Recent advances in image editing, driven by image diffusion models, have shown remarkable progress. However, significant challenges remain, as these models often struggle to follow complex edit instructions accurately and frequently compromise fidelity by altering key elements of the original image. Simultaneously, video generation has made remarkable strides, with models that effectively function as consistent and continuous world simulators. In this paper, we propose merging these two fields by utilizing image-to-video models for image editing. We reformulate image editing as a temporal process, using pretrained video models to create smooth transitions from the original image to the desired edit. This approach traverses the image manifold continuously, ensuring consistent edits while preserving the original image’s key aspects. Our approach achieves stateof-the-art results on text-based image editing, demonstrating significant improvements in both edit accuracy and image preservation. Visit our project page.

CoordFlow is a pixel-wise Implicit Neural Representation (INR) framework for video compression, achieving state-of-the-art results among pixel-wise methods and rivaling both frame-wise and classic techniques. By segmenting videos into layers, each represented by specialized neural networks with built-in motion compensation, CoordFlow boosts performance and enables additional practical video tasks.

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.

The rapid evolution of quantum hardware is propelling quantum computing to new frontiers. Nonetheless, the potential of natural language processing in the quantum paradigm (QNLP) is yet to be explored, including for Noisy Intermediate-Scale Quantum (NISQ) machines. To explore the QNLP frontier, we introduce LEXIQL, a novel noise-aware QNLP technique for text classification on NISQ quantum machines. LEXIQL employs an incremental data injection approach to process textual data in a quantum circuit. It also develops new and effective training methods, such as leveraging a diverse mix of expressible and shallow quantum circuits for the QNLP task of text classification. Our extensive evaluation using Yelp, IMDB, and Amazon datasets (along with synthetic QLNP datasets) demonstrates the effectiveness of LEXIQL’s noise-aware design in both ideal and noisy environments.

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.

Multiple technologies for realizing quantum computing are currently under development. Neutral atom quantum computing is one such promising technology; it offers advantages such as the ability to perform long-distance interactions and gates consisting of more than two qubits. A particular advantage it provides is the flexibility to arrange the qubits in different topologies by customizing atom layouts. We design Graphine, which, to the best of our knowledge, is the first technique to leverage this flexibility to design application-specific topologies for different quantum algorithms based on the structural characteristics of the algorithm circuits. This enables Graphine to improve key performance metrics like the number of gates and pulses by up to 56% and the probability of error by up to 42% on average over widely-used topology designs.

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.

Quantum computers can theoretically have significant acceleration over classical computers; but, the near-future era of quantum computing is limited due to small number of qubits that are also error prone. QUILT is a framework for performing multi-class classification task designed to work effectively on current error-prone quantum computers. QUILT is evaluated with real quantum machines as well as with projected noise levels as quantum machines become more noise free. QUILT demonstrates up to 85% multi-class classification accuracy with the MNIST dataset on a five-qubit system.

Quantum computers can theoretically speed up optimization workloads such as variational machine learning and classification workloads over classical computers. However, in practice, proposed variational algorithms have not been able to run on existing quantum computers for practical-scale problems owing to their error-prone hardware. We propose Optic, a framework to effectively execute quantum binary classification on real noisy intermediate-scale quantum (NISQ) computers.

Compared to widely-used superconducting qubits, neutral-atom quantum computing technology promises potentially better scalability and flexible arrangement of qubits to allow higher operation parallelism and more relaxed cooling requirements. The high performance computing (HPC) and architecture community is beginning to design new solutions to take advantage of neutral-atom quantum architectures and overcome its unique challenges.

When quantum programs are executed on noisy intermediate-scale quantum (NISQ) computers, they experience hardware noise; consequently, the program outputs are often erroneous. To mitigate the adverse effects of hardware noise, it is necessary to understand the effect of hardware noise on the program output and more fundamentally, understand the impact of hardware noise on specific regions within a quantum program. Identifying and optimizing regions that are more noise-sensitive is the key to expanding the capabilities of NISQ computers. Toward achieving that goal, we propose Charter, a novel technique to pinpoint specific gates and regions within a quantum program that are the most affected by the hardware noise and that have the highest impact on the program output. Using Charter's methodology, programmers can obtain a precise understanding of how different components of their code affect the output and optimize those components without the need for non-scalable quantum simulation on classical computers.