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Electronics
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Quantum Computing System Design and Architecture
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Quantum Computing System Design and Architecture

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Quantum Electronics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 19965

Special Issue Editor

Prof. Dr. Koen Bertels

E-Mail Website1 Website2
Guest Editor
1. QBee, Martelarenlaan 38, 3rd Floor, 3010 Leuven, Belgium
2. Quantum Computer Architecture lab, University of Ghent, 9000 Gent, Belgium
Interests: quantum computing architectures; quantum programming languages and simulation platforms; embedded systems; high-performance computing & Big Data heterogeneous multicore platforms

Special Issue Information

Dear Colleagues,

Quantum computing is becoming very popular and is researched all over the world. There are still a lot of challenges that need to be overcome in order to make a quantum accelerator. Our work is based on developing the full stack of all components needed for a quantum accelerator.

In this Special Issue, we will highlight the following topics.

  1. Physical qubits - the pre-transistor period: There is no agreement in the quantum physics world on how to make a good quantum bit, called a qubit. There are many disciplines in competition with each other and the quantum computing world needs to agree on a very small number of qubit technologies.
  2. The quality of the qubits: The error rates and decoherence of qubits are still very big. In CMOS, we are used to handling errors of 10-15/-16, whereas in quantum computing we are at 10-2/-3 . From what level on do we have industrially good qubits and when do we expect to have a good family of qubits?
  3. Quantum applications: A very important step towards what one might call a new scientific revolution is the invitation to scientists from all fields to start looking at the quantum logic concepts one needs to develop algorithms to address problems from their field. One should be aware that the quantum logic is radically different from any classical way of reasoning and it takes multiple years to reach a good level of understanding and maturity before any quantum solution is developed, programmed and tested.
  4. Quantum programming: For any application developed, it needs to be implemented and programmed in a quantum programming language. There are almost too many languages around, but it would be interesting to look at some big initiatives such as the Qiskit from IBM or other tools. Questions such as whether an accelerator language is important, the speed at which quantum algorithms are executed, and how we handle errors, etc., are all issues that might be studied.
  5. Micro-Architecture: In the quantum mechanical world, the phenomena that we are using for computing, such as superposition and full entanglement, need to be controlled by a digital architecture. So, it is very interesting to see how teams are building such a micro-architectures, explaining what the limitations are in terms of number of qubits, and how errors are dealt with. Of interest also is finding out what the quantum operating system is and what the digital components needed to store the digital version of the qubits are.
  6. Routing and Mapping: Independent of the qubit technology is the need to place qubits close together if a 2 or 3 qubit operation is required. This implies that qubits need to be routed to bring them near to each other. This is a complex problem as it is necessary to understand what qubits are more intensively needed in the quantum algorithm at any moment in time. Papers that describe the challenges of those routing and mapping algorithms are welcomed.
  7. Quantum Simulator: While we are waiting for good qubits to be produced, it is necessary in the meantime to be able to execute any quantum algorithm on a classical machine. There is no fundamental limitation to do that, so it is necessary to keep on developing and extending quantum simulators that will simulate the behavior of any qubit chip that will be made. For this topic, contributions that describe the features of the most powerful simulators are welcomed.

Prof. Dr. Koen Bertels
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • quantum applications
  • quantum programming
  • micro-architecture
  • routing and mapping
  • quantum simulator

Published Papers (7 papers)

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Research

16 pages, 323 KiB  
Article
Pattern QUBOs: Algorithmic Construction of 3SAT-to-QUBO Transformations
by Sebastian Zielinski, Jonas Nüßlein, Jonas Stein, Thomas Gabor, Claudia Linnhoff-Popien and Sebastian Feld
Electronics 2023, 12(16), 3492; https://doi.org/10.3390/electronics12163492 - 17 Aug 2023
Viewed by 1061
Abstract
One way of solving 3sat instances on a quantum computer is to transform the 3sat instances into instances of Quadratic Unconstrained Binary Optimizations (QUBOs), which can be used as an input for the QAOA algorithm on quantum gate systems or as an input [...] Read more.
One way of solving 3sat instances on a quantum computer is to transform the 3sat instances into instances of Quadratic Unconstrained Binary Optimizations (QUBOs), which can be used as an input for the QAOA algorithm on quantum gate systems or as an input for quantum annealers. This mapping is performed by a 3sat-to-QUBO transformation. Recently, it has been shown that the choice of the 3sat-to-QUBO transformation can significantly impact the solution quality of quantum annealing. It has been shown that the solution quality can vary up to an order of magnitude difference in the number of correct solutions received, depending solely on the 3sat-to-QUBO transformation. An open question is: what causes these differences in the solution quality when solving 3sat-instances with different 3sat-to-QUBO transformations? To be able to conduct meaningful studies that assess the reasons for the differences in the performance, a larger number of different 3sat-to-QUBO transformations would be needed. However, currently, there are only a few known 3sat-to-QUBO transformations, and all of them were created manually by experts, who used time and clever reasoning to create these transformations. In this paper, we will solve this problem by proposing an algorithmic method that is able to create thousands of new and different 3sat-to-QUBO transformations, and thus enables researchers to systematically study the reasons for the significant difference in the performance of different 3sat-to-QUBO transformations. Our algorithmic method is an exhaustive search procedure that exploits properties of 4×4 dimensional pattern QUBOs, a concept which has been used implicitly in the creation of 3sat-to-QUBO transformations before, but was never described explicitly. We will thus also formally and explicitly introduce the concept of pattern QUBOs in this paper. Full article
(This article belongs to the Special Issue Quantum Computing System Design and Architecture)
26 pages, 1097 KiB  
Article
Configurable Readout Error Mitigation in Quantum Workflows
by Martin Beisel, Johanna Barzen, Frank Leymann, Felix Truger, Benjamin Weder and Vladimir Yussupov
Electronics 2022, 11(19), 2983; https://doi.org/10.3390/electronics11192983 - 20 Sep 2022
Cited by 5 | Viewed by 2466
Abstract
Current quantum computers are still error-prone, with measurement errors being one of the factors limiting the scalability of quantum devices. To reduce their impact, a variety of readout error mitigation methods, mostly relying on classical post-processing, have been developed. However, the application of [...] Read more.
Current quantum computers are still error-prone, with measurement errors being one of the factors limiting the scalability of quantum devices. To reduce their impact, a variety of readout error mitigation methods, mostly relying on classical post-processing, have been developed. However, the application of these methods is complicated by their heterogeneity and a lack of information regarding their functionality, configuration, and integration. To facilitate their use, we provide an overview of existing methods, and evaluate general and method-specific configuration options. Quantum applications comprise many classical pre- and post-processing tasks, including readout error mitigation. Automation can facilitate the execution of these often complex tasks, as their manual execution is time-consuming and error-prone. Workflow technology is a promising candidate for the orchestration of heterogeneous tasks, offering advantages such as reliability, robustness, and monitoring capabilities. In this paper, we present an approach to abstractly model quantum workflows comprising configurable readout error mitigation tasks. Based on the method configuration, these workflows can then be automatically refined into executable workflow models. To validate the feasibility of our approach, we provide a prototypical implementation and demonstrate it in a case study from the quantum humanities domain. Full article
(This article belongs to the Special Issue Quantum Computing System Design and Architecture)
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25 pages, 1812 KiB  
Article
Selection and Optimization of Hyperparameters in Warm-Started Quantum Optimization for the MaxCut Problem
by Felix Truger, Martin Beisel, Johanna Barzen, Frank Leymann and Vladimir Yussupov
Electronics 2022, 11(7), 1033; https://doi.org/10.3390/electronics11071033 - 25 Mar 2022
Cited by 6 | Viewed by 2634
Abstract
Today’s quantum computers are limited in their capabilities, e.g., the size of executable quantum circuits. The Quantum Approximate Optimization Algorithm (QAOA) addresses these limitations and is, therefore, a promising candidate for achieving a near-term quantum advantage. Warm-starting can further improve QAOA by utilizing [...] Read more.
Today’s quantum computers are limited in their capabilities, e.g., the size of executable quantum circuits. The Quantum Approximate Optimization Algorithm (QAOA) addresses these limitations and is, therefore, a promising candidate for achieving a near-term quantum advantage. Warm-starting can further improve QAOA by utilizing classically pre-computed approximations to achieve better solutions at a small circuit depth. However, warm-starting requirements often depend on the quantum algorithm and problem at hand. Warm-started QAOA (WS-QAOA) requires developers to understand how to select approach-specific hyperparameter values that tune the embedding of classically pre-computed approximations. In this paper, we address the problem of hyperparameter selection in WS-QAOA for the maximum cut problem using the classical Goemans–Williamson algorithm for pre-computations. The contributions of this work are as follows: We implement and run a set of experiments to determine how different hyperparameter settings influence the solution quality. In particular, we (i) analyze how the regularization parameter that tunes the bias of the warm-started quantum algorithm towards the pre-computed solution can be selected and optimized, (ii) compare three distinct optimization strategies, and (iii) evaluate five objective functions for the classical optimization, two of which we introduce specifically for our scenario. The experimental results provide insights on efficient selection of the regularization parameter, optimization strategy, and objective function and, thus, support developers in setting up one of the central algorithms of contemporary and near-term quantum computing. Full article
(This article belongs to the Special Issue Quantum Computing System Design and Architecture)
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13 pages, 546 KiB  
Article
Assembly of a Coreset of Earth Observation Images on a Small Quantum Computer
by Soronzonbold Otgonbaatar and Mihai Datcu
Electronics 2021, 10(20), 2482; https://doi.org/10.3390/electronics10202482 - 12 Oct 2021
Cited by 7 | Viewed by 1832
Abstract
Satellite instruments monitor the Earth’s surface day and night, and, as a result, the size of Earth observation (EO) data is dramatically increasing. Machine Learning (ML) techniques are employed routinely to analyze and process these big EO data, and one well-known ML technique [...] Read more.
Satellite instruments monitor the Earth’s surface day and night, and, as a result, the size of Earth observation (EO) data is dramatically increasing. Machine Learning (ML) techniques are employed routinely to analyze and process these big EO data, and one well-known ML technique is a Support Vector Machine (SVM). An SVM poses a quadratic programming problem, and quantum computers including quantum annealers (QA) as well as gate-based quantum computers promise to solve an SVM more efficiently than a conventional computer; training the SVM by employing a quantum computer/conventional computer represents a quantum SVM (qSVM)/classical SVM (cSVM) application. However, quantum computers cannot tackle many practical EO problems by using a qSVM due to their very low number of input qubits. Hence, we assembled a coreset (“core of a dataset”) of given EO data for training a weighted SVM on a small quantum computer, a D-Wave quantum annealer with around 5000 input quantum bits. The coreset is a small, representative weighted subset of an original dataset, and its performance can be analyzed by using the proposed weighted SVM on a small quantum computer in contrast to the original dataset. As practical data, we use synthetic data, Iris data, a Hyperspectral Image (HSI) of Indian Pine, and a Polarimetric Synthetic Aperture Radar (PolSAR) image of San Francisco. We measured the closeness between an original dataset and its coreset by employing a Kullback–Leibler (KL) divergence test, and, in addition, we trained a weighted SVM on our coreset data by using both a D-Wave quantum annealer (D-Wave QA) and a conventional computer. Our findings show that the coreset approximates the original dataset with very small KL divergence (smaller is better), and the weighted qSVM even outperforms the weighted cSVM on the coresets for a few instances of our experiments. As a side result (or a by-product result), we also present our KL divergence findings for demonstrating the closeness between our original data (i.e., our synthetic data, Iris data, hyperspectral image, and PolSAR image) and the assembled coreset. Full article
(This article belongs to the Special Issue Quantum Computing System Design and Architecture)
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17 pages, 4224 KiB  
Article
QiBAM: Approximate Sub-String Index Search on Quantum Accelerators Applied to DNA Read Alignment
by Aritra Sarkar, Zaid Al-Ars, Carmen G. Almudever and Koen L. M. Bertels
Electronics 2021, 10(19), 2433; https://doi.org/10.3390/electronics10192433 - 7 Oct 2021
Cited by 7 | Viewed by 2753
Abstract
With small-scale quantum processors transitioning from experimental physics labs to industrial products, these processors in a few years are expected to scale up and be more robust for efficiently computing important algorithms in various fields. In this paper, we propose a quantum algorithm [...] Read more.
With small-scale quantum processors transitioning from experimental physics labs to industrial products, these processors in a few years are expected to scale up and be more robust for efficiently computing important algorithms in various fields. In this paper, we propose a quantum algorithm to address the challenging field of data processing for genome sequence reconstruction. This research describes an architecture-aware implementation of a quantum algorithm for sub-sequence alignment. A new algorithm named QiBAM (quantum indexed bidirectional associative memory) is proposed, which uses approximate pattern-matching based on Hamming distances. QiBAM extends the Grover’s search algorithm in two ways, allowing: (1) approximate matches needed for read errors in genomics, and (2) a distributed search for multiple solutions over the quantum encoding of DNA sequences. This approach gives a quadratic speedup over the classical algorithm. A full implementation of the algorithm is provided and verified using the OpenQL compiler and QX Simulator framework. Our implementation represents a first exploration towards a full-stack quantum accelerated genome sequencing pipeline design. Full article
(This article belongs to the Special Issue Quantum Computing System Design and Architecture)
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13 pages, 2232 KiB  
Article
Coreset Clustering on Small Quantum Computers
by Teague Tomesh, Pranav Gokhale, Eric R. Anschuetz and Frederic T. Chong
Electronics 2021, 10(14), 1690; https://doi.org/10.3390/electronics10141690 - 15 Jul 2021
Cited by 9 | Viewed by 3410
Abstract
Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by [...] Read more.
Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by Harrow introduces a new paradigm in hybrid quantum-classical computing to address this issue, relying on coresets to minimize the data loading overhead of quantum algorithms. We investigated using this paradigm to perform k-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset. We used numerical simulations to compare the performance of this approach to classical k-means clustering. We were able to find data sets with which coresets work well relative to random sampling and where QAOA could potentially outperform standard k-means on a coreset. However, finding data sets where both coresets and QAOA work well—which is necessary for a quantum advantage over k-means on the entire data set—appears to be challenging. Full article
(This article belongs to the Special Issue Quantum Computing System Design and Architecture)
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18 pages, 1006 KiB  
Article
Automated Quantum Hardware Selection for Quantum Workflows
by Benjamin Weder, Johanna Barzen, Frank Leymann and Marie Salm
Electronics 2021, 10(8), 984; https://doi.org/10.3390/electronics10080984 - 20 Apr 2021
Cited by 11 | Viewed by 3615
Abstract
The execution of a quantum algorithm typically requires various classical pre- and post-processing tasks. Hence, workflows are a promising means to orchestrate these tasks, benefiting from their reliability, robustness, and features, such as transactional processing. However, the implementations of the tasks may be [...] Read more.
The execution of a quantum algorithm typically requires various classical pre- and post-processing tasks. Hence, workflows are a promising means to orchestrate these tasks, benefiting from their reliability, robustness, and features, such as transactional processing. However, the implementations of the tasks may be very heterogeneous and they depend on the quantum hardware used to execute the quantum circuits of the algorithm. Additionally, today’s quantum computers are still restricted, which limits the size of the quantum circuits that can be executed. As the circuit size often depends on the input data of the algorithm, the selection of quantum hardware to execute a quantum circuit must be done at workflow runtime. However, modeling all possible alternative tasks would clutter the workflow model and require its adaptation whenever a new quantum computer or software tool is released. To overcome this problem, we introduce an approach to automatically select suitable quantum hardware for the execution of quantum circuits in workflows. Furthermore, it enables the dynamic adaptation of the workflows, depending on the selection at runtime based on reusable workflow fragments. We validate our approach with a prototypical implementation and a case study demonstrating the hardware selection for Simon’s algorithm. Full article
(This article belongs to the Special Issue Quantum Computing System Design and Architecture)
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