Towards Quantum Image Generation on Single Qubit using Quantum Information Bottleneck

Amidst the rapidly evolving landscape of information technology, the convergence of quantum computing and machine learning—referred to as quantum machine learning—offers promising potential to enhance classical algorithms. However, significant challenges remain in both hardware and software implementation during the Noisy Intermediate-Scale Quantum (NISQ) era, including imperfect qubits, architectural constraints, and high noise levels. In response to these obstacles, this research introduces a novel solution: Quantum Convolutional Variational Autoencoders (QCVAE), designed to operate with only a single qubit. This innovative approach efficiently utilizes a single qubit to manage large-scale data, making it particularly well-suited for quantum computers with limited resources. Simulation results demonstrate the robustness of QCVAE in handling image data, and its deployment on a real quantum computer showcases the model’s practical viability. Additionally, the proposed approach leverages the information bottleneck principle to optimize quantum embeddings, effectively mitigating the impact of prevalent quantum noise. By addressing these core challenges, QCVAE presents a compelling solution for advancing quantum computing applications within the constraints of current NISQ technology.