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Single and distributed advanced architectures for Quantum reservoir computing for time-series prediction

Problems addressed

Industrial plants and energy operators face unpredicted equipment failures, inefficient materials testing, and inaccurate energy forecasts due to siloed sensor and operational data and limited access to scalable HPC/quantum analytics.

Unique Value Proposition

Combines sensor, operational and energy data with HPC and quantum-enhanced computing workflows to support accurate time-series forecasting.

Validated UC & Key Performance Benchmark

(Where was it tested, and what exact performance improvement or resource reduction did it achieve?)

Tested in: Simulations in HPC systems using QC simulators with active noise profiles

Hard metrics: Grid Load short-term forecasting accuracy >95%

Target Stakeholder Segments

(Who adopts/Hands-on Users)

Industrial Plant Maintenance Engineers, HPC/Quantum Researchers, and Energy Management Operations.

Key Industry / Vertical Markets

(Where it is deployed)

Energy grid management Energy producer supply sectors Pharmaceutical and Chemicals industries

License Model & IP and Commercial Strategy

(Open Source License)

Open Source License: CC BY 4.0 license (methodology and research results)

Commercial Streams: Open access & R&I re-use

Proprietary Strategy:

Availability

(Code Repository link)

Open Source License: CC BY 4.0 license (methodology and research results)

Commercial Streams: Open access & R&I re-use

Proprietary Strategy:

Estimated Time to Market

1 year post-project
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