We address the issue of reducing the resource required to compute information-theoretic quantum correlation measures like quantum discord and quantum work deficit in two qubits and higher dimensional systems. We show that determination of the quantum correlation measure is possible even if we utilize a restricted set of local measurements. We find that the determination allows us to obtain a closed form of quantum discord and quantum work deficit for several classes of states, with a low error. We show that the computational error caused by the constraint over the complete set of local measurements reduces fast with an increase in the size of the restricted set, implying usefulness of constrained optimization, especially with the increase of dimensions. We perform quantitative analysis to investigate how the error scales with the system size, taking into account a set of plausible constructions of the constrained set. Carrying out a comparative study, we show that the resource required to optimize quantum work deficit is usually higher than that required for quantum discord. We also demonstrate that minimization of quantum discord and quantum work deficit is easier in the case of two-qubit mixed states of fixed ranks and with positive partial transpose in comparison to the corresponding states having non-positive partial transpose. Applying the methodology to quantum spin models, we show that the constrained optimization can be used with advantage in analyzing such systems in quantum information-theoretic language. For bound entangled states, we show that the error is significantly low when the measurements correspond to the spin observables along the three Cartesian coordinates, and thereby we obtain expressions of quantum discord and quantum work deficit for these bound entangled states.
Computational and physical complexity of synthesizing random multi-qudit quantum states and unitary operators
Advanced ICT Research Institute, National Institute of Information and Communications Technology（先进ICT研究所，信息通信技术研究所）；Research Institute for Science and Technology, Tokyo University of Science（科学技术研究所，东京理科大学）；Institute for Quantum Information and Matter and Department of Physics, California Institute of Technology（量子信息与物质研究所和物理系，加州理工学院）
Reducing QUBO Complexity in Drone Delivery Through Classical Feasibility Preprocessing
Gran Sasso Science Institute（格兰萨索科学研究所）；VSB – Technical University of Ostrava（俄斯特拉发科技大学）；IT4Innovations National Supercomputing Center（IT4创新国家超级计算中心）；Klaipeda University（克莱佩达大学）；Università dell’Aquila（阿奎拉大学）
Reducing the Computational Cost Scaling of Tensor Network Algorithms via Field-Programmable Gate Array Parallelism
Key Laboratory of Polar Materials and Devices (MOE), School of Physics, East China Normal University（华东师范大学物理学院极材料与器件教育部重点实验室）；Quantum Medical Sensing Laboratory and School of Health Science and Engineering, University of Shanghai for Science and Technology（上海理工大学健康科学与工程学院量子医疗传感实验室）；Electron Microscopy Laboratory, School of Physics, Peking University（北京大学物理学院电子显微镜实验室）；Hefei National Laboratory（合肥国家实验室）
