How Quantum Energy Positions Europe for the Next Leap in Clean-Tech Competitiveness
Executive Summary
The European energy transition has entered its decisive decade. Renewable generation is growing faster than grid flexibility can follow, and markets are becoming increasingly volatile. Every day, energy traders, aggregators, and asset operators make thousands of complex decisions — when to store, when to sell, and how to balance power flows profitably and responsibly.
Traditional optimisation tools are reaching their limits. As the number of variables expands exponentially — prices, weather, regulation, grid constraints — even the best classical algorithms struggle to find the true optimum.
Quantum Energy introduces a new class of intelligence: quantum-enhanced optimisation. Our platform combines AI forecasting, physics-based simulation, and quantum algorithms to explore millions of possible actions in parallel, selecting the most profitable and sustainable energy decisions in real time.
This white paper explains why quantum computing is relevant now, how hybrid quantum-classical optimisation works in practice, and how Europe can capture leadership by deploying quantum-ready systems in its clean-tech industries today.
1. The Context — Energy’s Algorithmic Bottleneck
Over the last decade, Europe has led the world in renewable integration. Solar, wind, and hydro already account for more than 45% of the continent’s electricity generation. Yet these achievements come with new forms of complexity:
- Volatile market prices changing every 15 minutes.
- Multiple market layers (Day-Ahead, Intraday, Balancing).
- Dynamic weather and demand forecasts feeding uncertainty.
- Grid congestion requiring localised decisions in milliseconds.
Every storage unit, every flexible plant, and every energy trader faces an enormous decision tree: when to charge, when to discharge, how much to hold, and how to coordinate across markets and assets.
Conventional optimisation — even with modern AI — must simplify these problems to make them solvable. Simplification means lost profit and inefficiency.
The result is clear: energy markets have outgrown classical intelligence.
2. Why Quantum Matters — From Exponential Problems to Exponential Processors
Quantum computing is not a faster version of classical computing; it’s a different paradigm.
Instead of processing one possibility at a time, quantum algorithms can evaluate many possibilities simultaneously, exploiting quantum phenomena such as superposition and entanglement.
This parallelism is especially valuable for combinatorial optimisation — problems with many interconnected choices, such as portfolio optimisation, logistics routing, and battery dispatch scheduling.
Energy trading decisions fit perfectly into this class: each time-step and each asset adds a new layer of interdependent variables. The number of possible schedules for a single 100-MW battery across one day can exceed 10¹⁸ combinations.
A classical solver must prune this space.
A quantum-enhanced solver can explore it more holistically — identifying better patterns and correlations across multiple markets and constraints.
3. The Hybrid Approach — Quantum Energy’s Architecture
The true power today lies not in pure quantum computers (still limited in qubit count and coherence), but in hybrid systems — algorithms that combine classical computing power with quantum modules.
Quantum Energy’s architecture is hybrid by design, featuring three layers:
1️⃣ Forecast Layer (AI & Data)
- Collects market data (HEnEx, ENTSO-E, EPEX), weather forecasts, and asset telemetry.
- Uses machine learning to predict price trajectories and grid conditions.
2️⃣ Optimisation Layer (Quantum-Classical Core)
- Formulates the trading or dispatch problem as a QUBO (Quadratic Unconstrained Binary Optimisation).
- Runs classical pre-solvers to reduce dimensionality and then executes a Quantum Approximate Optimisation Algorithm (QAOA) or similar quantum-inspired routine via IBM Qiskit Runtime.
- Returns a ranked list of charge-discharge schedules and market bids.
3️⃣ Execution & Learning Layer
- Implements the chosen strategy through API connections to market platforms or SCADA systems.
- Continuously evaluates realised vs. expected profits and battery health to refine subsequent decisions.
[Visual Placeholder: Architecture diagram — Forecast → QUBO Optimisation → Execution Feedback Loop]
This layered structure ensures commercial value today (running on classical cloud with quantum accelerators) while building future readiness for full-scale quantum hardware.
4. Demonstrated Performance — From Greek Data to European Markets
Quantum Energy has validated its prototype using real market data from the Greek Day-Ahead and Intraday markets (HEnEx) combined with ENTSO-E European datasets.
Key outcomes from pilot simulations:
- Revenue uplift of 6–12% vs. deterministic rule-based strategies.
- Improved SoC stability and reduced battery degradation.
- Faster computation of near-optimal trading actions under uncertainty.
- Scalable framework for portfolios of 10+ batteries or mixed asset classes.
These results represent a Technology Readiness Level (TRL) 7 system — a fully functional prototype validated in operational environments.
[Visual Placeholder: Graph comparing rule-based vs. Quantum Energy optimiser profits over 24h trading horizon]
5. Europe’s Strategic Opportunity
Europe has made massive investments in both quantum technology and the green transition — yet the two domains remain largely separate.
- The EU Quantum Flagship invests over €1 billion in hardware, software, and talent.
- Horizon Europe allocates billions for smart grids, flexibility markets, and AI-energy research.
- National Recovery Plans (RRF) fund large-scale battery storage and RES integration projects.
However, very few initiatives actively bridge these two streams.
That is where Quantum Energy positions itself:
as the applied link between quantum innovation and real-world clean-tech markets.
By deploying hybrid algorithms within today’s market systems, Europe can achieve both short-term economic gains and long-term technological sovereignty.
6. Application Domains Beyond Battery Optimisation
While energy storage is the initial focus, the same quantum-classical optimisation principles can extend to multiple sectors critical for the net-zero transition:
| Domain | Challenge | Quantum Energy Approach |
|---|---|---|
| Hydrogen logistics | Optimise production, compression, transport, and offtake scheduling | Multi-layer optimisation of demand, storage, and pipeline flow |
| EV charging networks | Minimise congestion and maximise profitability under dynamic tariffs | Real-time optimisation of charger dispatch and pricing |
| Microgrids and islands | Balance renewable variability with storage and diesel backup | Hybrid QUBO scheduling for reliability and cost |
| Energy trading portfolios | Manage large asset portfolios across markets | Quantum-enhanced portfolio optimisation and bidding |
[Visual Placeholder: Multi-sector chart showing Quantum Energy algorithm modules reused across sectors]
These extensions make Quantum Energy not a product, but a platform technology — capable of serving multiple verticals through the same optimisation core.
7. Integration Path and Deployment Model
Quantum Energy is designed as a cloud-native SaaS platform, deployable within existing energy management systems (EMS), virtual power plants (VPPs), or trading desks.
Integration features:
- REST and MQTT APIs for price feeds, market bids, and SCADA signals.
- Modular architecture allowing third-party data integration.
- Compatibility with both classical solvers (CVXPY, CBC, Gurobi) and quantum backends (IBM, D-Wave, AWS Braket).
- Optional on-premise installation for grid operators or defence applications.
Business model:
- SaaS licensing per MW of optimised capacity or per API call volume.
- Partnership model with BESS operators and aggregators sharing performance-based revenue.
- Joint R&D with universities and quantum hardware vendors.
[Visual Placeholder: Deployment model diagram – Cloud platform connected to BESS, trader, market API]
8. From Hybrid to Full Quantum — The Road Ahead
Quantum hardware is evolving rapidly. Within the next 3–5 years, machines with hundreds to thousands of error-corrected qubits will become available, enabling optimisations impossible on classical computers.
Quantum Energy’s roadmap aligns with this timeline:
| Phase | Capability | Hardware Status | Target Year |
|---|---|---|---|
| Today (TRL-7) | Hybrid quantum-classical optimisation running on IBM simulators and cloud backends | 100-qubit noisy devices | 2025 |
| Near-term (TRL-8) | Pilot integrations with real BESS assets (5–50 MW scale) | 200-500 qubits | 2026–2027 |
| Full-quantum (TRL-9) | End-to-end optimisation on fault-tolerant systems | 1,000+ qubits | 2028–2030 |
This roadmap ensures that every year of commercial deployment also serves as a quantum-readiness investment, positioning Europe’s energy companies ahead of global competition.
9. The European Imperative — From Innovation to Sovereignty
Europe’s competitiveness in the coming decades will hinge on its ability to integrate green energy, digital intelligence, and quantum technology into a coherent industrial strategy.
If quantum optimisation for energy remains dominated by non-European actors, Europe risks depending on external algorithms to run its own grid — a new form of digital dependency.
By contrast, Quantum Energy represents technological sovereignty:
- Developed in Europe, using European data and standards.
- Compatible with EU digital-sovereignty frameworks (GAIA-X, Data Spaces).
- Ready for partnerships with European hardware players (Atos, IQM, Pasqal).
- Contributing to EU goals of open strategic autonomy and climate neutrality.
10. Conclusion — From Potential to Profit, from Research to Reality
Quantum Energy proves that the quantum advantage is not a distant dream.
It begins here, in the real markets of Europe, where complexity demands new forms of intelligence.
By fusing classical algorithms, AI, and quantum computing, Quantum Energy delivers:
- Higher profitability for energy storage and trading.
- Better utilisation of renewable assets.
- Strategic independence for Europe’s clean-tech future.
The energy transition is not just about new hardware — it’s about new intelligence.
Quantum Energy provides that intelligence, turning uncertainty into opportunity and data into decisive action.
About Quantum Energy
Quantum Energy is a European deep-tech startup developing a hybrid quantum-classical optimisation platform for energy markets.
Its proprietary algorithms have reached TRL 7, validated on real datasets from the Greek Energy Exchange (HEnEx) and ENTSO-E.
Quantum Energy operates within the THEA incubator of the Athens Chamber of Commerce, collaborating with IBM Quantum and European research partners.
For collaboration, investment, or pilot inquiries:
📩 info@quantum-energy.eu
🌐 www.quantum-energy.eu