quantum work worldwide targets planning problems, not real-time operations — that is where a slow, better answer has value.
• India's National Quantum Mission funding rose 50% to ₹900 crore in Budget 2026; domestic capability is arriving faster than utility problem-readiness.
• The four readiness layers — model fidelity, problem statement, classical baseline, quantum formulation — must be built in order.
• Any pilot without a classical control arm is marketing. Demand the baseline.
• Post-quantum cryptography is a separate, more urgent programme. Do not conflate them.

Randhir Verma leads enterprise digital transformation and smart metering programmes across 6M+ endpoints, and writes daily on power, AI, forward-deployed engineering and quantum computing.

Sources: Quantum Resource Estimation for Minimising Energy Grid Losses (arXiv, CIRED 2026 Brussels Workshop preprint) · IonQ and Oak Ridge National Laboratory demonstrate quantum power grid optimisation · Quantum Computing Is Emerging as a Tool for Tomorrow's Electrical Grids (POWER Magazine, July 2026) · Budget 2026: National Quantum Mission Gets 50% Funding Boost to ₹900 Crore · DOE Partners on Quantum Algorithm Competition to Strengthen Grid Resilience