Recent quantum software engineering efforts have made significant progress in testing and debugging quantum algorithms -however, providing confidentiality and privacy to quantum software in the cloud remains an unexplored critical area.OpaQue is the first solution to obfuscate quantum software and output to prevent the leaking of confidential information over the cloud.OpaQue implements a lightweight, scalable, and effective solution based on the unique principles of quantum computing to achieve this task.

Quantum machine learning has shown early promise and potential for productivity improvements for machine learning classification tasks, but has not been systematically explored on photonics quantum computing platforms. Therefore, this paper presents the design and implementation of ProxiML - a novel quantum machine learning classifier for photonic quantum computing devices with multiple noise-aware design elements for effective model training and inference. Our extensive evaluation on a photonic device (Xanadu's X8 machine) demonstrates the effectiveness of ProxiML machine learning classifier (over 90% accuracy on a real machine for challenging four-class classification tasks), and competitive classification accuracy compared to prior reported machine learning classifier accuracy on other quantum platforms - revealing the previously unexplored potential of Xanadu's X8 machine.

Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs. However, these ef- forts have yet to solve unsupervised similarity detection tasks due to the challenge of porting them to run on quantum com- puters. To overcome this challenge, we propose SLIQ, the first open-sourced work for resource-efficient quantum sim- ilarity detection networks, built with practical and effective quantum learning and variance-reducing algorithms.

Quantum machine learning and vision have come to the fore recently, with hardware advances enabling rapid advancement in the capabilities of quantum machines. Recently, quantum image generation has been explored with many potential advantages over non-quantum techniques; however, previous techniques have suffered from poor quality and robustness. To address these problems, we introduce MosaiQ a high-quality quantum image generation GAN framework that can be executed on today’s Near-term Intermediate Scale Quantum (NISQ) computers.

Among the various types of quantum computers, photonic quantum computers have shown great potential due to their high degree of scalability. However, the development of photonic quantum computers is still in its infancy, and the characterization of their performance is of critical importance to guide further improvements. In this work, we present the first characterization and insights derived from Xanadu's X8 photonic quantum computer. Our work represents an important step toward the development of practical and scalable photonic quantum computers.