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Quantum Reports
Special Issues
Quantum Computing: A Taxonomy, Systematic Review, and Future Directions
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Quantum Computing: A Taxonomy, Systematic Review, and Future Directions

A special issue of Quantum Reports (ISSN 2624-960X).

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 10152

Special Issue Editor

Dr. Yousef Fazea

E-Mail Website
Guest Editor
Department of Computer Science and Electrical Engineering, Marshall University, 1 John Marshall Drive, Huntington, WV 25755, USA
Interests: high computing performance; next-generation computing and telecommunication; digital communication networks; high-speed networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue welcomes the submission of new advanced and substantial contributions on state-of-the-art quantum computing as well as on the current body of knowledge on quantum information. This may include research on emerging trends in high-performance computing, quantum computing, hybrid classical and quantum computing approaches, demonstrations of quantum advantages, technology milestones in quantum networks and infrastructures, or quantum information processes in biological systems.

Dr. Yousef Fazea
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Quantum Reports is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • high-performance computing
  • quantum computing
  • photonic quantum computing
  • quantum communications
  • quantum machine learning
  • quantum imaging and sensing
  • quantum education

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Published Papers (5 papers)

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Research

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21 pages, 533 KiB  
Article
An Extended Analysis of the Correlation Extraction Algorithm in the Context of Linear Cryptanalysis
by Christoph Graebnitz, Valentin Pickel, Holger Eble, Frank Morgner, Hannes Hattenbach and Marian Margraf
Quantum Rep. 2024, 6(4), 714-734; https://doi.org/10.3390/quantum6040043 - 22 Dec 2024
Viewed by 649
Abstract
In cryptography, techniques and tools developed in the subfield of linear cryptanalysis have previously successfully been used to allow attackers to break many sophisticated cryptographic ciphers. Since these linear cryptanalytic techniques require exploitable linear approximations to relate the input and output of vectorial [...] Read more.
In cryptography, techniques and tools developed in the subfield of linear cryptanalysis have previously successfully been used to allow attackers to break many sophisticated cryptographic ciphers. Since these linear cryptanalytic techniques require exploitable linear approximations to relate the input and output of vectorial Boolean functions, e.g., the plaintext, ciphertext, and key of the cryptographic function, finding these approximations is essential. For this purpose, the Correlation Extraction Algorithm (CEA), which leverages the emerging field of quantum computing, appears promising. However, there has been no comprehensive analysis of the CEA regarding finding an exploitable linear approximation for linear cryptanalysis. In this paper, we conduct a thorough theoretical analysis of the CEA. We aim to investigate its potential in finding a linear approximation with prescribed statistical characteristics. To support our theoretical work, we also present the results of a small empirical study based on a computer simulation. The analysis in this paper shows that an approach that uses the CEA to find exploitable linear approximations has an asymptotic advantage, reducing a linear factor to a logarithmic one in terms of time complexity, and an exponential advantage in terms of space complexity compared to a classical approach that uses the fast Walsh transform. Furthermore, we show that in specific scenarios, CEA can exponentially reduce the search space for exploitable linear approximations in terms of the number of input bits of the cipher. Neglecting the unresolved issue of efficiently checking the property of linear approximations measured by the CEA, our results indicate that the CEA can support the linear cryptanalysis of vectorial Boolean functions with relatively few (e.g., n≤32) output bits. Full article
(This article belongs to the Special Issue Quantum Computing: A Taxonomy, Systematic Review, and Future Directions)
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14 pages, 1252 KiB  
Article
Reverse Quantum Annealing Assisted by Forward Annealing
by Manpreet Singh Jattana
Quantum Rep. 2024, 6(3), 452-464; https://doi.org/10.3390/quantum6030030 - 23 Aug 2024
Viewed by 1158
Abstract
Quantum annealers conventionally use forward annealing to generate heuristic solutions. Reverse annealing can potentially generate better solutions but necessitates an appropriate initial state. Ways to find such states are generally unknown or highly problem dependent, offer limited success, and severely restrict the scope [...] Read more.
Quantum annealers conventionally use forward annealing to generate heuristic solutions. Reverse annealing can potentially generate better solutions but necessitates an appropriate initial state. Ways to find such states are generally unknown or highly problem dependent, offer limited success, and severely restrict the scope of reverse annealing. We use a general method that improves the overall solution quality and quantity by feeding reverse annealing with low-quality solutions obtained from forward annealing. An experimental demonstration of solving the graph coloring problem using the D-Wave quantum annealers shows that our method is able to convert invalid solutions obtained from forward annealing to at least one valid solution obtained after assisted reverse annealing for 57% of 459 random Erdos–Rényi graphs. Our method significantly outperforms random initial states, obtains more unique solutions on average, and widens the applicability of reverse annealing. Although the average number of valid solutions obtained drops exponentially with the problem size, a scaling analysis for the graph coloring problem shows that our method effectively extends the computational reach of conventional forward annealing using reverse annealing. Full article
(This article belongs to the Special Issue Quantum Computing: A Taxonomy, Systematic Review, and Future Directions)
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13 pages, 2016 KiB  
Article
The Quantum Amplitude Estimation Algorithms on Near-Term Devices: A Practical Guide
by Marco Maronese, Massimiliano Incudini, Luca Asproni and Enrico Prati
Quantum Rep. 2024, 6(1), 1-13; https://doi.org/10.3390/quantum6010001 - 24 Dec 2023
Cited by 2 | Viewed by 2933
Abstract
The Quantum Amplitude Estimation (QAE) algorithm is a major quantum algorithm designed to achieve a quadratic speed-up. Until fault-tolerant quantum computing is achieved, being competitive over classical Monte Carlo (MC) remains elusive. Alternative methods have been developed so as to require fewer resources [...] Read more.
The Quantum Amplitude Estimation (QAE) algorithm is a major quantum algorithm designed to achieve a quadratic speed-up. Until fault-tolerant quantum computing is achieved, being competitive over classical Monte Carlo (MC) remains elusive. Alternative methods have been developed so as to require fewer resources while maintaining an advantageous theoretical scaling. We compared the standard QAE algorithm with two Noisy Intermediate-Scale Quantum (NISQ)-friendly versions of QAE on a numerical integration task, with the Monte Carlo technique of Metropolis–Hastings as a classical benchmark. The algorithms were evaluated in terms of the estimation error as a function of the number of samples, computational time, and length of the quantum circuits required by the solutions, respectively. The effectiveness of the two QAE alternatives was tested on an 11-qubit trapped-ion quantum computer in order to verify which solution can first provide a speed-up in the integral estimation problems. We concluded that an alternative approach is preferable with respect to employing the phase estimation routine. Indeed, the Maximum Likelihood estimation guaranteed the best trade-off between the length of the quantum circuits and the precision in the integral estimation, as well as greater resistance to noise. Full article
(This article belongs to the Special Issue Quantum Computing: A Taxonomy, Systematic Review, and Future Directions)
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34 pages, 9544 KiB  
Article
Variational Amplitude Amplification for Solving QUBO Problems
by Daniel Koch, Massimiliano Cutugno, Saahil Patel, Laura Wessing and Paul M. Alsing
Quantum Rep. 2023, 5(4), 625-658; https://doi.org/10.3390/quantum5040041 - 1 Oct 2023
Viewed by 2399
Abstract
We investigate the use of amplitude amplification on the gate-based model of quantum computing as a means for solving combinatorial optimization problems. This study focuses primarily on quadratic unconstrained binary optimization (QUBO) problems, which are well-suited for qubit superposition states. Specifically, we demonstrate [...] Read more.
We investigate the use of amplitude amplification on the gate-based model of quantum computing as a means for solving combinatorial optimization problems. This study focuses primarily on quadratic unconstrained binary optimization (QUBO) problems, which are well-suited for qubit superposition states. Specifically, we demonstrate circuit designs which encode QUBOs as ‘cost oracle’ operations UC, which distribute phases across the basis states proportional to a cost function. We then show that when UC is combined with the standard Grover diffusion operator Us, one can achieve high probabilities of measurement for states corresponding to optimal and near optimal solutions while still only requiring O(π42N/M) iterations. In order to achieve these probabilities, a single scalar parameter ps is required, which we show can be found through a variational quantum–classical hybrid approach and can be used for heuristic solutions. Full article
(This article belongs to the Special Issue Quantum Computing: A Taxonomy, Systematic Review, and Future Directions)
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Review

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31 pages, 10192 KiB  
Review
A Comprehensive Review of Quantum Circuit Optimization: Current Trends and Future Directions
by Krishnageetha Karuppasamy, Varun Puram, Stevens Johnson and Johnson P. Thomas
Quantum Rep. 2025, 7(1), 2; https://doi.org/10.3390/quantum7010002 - 1 Jan 2025
Viewed by 1080
Abstract
Optimizing quantum circuits is critical for enhancing computational speed and mitigating errors caused by quantum noise. Effective optimization must be achieved without compromising the correctness of the computations. This survey explores recent advancements in quantum circuit optimization, encompassing both hardware-independent and hardware-dependent techniques. [...] Read more.
Optimizing quantum circuits is critical for enhancing computational speed and mitigating errors caused by quantum noise. Effective optimization must be achieved without compromising the correctness of the computations. This survey explores recent advancements in quantum circuit optimization, encompassing both hardware-independent and hardware-dependent techniques. It reviews state-of-the-art approaches, including analytical algorithms, heuristic strategies, machine learning-based methods, and hybrid quantum-classical frameworks. The paper highlights the strengths and limitations of each method, along with the challenges they pose. Furthermore, it identifies potential research opportunities in this evolving field, offering insights into the future directions of quantum circuit optimization. Full article
(This article belongs to the Special Issue Quantum Computing: A Taxonomy, Systematic Review, and Future Directions)
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