Predicting energy demand is a critical challenge for power systems. Forecasting methods vary in spatial and temporal resolution, from single appliances to national grids and from sub-hourly to yearly predictions (Debnath and Mourshed, 2018). Classical methods include statistical time-series models (e.g., ARIMA) and regression, while modern approaches heavily rely on machine learning models like NNs and SVMs to capture complex, nonlinear relationships in consumption data (Wei et al., 2019).

QML offers new avenues for improving forecasting accuracy. Nutakki et al. (2024) applied a quantum support vector machine (QSVM) to forecast household energy consumption. This addresses the challenge classical SVMs face with highly complex, nonlinear consumption patterns by leveraging quantum feature spaces to potentially find more effective separating hyperplanes. Their results showed that the QSVM achieved higher accuracy (97.36%) than classical deep learning models like RNN and LSTM.

Safari and Badamchizadeh (2024) introduced “NeuroQuMan,” a QNN-based system to predict energy demand based on user reaction times, which demonstrated superior accuracy over classical benchmarks in simulations. Similarly, hybrid quantum-classical frameworks for demand response in buildings have shown promise. Ajagekar and You (2024) used a VQC within a reinforcement learning (RL) framework to optimize energy use, reporting a 13.6% reduction in energy consumption compared to classical control methods. Another QRL approach by Andrés et al. (2022) demonstrated that a hybrid quantum agent could learn an optimal energy-saving policy for an HVAC system more effectively than a classical neural network agent.

For smart grid management, Arvanitidis et al. (2023) used a variational quantum classifier (VQC) for appliance identification from power consumption data, achieving a 5% accuracy improvement over a classical CNN. The VQC’s ability to map data into a high-dimensional Hilbert space allows it to distinguish subtle signatures that are challenging for classical feature extraction methods.

Energy generation forecasting (EGF) plays a crucial role in managing the variability of renewable sources such as solar and wind, whose intermittent and volatile nature requires accurate forecasting to maintain grid stability and optimize resource use. Direct approaches often rely on time-series analysis or meteorological data from numerical weather prediction (NWP) models, while indirect approaches first predict site-specific weather profiles and then convert these into power output via weather-to-power performance models. Modern forecasting increasingly employs ML techniques—particularly NNs, SVMs, and LSTMs—whose performance can be enhanced by hyperparameter optimization methods such as ACO, genetic algorithms, and PSO (Jallal et al., 2020). Deep architectures, hybrid ML–physical approaches, and physics-informed methods have further improved accuracy and robustness in renewable generation forecasting (Sharadga et al., 2020; Mayer, 2022).

Early QML applications to EGF include Senekane and Taele (2016), who used a QSVM to forecast solar irradiance from Cambridge University weather station data, and Li et al. (2015), who demonstrated a full quantum pipeline involving state preparation, matrix inversion, and variational classification. Building on this, Oliveira Santos et al. (2024) compared QNNs using angle encoding and a two-local ansatz against classical models on the Folsom, California dataset, finding that while XGBoost excelled in short-term horizons, QNNs were more effective for longer-term predictions. Extending to recurrent architectures, Yu et al. (2023) embedded VQCs into LSTM gates to create a QLSTM that outperformed SARIMA, CNN, RNN, GRU, and classical LSTM across five Chinese solar observatories.

Hybrid convolutional designs have also been explored, with Hong et al. (2024) developing an HQCNN optimized via Bayesian methods on Taiwanese irradiance data, demonstrating improved loss metrics, robustness to sensor faults, and superior speed using CUDA Quantum. Similarly, Sushmit and Mahbubul (2023) integrated PQC-based quantum layers into deep FFNs trained on NASA POWER data, showing that a two-quantum-layer hybrid achieved the best balance of accuracy and efficiency, while pure PQC models lagged behind. Sagingalieva et al. (2023) extended the hybrid concept to temporal models, proposing HQNN, HQLSTM, and HQSeq2Seq architectures for PV power forecasting, achieving 16%–41% accuracy gains over MLP and LSTM with fewer parameters and stronger performance on limited datasets. In another comparative study, Khan et al. (2024) found that QLSTM models trained on Indian and NREL datasets converged faster, exhibited more stable learning, and achieved higher accuracy than classical LSTM, although at the cost of longer evaluation times.

More complex hybrid pipelines have been proposed, such as the VAE-GWO-VQC-GRU framework of Zhu et al. (2024), which augmented and clustered Alice Springs PV data by weather condition before prediction, significantly outperforming GRU and VQC-GRU baselines. Beyond forecasting, Uehara et al. (2022) applied a hybrid QNN to optimize PV array topology under partial shading, achieving 85.12% classification accuracy for optimal configurations. In wind forecasting, Hong et al. (2023) combined LSTM with QNN and used the Taguchi method for systematic hyperparameter tuning, enhancing robustness across seasonal variations, while Hangun et al. (2024a) employed amplitude-encoded QNNs as feature extractors feeding into SVR for offshore wind farms, improving MAE and R 2 over classical baselines. Expanding to broader climate series, Hsu et al. (2025) embedded quantum kernels into LSTM transformations to create QK-LSTM, achieving higher accuracy with fewer parameters. Finally, Karniadakis et al. (2021) and Kashinath et al. (2021) reviewed physics-informed ML for climate and weather modeling, with Jaderberg et al. (2024) demonstrated that a quantum PINN could solve the barotropic vorticity equation, thus illustrating the potential for physically grounded quantum models in EGF.

Transmission systems face new challenges from renewable integration, the rise of prosumers, and the reduced inertia of non-synchronous generation, making fault detection, diagnosis, and stability assessment increasingly critical. Traditional FDD approaches, rule-based, model-based, and more recently, ML- and DL-based, struggle with the growing data volume and complexity, opening opportunities for QML to improve detection accuracy, computational speed, robustness, and predictive maintenance (Correa-Jullian et al., 2022).

For fault diagnosis, Ajagekar and You (2021) proposed a hybrid QC-trained CRBM on the IEEE 30-bus system, combining quantum generative training with discriminative fine-tuning to match or exceed ANN and DT performance while halving classification latency. In PV fault detection, Uehara et al. (2021) compared QNNs with different feature maps and ansatz choices on NREL datasets, showing competitive accuracy and reduced training epochs. Extending to wind turbine pitch systems, Correa-Jullian et al. (2022) benchmarked Q-SVMs against classical SVM, RF, and k-NN, finding that angular encoding Q-SVMs outperform RF and k-NN in certain feature settings when combined with PCA or AE-based reduction.

In transient stability assessment, Zhou and Zhang (2023) introduced qTSA with VQCs to separate stable and unstable states in SMIB, two-area, and NPCC systems, maintaining >95% accuracy on IBM hardware despite noise. Similarly, Sabadra et al. (2024) employed quantum-embedded kernels optimized by aligning the target kernel, achieving up to 98. 4% precision in SMIB. Hangun et al. (2024b) found that classical SVM outperformed VQC on a small smart grid, highlighting the need for careful feature map and ansatz tuning. Chen and Li (2024) demonstrated that combining QPCA, quantum inner products, and VQA could yield 98.7% accuracy in microgrid TSA with fewer measurements.

Distributed approaches have been explored by Yu et al. (2024), whose Q-dTSA used HELD-based federated QNNs to preserve local data privacy while matching DNN accuracy with 75% fewer parameters and faster convergence. The robustness to adversarial manipulation was addressed by Yu and Zhou (2024), who developed ReHELD circuits that improved classification under data poisoning and deletion by up to 18%. Optimization-based methods also feature, with Fei et al. (2024) formulating combinatorial fault diagnosis as a QAOA problem, introducing symmetric equivalent decomposition for efficient multi-z-rotation gates. For time anomaly detection in PMU streams, Jafari et al. (2024) proposed the QVR algorithm, optimized for shallow NISQ circuits and integrated with high-speed classical computing.

In power quality analysis, Wang et al. (2024) applied QSVMs with quantum feature mapping and kernel computation, achieving perfect disturbance detection in some datasets and maintaining over 87% accuracy under noise. Finally, Gbashie et al. (2024) integrated VQCs into CNN architectures for wind turbine gearbox fault detection, surpassing 99.2% accuracy with faster convergence when using the Adam optimizer.

Carbon markets are a key instrument for mitigating climate change; this makes accurate carbon price forecasting and risk estimation essential for investors and policymakers. Other financial-related operations, such as energy trading and scheduling, could also benefit from advanced ML.

Cao et al. (2023) developed an improved quantum long-term memory model (L-QLSTM) to predict carbon prices. By replacing classical gates in an LSTM with VQCs, the model leverages quantum expressivity to capture complex temporal dependencies that may be difficult for classical LSTMs to model efficiently. The L-QLSTM showed performance comparable to that of a classical LSTM but with improved learning stability.

For estimating carbon market risk, Zhou et al. (2024) proposed a framework using a quantum conditional generative adversarial network (QCGAN) to model return distributions and quantum amplitude estimation (QAE) to measure risk. This quantum approach offers a potential quadratic speedup over classical Monte Carlo methods for risk estimation, which are notoriously computationally intensive. The framework demonstrated a significant reduction in computational time and improved accuracy over classical models.

In energy trading, Kumar et al. (2023) designed a system combining blockchain with quantum reinforcement learning (QRL) to optimize P2P energy trading for EVs. The QRL agent learned an optimal pricing policy faster and more effectively than its classical counterparts.

This dimension reflects how prepared a use case is for real-world deployment, looking at its ability to scale, fit market conditions, and be implemented with minimal friction. Scalability concerns whether the use case can handle growing demand, larger user bases, and evolving needs, paying attention to its capacity for user growth and flexibility to integrate with new technologies or adapt to changing business contexts. Market compatibility examines how well the current environment, society, stakeholders, technology, business structures, and ecosystems can support adoption, considering both customer readiness and the availability of the necessary technological infrastructure. Implementation feasibility captures the ease of integrating the use case into existing systems and processes, focusing on the complexity of the required integrations and the ability to meet regulatory requirements without excessive effort.

This dimension measures the value a use case provides to the industry in terms of efficiency and long-term advantage. Impact on efficiency addresses the potential for cost reduction, return on investment, and productivity gains, showing whether the benefits justify the investment and improve operations. Criticality of the problem assesses the urgency and importance of the issue to be addressed, taking into account its severity, the level of market demand for a solution, and the sustainability of the impact—environmental, social and economic. Margin for further improvement looks at how much room remains for both vertical and horizontal development, taking into account the current stage of maturity and any performance gaps that signal opportunities for improvement.

Each use case was scored from 1 (weak) to 4 (high) according to the AMIM criteria (Table 3). The aggregated scores position each use case in one of four quadrants (Figure 6): transformative leaders, research-heavy innovators, emerging niches, and experimental niches. Our analysis identifies power stability assessment (ID 16), fault diagnosis (ID 12), and wind speed forecasting (ID 7) as transformative leaders. These use cases are critical for the energy transmission and generation sectors, especially with growing grid complexity, and show both high market readiness and significant potential benefit from QML solutions. The transmission value chain stands out for hosting the most valuable use cases. In contrast, financial applications currently fall more into the experimental category. A key finding is that, while the technological feasibility of QML is still low, the overall market readiness is often decent due to the strong demand of the energy industry for innovative and scalable solutions (Figure 4).

It is important to acknowledge that AMIM is a novel framework proposed in this study. As such, it has not yet undergone external validation. Its current application relies on our assessment based on the literature reviewed. To strengthen its credibility and promote broader adoption, future research should focus on validating the framework. Potential validation methods include the following.

• Expert review: engaging a panel of industry and academic experts from both the energy and quantum computing sectors to review and refine the AMIM dimensions and KPIs.

This validation process would enhance the robustness of AMIM and solidify its value as a tool for strategic innovation management.