Quantum Computing Based Edge Intelligence for Enhancing the Efficiency and Scalability of IoT Applications
Bibliographic record
Abstract
Efficient energy management in smart homes has become increasingly critical due to the proliferation of IoT-enabled appliances, renewable energy integration, and dynamic electricity pricing. Traditional optimization approaches often struggle to balance dynamic household demand, renewable generation, and battery storage in real-time. This study proposes a Quantum Edge Intelligence (QEI) framework for smart grid energy optimization, integrating LSTM-based load forecasting with quantum-inspired combinatorial optimization and edge-based decision-making to improve efficiency, scalability, and resilience. The framework operates in three stages: First, historical household energy consumption data are preprocessed and categorized into flexible and inflexible loads to model real-world usage patterns. Second, a Long Short-Term Memory (LSTM) network generates short-term forecasts of household energy demand, capturing temporal dependencies in consumption patterns. Third, a quantum-inspired optimization module, based on the Quantum Approximate Optimization Algorithm (QAOA), schedules flexible appliances, battery operations, and renewable integration to minimize electricity costs, reduce peak demand, and enhance grid stability. Edge intelligence ensures local computation at simulated nodes, enabling faster and scalable decision-making across multiple households within the smart grid. Evaluation using the UCI Household Power Consumption dataset demonstrates that the proposed framework reduces electricity costs by up to 30%, lowers peak demand by 20%, and improves load-shifting efficiency compared to classical heuristics and Mixed Integer Linear Programming (MILP) based methods. The convergence of these technologies in QEI promises to overcome the existing limitations of IoT frameworks and set a new standard for the future of IoT applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".