Despite the amount of omics data generated in the last decade, the low data regime remains a significant challenge in healthcare, particularly in clinical trials and the study of rare diseases. In this study, we explore the application of quantum machine learning techniques to address the challenges posed by low data availability in healthcare. Leveraging the power of quantum computing, we focus on efficient selection of relevant features and embedding, reducing the model complexity (and number of qubit needed) to enhance the performance even with limited data. In our case study, we used RNA data from epilepsy patients demonstrating how the predictive performance of QML models varies significantly depending on the feature selection approach employed, with noticeable improvements over classical counter part when domain knowledge is integrated to guide the selection process.
Currently, quantum processors offered by quantum service providers are significantly underutilized, resulting in high execution costs and limited scalability. Various approaches have explored parallelization and circuit scheduling techniques to improve resource utilization. While these methods can significantly reduce both cost and execution times, they often have an impact on the results' noise. Addressing this challenge, this emerging study proposes a technique called Quantum Island Mapping, which enables the execution of multiple quantum circuits by separating them into “islands” within the qubit topology of a processor. The goal is to schedule multiple circuits into a single task to reduce execution costs and queuing, while maintaining a controlled separation of qubits between circuits to minimize interferences. To achieve this, separation distance parameters are defined, specifying the minimum number of qubits between different islands. This allows for configurations ranging from zero separation (maximum processor utilization) to maximum separation (single-circuit execution). The technique has been validated through experiments on a real quantum computer using well-known quantum algorithms, achieving optimal results.
Quantum Computing is a rapidly growing field, where room-temperature control electronics have become a major bottleneck for scalability, particularly in academic environments, where open control stacks and cost-effectiveness are essential. In this paper, we present initial results of our approach to scaling multiple instances of the Quantum Interface Controller (QiController), a control and readout electronic stack developed at the Karlsruhe Institute of Technology. We outline key concepts such as low-latency communication and clock synchronization required for a scalable architecture leveraging an ATCA-based platform, validate these concepts through several demonstrator setups, and present promising preliminary results. We also discuss the remaining challenges in developing a fully scalable system.
This article introduces a novel algebraic-logical formalism, called synalgebra, for describing, simulating, and reasoning about photonic quantum circuits. Existing approaches are typically limited to either descriptive languages or circuit simulators. In contrast, synalgebra employs the new concept of intergraphs as a core data structure for equational reasoning about photonic circuit architectures. Specifically, the article presents preliminary simulations of the variational quantum eigensolver (VQE) algorithm for up to 100 qubits, demonstrating how synalgebra can support higher-level abstractions of VQE as well as hardware optimization of photonic waveguides.
