Quo Vadis Qubit?

With the explosion of big data, higher requirements for computational efficiency have emerged. Compared with classical computing, quantum computing possesses quantum parallelism due to the unique nature of quantum systems. It has been found that many classical algorithms can be accelerated using quantum computing. In addition to factorizing a large integer, quantum computers can be used for data processing and analysis. In recent years, two frontiers, i.e., big data and quantum computing have begun to merge. Though practical quantum computers have not yet been built, theoretical studies have made some important progress. In this review, we introduce the basic principles of quantum computing. As a representative example, we describe the Grover search algorithm and its important generalizations. Quantum machine learning is the entry point for the integration of big data with quantum computation. We review in detail, the applications of quantum computation in data mining, the main application of machine learning. Other aspects of quantum computing in big data are also briefly summarized.

This Research to Practice Full Paper discusses an approach to motivating graduate computer science and electrical engineering students in the study of quantum computing. The U.S. Congress and other governmental and industrial organizations have recognized that teaching Quantum Computing in university-level Computer Science and Electrical Engineering programs is strategically important for workforce development along with technology development. Success in this endeavor requires generating genuine interest in the topic and motivating potential students. Quantum Computing requires an understanding of relatively difficult related subjects including physics, math and computer science. Many computer science and electrical engineering students shy away from quantum computing because of its complexity. This paper discusses our success with generating interest in quantum computing among graduate students and motivating them through exploring, in related courses, some aspects of quantum computing that have the potential to generate discontinuities. The approach is also examined in terms of the Scientific Learning Cycle as discussed by Wankat & Oreovicz. Opportunities and challenges of adding Quantum Computing to Computer Science and Electrical Engineering programs were studied through a student small group project in a cybersecurity course. The work built on extensive research into the effectiveness of small group projects and the role of “play” in learning. Our small group project on quantum computing was introduced three years ago into a cybersecurity course which is required for all students in our university's Masters in Computer Science and Masters in Electrical Engineering programs. The basic goals of the project are to teach students some basics of quantum computing by allowing them to “play with it” and to alert students to the fact that quantum computing is a rapidly developing new field that they need to follow during at least the next decade. The small group project helped students to explore the potential of quantum computing to substantially undermine current Public Key Cryptography and, at the same time provide a new approach to encryption that is even more secure than Public Key Cryptography. A qualitative analysis of discussions and assigned tasks suggests that this strategy succeeded in generating significant interest in quantum computing among students. They became motivated and immersed in a difficult subject despite the requirement for investment of significant time and effort to understand a complex combination of physics, math and computer science.

It should be clear by now that quantum computers are theoretically possible. The questions are whether they can be built and, if so, how to build them. Throughout this book we have been discussing the one-atom-per-bit limit that, judging on the basis of current extrapolations, will be reached by the year 2020. If that goal is two decades away, why should we be worrying today about how to build quantum computers? The reason is that quantum computers need not wait for the one-atom-per-bit limit to be reached. Just as transistor technology achieved astounding miniaturizations from the first coin-sized transistors, we can expect the same sort of evolution from quantum computers. As we will see, true quantum switches and logic gates have already been built. These tabletop-sized devices are obviously not feasible for any practical quantum computer, but they represent the necessary first steps in the process.

Quantum computers operate on the principles of quantum logic, a fundamentally distinct paradigm from classical Boolean logic. This disparity leads to quantum computation's enhanced efficiency compared to classical computing. In this comprehensive review, we demystify the fundamental concepts of quantum computation, covering the creation of elementary gates and networks. We highlight the capabilities of quantum algorithms by examining the simple Deutsch problem and, in straightforward terms, dissect the renowned Shor algorithm for factoring large numbers into primes. Furthermore, we delve into the realm of physical quantum computer implementations, with a particular focus on tlo he linear ion trap approach. Here, we shed light on the primary challenge hindering the realization of practical quantum computers: the issue of decoherence. Nonetheless, we demonstrate that this hurdle can be overcome through the application of quantum error correction methods

This article analyzes the transformative impact of convergent technologies— quantum computing and artificial intelligence—on the architecture of international security. Amid the accelerated development of quantum artificial intelligence (quantum AI), new classes of strategic threats are emerging, affecting the foundations of international and national security, cryptographic infrastructure, global intelligence, and military potential. The research is based on an analysis of official strategic documents from NATO, the European Union, the G7, US intelligence assessments, and initiatives of the international community. It identifies critical challenges and proposes adaptive response mechanisms through the "Bletchley Method" paradigm for the quantum era.

Generative Artificial Intelligence (GenAI) for Natural Language Processing (NLP) is the predominant AI technology to date. An important perspective for Quantum Computing (QC) is the question whether QC has the potential to reduce the vast resource requirements for training and operating GenAI models. While large-scale generative NLP tasks are currently out of reach for practical quantum computers, the generation of short semantic structures such as passwords is not. Generating passwords that mimic real user behavior has many applications, for example to test an authentication system against realistic threat models. Classical password generation via deep learning have recently been investigated with significant progress in their ability to generate novel, realistic password candidates. In the present work we investigate the utility of adiabatic quantum computers for this task. More precisely, we study different encodings of token strings and propose novel approaches based on the Quadratic Unconstrained Binary Optimization (QUBO) and the Unit-Disk Maximum Independent Set (UD-MIS) problems. Our approach allows us to estimate the token distribution from data and adiabatically prepare a quantum state from which we eventually sample the generated passwords via measurements. Our results show that relatively small samples of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2 8}$</tex> passwords, generated on the QuEra Aquila 256-qubit neutral atom quantum computer, contain human-like passwords such as Tunas200992 or teedem28iglove.

The best design for practical quantum computing is one that emulates the multi-agent quantum logic function of natural biological systems. Such systems are theorized to be based upon a quantum gate formed by a nucleic acid Szilard engine (NASE) that converts Shannon entropy of encountered molecules into useful work of nucleic acid geometric reconfiguration. This theoretical mechanism is logically and thermodynamically reversible in this special case because it is literally constructed out of the (nucleic acid) information necessary for its function, thereby allowing the nucleic acid Szilard engine to function reversibly because, since the information by which it functions exists on both sides of the theoretical mechanism simultaneously, there would be no build-up of information within the theoretical mechanism, and therefore no irreversible thermodynamic energy cost would be necessary to erase information inside the mechanism. This symmetry breaking Szilard engine function is associated with emission and/or absorption of entangled photons that can provide quantum synchronization of other nucleic acid segments within and between cells. In this manner nucleic acids can be considered as a natural model of topological quantum computing in which the nonabelian interaction of genes can be represented within quantum knot/braid theory as anyon crosses determined by entropic loss or gain that leads to changes in nucleic acid covalent bond angles. This naturally occurring biological form of topological quantum computing can serve as a model for workable man-made multi-agent quantum computing systems.

The fundamental unit for quantum computing is the qubit, an isolated, controllable two-level system. However, for many proposed quantum computer architectures, especially photonic systems, the qubits can be lost or can leak out of the desired two-level systems, posing a significant obstacle for practical quantum computation. Here, we experimentally demonstrate, both in the quantum circuit model and in the one-way quantum computer model, the smallest nontrivial quantum codes to tackle this problem. In the experiment, we encode single-qubit input states into highly entangled multiparticle code words, and we test their ability to protect encoded quantum information from detected 1-qubit loss error. Our results prove in-principle the feasibility of overcoming the qubit loss error by quantum codes.

While quantum computers might speed up in principle certain computations dramatically, in practice, though quantum computing technology is still in its infancy. Even we cannot clearly envision at present what the hardware of that machine will be like. Nevertheless, we can be quite confident that it will be much easier to build any practical quantum computer operating on a few number of quantum bits rather than one operating on a huge number of quantum bits. It is therefore of big practical impact to use the resource of quantum bits very spare, i.e., to find quantum algorithms which use as few as possible quantum bits. Here, we present a method to reduce the number of actually needed qubits in Shor's algorithm to factor a composite number N. Exploiting the inherent probabilism of quantum computation we are able to substitute the continued fraction algorithm to find a certain unknown fraction by a simultaneous Diophantine approximation. While the continued fraction algorithm is able to find a Diophantine approximation to a single known fraction with a denominator greater than N2, our simultaneous Diophantine approximation method computes in polynomial time unusually good approximations to known fractions with a denominator of size N1+?, where ? is allowed to be an arbitrarily small positive constant. As these unusually good approximations are almost unique we are able to recover an unknown denominator using fewer qubits in the quantum part of our algorithm.

Practical quantum computing is rapidly becoming a reality. To harness quantum computers' real potential in software applications, one needs to have an in-depth understanding of all such characteristics of quantum computing platforms (QCPs), relevant from the Software Engineering (SE) perspective. Restrictions on copying, deletion, the transmission of qubit states, a hard dependency on quantum algorithms are few, out of many, examples of QCP characteristics that have significant implications for building quantum software. Thus, developing quantum software requires a paradigm shift in thinking by software engineers. This paper presents the key findings from the SE perspective, resulting from an in-depth examination of state-of-the-art QCPs available today. The main contributions that we present include i) Proposing a general architecture of the QCPs, ii) Proposing a programming model for developing quantum software, iii) Determining architecturally significant characteristics of QCPs, and \textbf{iv)} Determining the impact of these characteristics on various Quality Attributes (QAs) and Software Development Life Cycle (SDLC) activities. We show that the nature of QCPs makes them useful mainly in specialized application areas such as scientific computing. Except for performance and scalability, most of the other QAs (e.g., maintainability, testability, and reliability) are adversely affected by different characteristics of a QCP.

Trapped ions are among the most promising systems for practical quantum computing (QC). The basic requirements for universal QC have all been demonstrated with ions, and quantum algorithms using few-ion-qubit systems have been implemented. We review the state of the field, covering the basics of how trapped ions are used for QC and their strengths and limitations as qubits. In addition, we discuss what is being done, and what may be required, to increase the scale of trapped ion quantum computers while mitigating decoherence and control errors. Finally, we explore the outlook for trapped-ion QC. In particular, we discuss near-term applications, considerations impacting the design of future systems of trapped ions, and experiments and demonstrations that may further inform these considerations.

Continuous-variable measurement-based quantum computation, which requires deterministically generated large-scale cluster states, is a promising candidate for practical, scalable, universal, and fault-tolerant quantum computation. In this work, based on our compact and scalable scheme of generating a two-dimensional spatiotemporal cluster state, a complete architecture including cluster state preparation, gate implementations, and error correction, is proposed. First, a scheme for generating two-dimensional large-scale continuous-variable cluster state by multiplexing both the temporal and spatial domains is proposed. Then, the corresponding gate implementations by gate teleportation are discussed and the actual gate noise from the generated cluster state is considered. After that, the quantum error correction can be further achieved by utilizing the square-lattice Gottesman-Kitaev-Preskill (GKP) code. Finally, a fault-tolerant quantum computation can be realized by introducing bias into the square-lattice GKP code (to protect against phase-flip errors) and concatenating a repetition code (to handle the residual bit-flip errors), with a squeezing threshold of 12.3 dB. Our work provides a possible option for a complete fault-tolerant quantum computation architecture in the future.

Parallel operations in conventional computing have proven to be an essential tool for efficient and practical computation, and the story is not different for quantum computing. Indeed, there exists a large body of works that study advantages of parallel implementations of quantum gates for efficient quantum circuit implementations. Here, we focus on the recently invented efficient, arbitrary, simultaneously entangling (EASE) gates, available on a trapped-ion quantum computer. Leveraging its flexibility in selecting arbitrary pairs of qubits to be coupled with any degrees of entanglement, all in parallel, we show an n-qubit Clifford circuit can be implemented using 6log(n) EASE gates, an n-qubit multiply-controlled NOT gate can be implemented using 3n/2 EASE gates, and an n-qubit permutation can be implemented using six EASE gates. We discuss their implications to near-term quantum chemistry simulations and the state of the art pattern matching algorithm. Given Clifford + multiply-controlled NOT gates form a universal gate set for quantum computing, our results imply efficient quantum computation by EASE gates, in general.

Among a number of candidates, photons have advantages for implementing qubits: very weak coupling to the environment, the existing single photon measurement technique, and so on. Moreover, commercially available fiber-optic devices enable us to construct quantum circuits that consist of one-qubit operations (including classically controlled gates). Fiber optics resolves the mode matching problems in conventional optics and provides mechanically stable optical circuits. A quantum Fourier transform (QFT) followed by measurement was demonstrated with a simple circuit based on fiber optics. The circuit was shown to be robust against imperfections in the rotation gate. The error probability was estimated to be 0.01 per qubit, which corresponded to error-free operation for 100 qubits. The error probability can be further reduced to achieve successful QFT of 1024 qubits by taking the majority of the accumulated results. As is well known, QFT is a key function in quantum computations such as the final part of Shor’s factorization algorithm. The present QFT circuit, in combination with controlled unitary gates, would make possible practical quantum computers. Possible schemes of realizing quantum computers in this line are explored.

Quantum computing promises an exponentially higher computational power than classical computers; although all the building blocks have become available, certain constraints still prevent quantum advantage. The fundamental challenge in building a practical quantum computer is integrating thousands of highly coherent qubits with the control and readout electronics. The need for a high‐coherence qubit drives the effort for quantum error correction algorithms to create fault‐tolerant quantum systems. Error correction becomes tangible in a quantum processor only in large numbers of qubits. Thus, the other challenge is reducing the number of physical interconnects (coaxial lines) between the quantum–classical interface and bulky room‐temperature electronics. To interface thousands of qubits, interconnects can be reduced by bringing the control and readout electronics near the quantum processor. Cryogenic complementary metal–oxide–semiconductor (CMOS) technology has been an ideal candidate for this purpose. Integrated control and readout at cryogenic temperatures require low power dissipation circuit designs and techniques such as frequency‐division multiplexing (FDM) due to the finite cooling power of a dilution refrigerator. Herein, an overview of each building block in a superconducting quantum computer is provided, focusing on scalability. Furthermore, this article is concluded with an outlook discussing current challenges and future directions for the scalable superconducting control and readout.