Linear programming is a key technique for analysis and verification of numerical properties in programs, neural networks, etc. In particular, in program analysis based on abstract interpretation, many numerical abstra...
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Boson sampling is a promising candidate for demonstrating quantum supremacy. The validation that involves judging whether a quantum setup outputs photons following the boson sampling model is an essential task in the ...
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Boson sampling is a promising candidate for demonstrating quantum supremacy. The validation that involves judging whether a quantum setup outputs photons following the boson sampling model is an essential task in the experiments. However, the current validation methods may result in an incorrect conclusion being reached in realistic experiments, in which no ideally identical photons exist. Accordingly, this study proposes a slope-based approach, which is an extended Bayesian validation, to model the degree of photon indistinguishability. Through numerical simulations and performance evaluations, we demonstrate that the proposed approach can correctly validate boson sampling against the distribution of classical particles. In addition to offering a useful approach for validation, our research indicates that physicists should pay more attention to the quality of photon indistinguishability in boson sampling experiments.
The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population, especially in relation to falls. While falls can lead to significant cognitive impairments, timel...
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This article introduces a comprehensive approach for designing and analyzing signal integrity in heterogeneous integrated systems that incorporate neuromorphic Darwin chips. The proposed integrated system architecture...
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We propose a high-rate scheme for discretely-modulated continuous-variable quantum key distribution (DM CVQKD) using quantum machine learning technologies, which divides the whole CVQKD system into three parts, i.e., ...
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The Kyber algorithm is one of the standardization of Post Quantum Cryptography project proposed by NIST to withstand quantum attacks. In order to increase throughput and reduce the execution time limited by the high c...
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Resistive Random Access Memory (RRAM) is a promising technology for efficient neural computing systems with low power, non-volatile, and good compatibility with CMOS. The RRAM based crossbar is usually employed to acc...
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In the existing reinforcement learning (RL)-based neural architecture search (NAS) methods for a generative adversarial network (GAN), both the generator and the discriminator architecture are usually treated as the s...
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We consider the quantum memory assisted quantum state verification task, where an adversary prepares independent multipartite entangled states and sends them to the local verifiers, who then store several copies in th...
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We consider the quantum memory assisted quantum state verification task, where an adversary prepares independent multipartite entangled states and sends them to the local verifiers, who then store several copies in the quantum memory and measure them collectively to make a decision. We establish an exact analytic formula for optimizing two-copy state verification, where the verifiers store two copies, and give a globally optimal two-copy strategy for multiqubit graph states involving only Bell measurements. When the verifiers can store arbitrarily many copies, we present a dimension expansion technique that designs efficient verification strategies for this case, showcasing its application to efficiently verifying Greenberger-Horne-Zeilinger (GHZ)-like states. These strategies become increasingly advantageous with growing memory resources, ultimately approaching the theoretical limit of efficiency. Our findings demonstrate that quantum memories enhance state verification efficiency, shedding light on error-resistant strategies and practical applications of large-scale quantum memory assisted verification.
While Signed Distance Fields (SDF) are well-established for modeling watertight surfaces, Unsigned Distance Fields (UDF) broaden the scope to include open surfaces and models with complex inner structures. Despite the...
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