The use of quantum processing units (QPUs) promises speed-ups for solving computational problems, in particular for discrete optimisation. While a few groundbreaking algorithmic approaches are known that can provably ...
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(纸本)9798400705533
The use of quantum processing units (QPUs) promises speed-ups for solving computational problems, in particular for discrete optimisation. While a few groundbreaking algorithmic approaches are known that can provably outperform classical computers, we observe a scarcity of programming abstractions for constructing efficient quantum algorithms. A good fraction of the literature that addresses solving concrete problems related to database management concentrates on casting them as quadratic unconstrained binary optimisation problems (QUBOs), which can then, among others, be processed on gate-based machines (using the quantum approximate optimisation algorithm), or quantum annealers. A critical aspect that affects efficiency and scalability of either of these approaches is how classical data are loaded into qubits, respectively how problems are encoded into QUBO representation. The effectiveness of encodings is known to be of crucial importance for quantum computers, especially since the amount of available qubits is strongly limited in the era of noisy, intermediate-size quantum computers. In this paper, we present three encoding patterns, discuss their impact on scalability, and their ease of use. We consider the recreational (yet computationally challenging) Sudoku problem and its reduction to graph colouring as an illustrative example to discuss their individual benefits and disadvantages. Our aim is enable database researchers to choose an appropriate encoding scheme for their purpose without having to acquire in-depth knowledge on quantum peculiarities, thus easing the path towards applying quantum acceleration on data management systems.
The existing body of research on quantum embedding techniques is not only confined in scope but also lacks a comprehensive understanding of the intricacies of the quantum embedding process. To address this critical is...
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The existing body of research on quantum embedding techniques is not only confined in scope but also lacks a comprehensive understanding of the intricacies of the quantum embedding process. To address this critical issue, this article explores quantum encoding schemes, uncovering valuable insights into their encoding algorithms from theoretical foundations to a mathematical perspective, as well as practical applications. Initially, the article briefly overviews classical computing and the limitations associated with classical bits in representing and processing complex information. Next, the article scrutinizes a variety of quantum embedding patterns, including basis encoding, amplitude encoding, Qsample encoding, angle encoding, quantum associative memory encoding, quantum random access memory, superdense encoding, Hamiltonian encoding, and others. In addition, each technique is accompanied by mathematical formulas and examples illustrating how each strategy can be applied. Finally, the article provides a comparative analysis of different quantum embedding/encoding methods, outlining their strengths and limitations. Overall, this insightful article highlights the potential of quantum encoding techniques for efficient information processing beyond classical bits, thereby facilitating scientists and design engineers in selecting the most appropriate encoding technique to develop smart algorithms for revolutionizing the field of quantum computing.
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