Quantum deep neural networks modeling for inventory management

Purpose The purpose of this study is to explore the potential for quantum computing to enhance applied machine learning tasks. The goal is to investigate whether quantum computing principles such as superposition, entanglement and quantum parallelism can improve predictive accuracy and efficiency in machine learning models. Design/methodology/approach To examine the potential, this study simulated a 4-qubit quantum processor on an Intel Core i5 8th generation central processing unit (CPU). The proposed model combines quantum principle and deep neural networks architecture involving quantum-forward and classical-backward processes; quantum part is in the forward pass, and the traditional neural network handles the backpropagation during model training. This model was tested using a real dataset to assess its practical applicability to machine learning tasks under real-world constraints. Findings The findings reveal the effectiveness of the quantum-classical hybrid approach which achieves an average area under the curve score of 92%, with a precision of 22.6% and a recall of 25.3%. These results suggest that, while quantum noise and Qubit limitations inherent to Noisy Intermediate-Scale Quantum systems impacted prediction accuracy and model generalizability, the quantum-forward process enhanced the model’s ability to identify true positive cases in a complex dataset. Compared to traditional approaches, this model addresses both the stability and predictive demands of highly imbalanced inventory data, although limitations remain in precision and scalability. Originality/value While many open questions remain about the real advantages of quantum computing in applied field of business, this study contributes to the feasibility of leveraging quantum machine learning for complex decision-making problems in supply chain management.