Abstract: Noisy intermediate-scale quantum (NISQ) devices with limited qubit count and connectivity limit the scale of quantum circuits that can be executed. Circuit cutting methods simulate larger quantum computers by decomposing large circuits into small subcircuits that can be executed on NISQ hardware. Existing circuit cutting methods only consider the qubit count of the target […]
Abstract: Motivated by recent progress in quantum hardware and algorithms, researchers have developed quantum heuristics for optimization problems, aiming for advantages over classical methods. To date, quantum hardware is still error-prone and limited in size such that quantum heuristics cannot be scaled to relevant problem sizes and are often outperformed by their classical counterparts. Moreover, […]
Abstract: Linear regression is fundamental to statistical analysis and machine learning, but its application to large-scale datasets necessitates distributed computing. The problem also arises in quantum computing, where handling extensive data requires distributed approaches. This paper investigates distributed linear regression in the quantum coordinator model. Building upon the distributed quantum least squares protocol developed by […]
Abstract: Quantum key distribution (QKD) supports secret key exchange to enable data exchange with guaranteed security but remains vulnerable to key exchange interruption caused by physical-layer threats, such as high-power jamming attacks. In particular, in a fiber-based QKD network equipped with optical switching capabilities, a high-power jamming signal injected into a single link can be […]
Abstract: Quantum computing is progressing at a rapid pace, although still constrained by the limitations of noisy intermediate-scale quantum (NISQ) devices, such as restricted qubit counts and high susceptibility to noise. To address these constraints, researchers have begun adapting classical software engineering principles to the quantum realm, giving rise to the field of quantum software […]
Abstract: Computed tomography (CT) is a non-destructive technique for observing internal images and has proven highly valuable in medical diagnostics. Recent advances in quantum computing have begun to influence tomographic reconstruction techniques. The quantum tomographic reconstruction algorithm is less affected by artifacts or noise than classical algorithms by using the square function of the difference […]
Abstract: Mixed-integer linear programs are widely used to model optimization problems involving both discrete and continuous variables, but remain computationally challenging due to the combinatorial complexity. To exploit the potential advantages of quantum computing in tackling the combinatorial optimization part, recent efforts have explored hybrid quantum-classical Benders decomposition frameworks, which delegate the discrete master problem […]
Abstract: Error mitigation techniques are crucial to achieving near-term quantum advantage. Classical post-processing of quantum computation outcomes is a popular approach for error mitigation, which includes methods such as Zero Noise Extrapolation, Virtual Distillation, and learning-based error mitigation. However, these techniques have limitations due to the propagation of uncertainty resulting from the finite shot number […]
Abstract: Quantum walks are a potent technique for building quantum algorithms. This paper examines the quantum walk search algorithm on complete multipartite graphs with multiple marked vertices, which has not been explored before. We employ the coined quantum walk model and achieve quadratic speedup with a constant probability of finding a marked vertex in two […]
Abstract: Quantum annealing is a quantum algorithm to solve combinatorial optimization problems. In the current quantum annealing devices, the dynamic range of the input Ising Hamiltonian, defined as the ratio of the largest to the smallest coefficient, significantly affects the quality of the output solution due to limited hardware precision. Several methods have been proposed […]