The phenomenal progress of quantuminformation theory over the last decade has substantially broadened the potential to simulate the superposition of states for exponential speedup of quantumalgorithms over their cla...
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The phenomenal progress of quantuminformation theory over the last decade has substantially broadened the potential to simulate the superposition of states for exponential speedup of quantumalgorithms over their classical ***,the conventional and modern cryptographic standards(encryption and authentication)are susceptible to Shor’s and Grover’s algorithms on quantum *** significant improvement in technology permits consummate levels of data protection by encoding classical data into small quantum states that can only be utilized once by leveraging the capabilities of quantum-assisted classical *** the frequent data breaches and increasingly stringent privacy legislation,we introduce a hybrid quantum-classical model to transform classical data into unclonable states,and we experimentally demonstrate perfect state transfer to exemplify the classical *** alleviate implementation complexity,we propose an arbitrary quantum signature scheme that does not require the establishment of entangled states to authenticate users in order to transmit and receive arbitrated states to retrieve classical *** consequences of the probabilistic model indicate that the quantum-assisted classical framework substantially enhances the performance and security of digital data,and paves the way toward real-world applications.
Using the recent ability of quantum computers to initialize quantum states rapidly with high fidelity, we use a function operating on a discrete set to create a simple class of quantum channels. Fixed points and perio...
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Using the recent ability of quantum computers to initialize quantum states rapidly with high fidelity, we use a function operating on a discrete set to create a simple class of quantum channels. Fixed points and periodic orbits, that are present in the function, generate fixed points and periodic orbits in the associated quantum channel. Phenomenology such as periodic doubling is visible in a 6 qubit dephasing channel constructed from a truncated version of the logistic map. Using disjoint subsets, discrete function-generated channels can be constructed that preserve coherence within subspaces. Error correction procedures can be in this class as syndrome detection uses an initialized quantum register. A possible application for function-generated channels is in hybrid classical/quantumalgorithms. We illustrate how these channels can aid in carrying out classical computations involving iteration of non-invertible functions on a quantum computer with the Euclidean algorithm for finding the greatest common divisor of two integers.
We propose a versatile privacy framework for quantum systems, termed quantum pufferfish privacy (QPP). Inspired by classical pufferfish privacy, our formulation generalizes and addresses limitations of quantum differe...
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We propose a versatile privacy framework for quantum systems, termed quantum pufferfish privacy (QPP). Inspired by classical pufferfish privacy, our formulation generalizes and addresses limitations of quantum differential privacy by offering flexibility in specifying private information, feasible measurements, and domain knowledge. We show that QPP can be equivalently formulated in terms of the Datta-Leditzky information spectrum divergence, thus providing the first operational interpretation thereof. We reformulate this divergence as a semi-definite program and derive several properties of it, which are then used to prove convexity, composability, and post-processing of QPP mechanisms. Parameters that guarantee QPP of the depolarization mechanism are also derived. We analyze the privacy-utility tradeoff of general QPP mechanisms and, again, study the depolarization mechanism as an explicit instance. The QPP framework is then applied to privacy auditing for identifying privacy violations via a hypothesis testing pipeline that leverages quantumalgorithms. Connections to quantum fairness and other quantum divergences are also explored and several variants of QPP are examined.
The quantum search operation as dictated in Grover's landmark paper had been a crucial area in the study of quantumalgorithms. It has become a critical component in many quantum cryptography and computation algor...
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The quantum search operation as dictated in Grover's landmark paper had been a crucial area in the study of quantumalgorithms. It has become a critical component in many quantum cryptography and computation algorithms and threatens today's AES security infrastructure. The quadratic speedup provided by Grover's algorithm is hampered severely due to the presence of a realistic environment. Many studies have analyzed the effect of different noises on Grover's search algorithm. However, the efficiency of the algorithm also depends on the connectivity of qubits on realistic quantum hardware. This study evaluated the performance of Grover's algorithm with varying qubit connectivity under the presence of two-qubit depolarizing noise and single-qubit amplitude damping and dephasing noise. Unidirectional and bidirectional variants of nine coupling maps for qubit connectivity were chosen. The analysis has shown that the transpilation efficiency for Grover's algorithm is deeply sensitive to the connectivity and degree of the hardware, which influences the depth of the circuit. This, in turn, has a measurable effect on the performance of the algorithm on a particular hardware. This study also ranks the favorable coupling maps using the decision-making technique of AHP-TOPSIS. The analysis has shown that grid, hex, and modified star are the most favorable hardware connectivity. The unidirectional linear, ring, star, and full-connected are the worst choices.
We develop genetic algorithms for searching quantum circuits, in particular stabilizer quantum error correction codes. quantum codes equivalent to notable examples such as the 5-qubit perfect code, Shor's code and...
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We develop genetic algorithms for searching quantum circuits, in particular stabilizer quantum error correction codes. quantum codes equivalent to notable examples such as the 5-qubit perfect code, Shor's code and the 7-qubit color code are evolved out of initially random quantum circuits. We anticipate evolution as a promising tool in the NISQ era, with applications such as the search for novel topological ordered states, quantum compiling and hardware optimization.
Edge computing aims to address the challenges associated with communicating and transferring large amounts of data generated remotely to a data center in a timely and efficient manner. A central pillar of edge computi...
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Edge computing aims to address the challenges associated with communicating and transferring large amounts of data generated remotely to a data center in a timely and efficient manner. A central pillar of edge computing is local (i.e., at- or near-source) data processing capability so that data transfer to a data center for processing can be minimized. Data compression at the edge is therefore a natural component of edge workflows. We present a survey of data compression algorithms with a focus on edge computing. Not all compression algorithms can accommodate the data type heterogeneity, tight processing and communication time constraints, or energy efficiency requirement characteristics of edge computing. We discuss specific examples of compression algorithms that are being explored in the context of edge computing. We end our review with a brief survey of emerging quantum compression techniques that are of importance in quantuminformationprocessing, including the proposed concept of quantum edge computing.
Image classification is a crucial task in machine learning with widespread practical applications. The existing classical framework for image classification typically utilizes a global pooling operation at the end of ...
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Image classification is a crucial task in machine learning with widespread practical applications. The existing classical framework for image classification typically utilizes a global pooling operation at the end of the network to reduce computational complexity and mitigate overfitting. However, this operation often results in a significant loss of information, which can affect the performance of classification models. To overcome this limitation, we introduce a novel image classification framework that leverages variational quantumalgorithms (VQAs) hybrid approaches combining quantum and classical computing paradigms within quantum machine learning. The major advantage of our framework is the elimination of the need for the global pooling operation at the end of the network. In this way, our approach preserves more discriminative features and fine-grained details in the images, which enhances classification performance. Additionally, employing VQAs enables our framework to have fewer parameters than the classical framework, even in the absence of global pooling, which makes it more advantageous in preventing overfitting. We apply our method to different state-of-the-art image classification models and demonstrate the superiority of the proposed quantum architecture over its classical counterpart through a series of state vector simulation experiments on public datasets. Our experiments show that the proposed quantum framework achieves up to a 9.21% increase in accuracy and up to a 15.79% improvement in F1 score, compared to the classical framework. Additionally, we explore the impact of shot noise on our method through shot-based simulation and find that increasing the number of measurements does not always lead to better results. Selecting an appropriate number of measurements can yield optimal results, even surpassing those obtained from state vector simulation.
The use of quantum entanglement has garnered increasing attention among researchers in recent years due to its wide range of applications, not only revolutionizing the field of informationprocessing but also enhancin...
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The use of quantum entanglement has garnered increasing attention among researchers in recent years due to its wide range of applications, not only revolutionizing the field of informationprocessing but also enhancing quantum-safe communications. Identifying the degree of entanglement present in quantum states is a crucial focus, and designing an algorithm capable of feasibly measuring entanglement is imperative. While theoretical calculations hold high regard, the ease of implementing these algorithms in a laboratory setting is essential to gauge their *** this context, IBM quantum computers stand out as discrete value NISQ (Noisy Intermediate-Scale quantum) platforms These platforms are based on superconducting qubits, providing an opportunity to test our algorithms without the need for extravagant laboratory equipment. This paper proposes an algorithm designed to measure entanglement in a bipartite system. We will execute the algorithm on IBM's 127-qubit backends to compare our calculations with real-world results. Furthermore, we aim to address and mitigate errors inherent in these devices by utilizing local mitigation technique available in the IBM Experiments Python package, aiming for more accurate and reliable outcomes.
Among the various quantum phenomena that contribute to the efficiency of quantum computation compared to classical computation, entanglement plays a pivotal role. To witness and measure entanglement, various approache...
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Among the various quantum phenomena that contribute to the efficiency of quantum computation compared to classical computation, entanglement plays a pivotal role. To witness and measure entanglement, various approaches such as concurrence for bipartite systems and tangle for three-qubit entangled systems have been developed. In this regard, researchers have endeavored to design algorithms capable of directly measuring these parameters to enhance efficiency and feasibility in computation. This topic has garnered significant attention due to its importance in the experimental implementation of desired quantum computations. While theoretical proposals are respected and have generated many unique ideas, experimental implementation can provide invaluable insights. To achieve this goal, IBM quantum platforms, available in cloud form, serve as unique testbeds for scrutinizing theoretical algorithms performed on these devices, which simulate artificial atoms using different technologies like low-capacitance Josephson junctions. In this paper, we propose a new algorithm capable of directly measuring the three-tangle in tripartite GHZ states. To assess the reliability of our algorithm, we ran experiments by executing the circuit on three IBM backends featuring 127 qubits, deliberately selecting qubits with the lowest readout error. Despite the NISQ nature of the IBM quantum platforms, the results indicate the precision of our proposed protocol. This research introduces a valuable protocol, alongside its experimental proof, to measure three-tangle without using tomography and intensive numerical optimization methods to calculate three-tangle.
quantum effects like entanglement and coherent amplification can be used to drastically enhance the accuracy of quantum parameter estimation beyond classical limits. However, challenges such as decoherence and time-de...
quantum effects like entanglement and coherent amplification can be used to drastically enhance the accuracy of quantum parameter estimation beyond classical limits. However, challenges such as decoherence and time-dependent errors hinder Heisenberg-limited amplification. We introduce quantum Signal-processing Phase Estimation algorithms that are robust against these challenges and achieve optimal performance as dictated by the Cram & eacute;r-Rao bound. These algorithms use quantum signal transformation to decouple interdependent phase parameters into largely orthogonal ones, ensuring that time-dependent errors in one do not compromise the accuracy of learning the other. Combining provably optimal classical estimation with near-optimal quantum circuit design, our approach achieves a standard deviation accuracy of 10(-4) radians for estimating unwanted swap angles in superconducting two-qubit experiments, using low-depth ( < 10) circuits. This represents up to two orders of magnitude improvement over existing methods. Theoretically and numerically, we demonstrate the optimality of our algorithm against time-dependent phase errors, observing that the variance of the time-sensitive parameter phi scales faster than the asymptotic Heisenberg scaling in the small-depth regime. Our results are rigorously validated against the quantum Fisher information, confirming our protocol's ability to achieve unmatched precision for two-qubit gate learning.
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