Detailed Account of Complexity for Implementation of Circuit-Based Quantum Algorithms

Cardoso, Fernando R.; Akamatsu, Daniel Yoshio; Campo, Vivaldo L.; Duzzioni, E. I.; Jaramillo, Alfredo; Villas-Bôas, C. J.

doi:10.3389/fphy.2021.731007

Cited by 3 publications

(1 citation statement)

References 68 publications

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“…This encoding will represent the solution x to the linear system; this is also referred to as the "quantum analogdigital conversion" process [107]. However, there are certain issues with the HHL algorithm that compromise the exponential speed-up [149], e.g., loading |b⟩ in amplitude encoding may require an exponential number of operations-the 'input problem'. Also, the algorithm produces the solution vector |x⟩ in amplitude encoding, which possibly requires multiple repetitions to estimate the amplitudes-the 'output problem' [150].…”

Section: Challenges and Future Research Prospectsmentioning

confidence: 99%

Beyond Bits: A Review of Quantum Embedding Techniques for Efficient Information Processing

Khan,

Aman,

Sikdar

2024

IEEE Access

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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.INDEX TERMS Encoding patterns, qubits, quantum computing, quantum information processing, quantum circuits.

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Section: Challenges and Future Research Prospectsmentioning

confidence: 99%

Beyond Bits: A Review of Quantum Embedding Techniques for Efficient Information Processing

Khan,

Aman,

Sikdar

2024

IEEE Access

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A Parallel and Distributed Quantum SAT Solver Based on Entanglement and Teleportation

Lin,

Wang,

Chen

et al. 2024

Tools and Algorithms for the Construction and Analysis of Systems

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Boolean satisfiability (SAT) solving is a fundamental problem in computer science. Finding efficient algorithms for SAT solving has broad implications in many areas of computer science and beyond. Quantum SAT solvers have been proposed in the literature based on Grover’s algorithm. Although existing quantum SAT solvers can consider all possible inputs at once, they evaluate each clause in the formula one by one sequentially, making the time complexity O(m), linear to the number of clauses m, per Grover iteration. In this work, we develop a parallel quantum SAT solver, which reduces the time complexity in each iteration to constant time O(1) by utilising extra entangled qubits. To further improve the scalability of our solution in case of extremely large problems, we develop a distributed version of the proposed parallel SAT solver based on quantum teleportation such that the total qubits required are shared and distributed among a set of quantum computers (nodes), and the quantum SAT solving is accomplished collaboratively by all the nodes. We prove the correctness of our approaches and evaluate them in simulations and real quantum computers.

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Q-SupCon: Quantum-Enhanced Supervised Contrastive Learning Architecture within the Representation Learning Framework

Don,

Khalil

2024

ACM Transactions on Quantum Computing

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In the evolving landscape of data privacy regulations, the challenge of providing extensive data for robust deep classification models arises. The accuracy of these models relies on the amount of training data, due to the multitude of parameters that require tuning. Unfortunately, obtaining such ample data proves challenging, particularly in domains like medical applications, where there is a pressing need for robust models for early disease detection but a shortage of labeled data. Nevertheless, the classical supervised contrastive learning models, have shown the potential to address this challenge up to a certain limit, by utilizing deep encoder models. However, recent advancements in quantum machine learning enable the extraction of meaningful representations from extremely limited and simple data. Thus, replacing classical counterparts in classical or hybrid quantum-classical supervised contrastive models enhances feature learning capability with minimal data. Therefore, this work proposes the Q-SupCon model, a fully quantum-powered supervised contrastive learning model comprising a quantum data augmentation circuit, quantum encoder, quantum projection head, and quantum variational classifier, enabling efficient image classification with minimal labeled data. Furthermore, the novel model attains 80%, 60%, and 80% test accuracy on MNIST, KMNIST, and FMNIST datasets, marking a significant advancement in addressing the data scarcity challenge.

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Detailed Account of Complexity for Implementation of Circuit-Based Quantum Algorithms

Cited by 3 publications

References 68 publications

Beyond Bits: A Review of Quantum Embedding Techniques for Efficient Information Processing

Beyond Bits: A Review of Quantum Embedding Techniques for Efficient Information Processing

A Parallel and Distributed Quantum SAT Solver Based on Entanglement and Teleportation

Q-SupCon: Quantum-Enhanced Supervised Contrastive Learning Architecture within the Representation Learning Framework

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