Article
Quantum Science & Technology
J. A. Montanez-Barrera, R. T. Holladay, G. P. Beretta, Michael R. von Spakovsky
Summary: This paper presents a general method for generating randomly perturbed density operators under different sets of constraints. It examines the sensitivity of various entanglement measures to the perturbation magnitude and simulates the results of real quantum devices.
QUANTUM INFORMATION PROCESSING
(2022)
Article
Physics, Multidisciplinary
Muhammad Waseem Hafiz, Seong Oun Hwang
Summary: The remarkable advancements in quantum information theory in the past decade have expanded the potential for simulating superposition states, enabling exponential speedup of quantum algorithms compared to classical ones. Consequently, conventional and modern cryptographic standards are vulnerable to attacks from Shor's and Grover's algorithms on quantum computers. By encoding classical data into small quantum states and leveraging quantum-assisted classical computations, the improved technology offers superior levels of data protection. Addressing frequent data breaches and stricter privacy regulations, a hybrid quantum-classical model is proposed to transform classical data into unclonable states and demonstrate perfect state transfer in experiments. Additionally, an arbitrary quantum signature scheme is introduced to authenticate users and retrieve classical data without establishing entangled states, reducing implementation complexity. The probabilistic model confirms that the quantum-assisted classical framework significantly enhances the performance and security of digital data, paving the way for real-world applications.
FRONTIERS OF PHYSICS
(2023)
Article
Physics, Multidisciplinary
Bartosz Regula
Summary: This paper presents a new resource monotone that can rule out all transformations, probabilistic or deterministic, between states in any quantum resource theory. The results obtained from this approach provide significant improvements for probabilistic distillation protocols, allowing for better error and overhead bounds. The monotone also serves as a necessary and sufficient condition for state convertibility.
PHYSICAL REVIEW LETTERS
(2022)
Article
Quantum Science & Technology
Bartosz Regula
Summary: In this paper, we develop two general approaches for characterising the manipulation of quantum states using probabilistic protocols. We provide a necessary condition for the existence of a physical transformation between quantum states using a recently introduced resource monotone based on the Hilbert projective metric. We also introduce a method for bounding achievable probabilities in resource transformations under resource-non-generating maps through convex optimisation problems. These approaches are useful in studying quantum entanglement distillation.
Article
Physics, Multidisciplinary
Ermanno Pinotti, Stefano Longhi
Summary: A quantum particle constrained between two high potential barriers can exhibit quasi-bound states. The decay of the wave function in such states can be accelerated by additional lateral barriers, contrary to intuition. This acceleration is due to resonant tunneling effects and results in deviations from exponential decay.
Article
Physics, Multidisciplinary
Lewis Wooltorton, Peter Brown, Roger Colbeck
Summary: Two parties sharing entangled quantum systems can generate nonlocal correlations, which can be used for device-independent random number generation, and the upper bound of certifiable randomness can be quantified using the CHSH value.
PHYSICAL REVIEW LETTERS
(2022)
Article
Materials Science, Multidisciplinary
Tomas Lothman, Christopher Triola, Jorge Cayao, Annica M. Black-Schaffer
Summary: The EPOCH method is an efficient computational method for calculating time-dependent equilibrium Green's functions in large inhomogeneous systems. It generalizes from quantum chemistry methods and incorporates Fermi-Dirac statistics, with computational cost scaling linearly with system degrees of freedom.
Article
Optics
Ludovico Lami, Bartosz Regula, Ryuji Takagi, Giovanni Ferrari
Summary: Resource theories provide a framework for characterizing properties of physical systems, with a universal resource quantifier based on robustness. It can be used to quantify resources in general probabilistic theories and is computed through convex conic optimization problems. The robustness acts as a faithful and strongly monotonic measure in resource theories described by convex and closed sets of free states, and has applications in various physical resources such as optical nonclassicality, entanglement, non-Gaussianity, and coherence.
Article
Quantum Science & Technology
Farid Shahandeh
Summary: The concept of generalized contextuality studies the inability to explain measurement statistics using a context-independent probabilistic and ontological model, instead looking at frameworks such as general probabilistic theories (GPTs) to model these statistics. It is found that GPTs satisfying the no-restriction hypothesis are ontologically noncontextual only when they are simplicial, while GPTs violating this hypothesis can be considered as subtheories of GPTs satisfying the hypothesis. Subtheories are ontologically noncontextual if they are subtheories of simplicial GPTs of the same dimensionality.
Article
Multidisciplinary Sciences
Kyung Seok Woo, Jaehyun Kim, Janguk Han, Woohyun Kim, Yoon Ho Jang, Cheol Seong Hwang
Summary: As the field of big data continues to expand, a computing scheme that can solve complex tasks is necessary. Probabilistic computing, which uses probabilistic bits to efficiently handle problems, is proposed in this study. The theoretical background of the computing system is introduced, and a network of probabilistic bits is formed using a memristor-based threshold switching behavior. The memristor-based probabilistic computing enables all 16 Boolean logic operations, showing potential for complex operations.
NATURE COMMUNICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Ke Su, Hang Su, Chongxuan Li, Jun Zhu, Bo Zhang
Summary: Neural-symbolic models combine symbolic program execution and deep representation learning for complex visual reasoning tasks. This article proposes a method to incorporate domain knowledge into the learning process of probabilistic neural-symbolic models, which effectively regulates the posterior probability of the structure. Experimental results demonstrate that this method achieves state-of-the-art performance on abstract reasoning datasets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Quantum Science & Technology
Robert Starek, Michal Micuda, Michal Kolar, Radim Filip, Jaromir Fiurasek
Summary: In this study, conditional enhancement of overall coherence of quantum states was investigated through probabilistic quantum operations that utilize a quantum filter diagonal in the basis of incoherent states. Optimal filters were identified to maximize output coherence for a given probability of successful filtering. Through a proof-of-principle experiment with linear optics, the performance of the studied quantum filters was verified and optimal quantum coherence enhancement by quantum filtering was observed.
QUANTUM SCIENCE AND TECHNOLOGY
(2021)
Review
Physics, Multidisciplinary
Martin Plavala
Summary: This article introduces the framework of General Probabilistic Theories (GPTs), including basic concepts and elements, and proves several important results. The review aims to provide readers with a consistent introduction to GPTs.
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
(2023)
Article
Multidisciplinary Sciences
Doan Cong Le, Krisana Chinnasarn, Jirapa Chansangrat, Nattawut Keeratibharat, Paramate Horkaew
Summary: A semi-automatic liver segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division was presented in this paper, requiring minimal human interactions. The method consists of three main stages and aims to improve the accuracy and reliability of asymptomatic liver segmentation.
SCIENTIFIC REPORTS
(2021)
Article
Optics
Atithi Acharya, Siddhartha Saha, Anirvan M. Sengupta
Summary: In this paper, a novel method is introduced to predict target functions of unknown quantum states using shadow tomography protocols with high confidence. By utilizing positive operator-valued measurements, k-bit correlation functions for quantum states can be reliably computed. This approach offers a simpler and more efficient alternative compared to conventional quantum state tomography methods.
