Newspapers and websites are reporting with strong emphasys the successes of quantum computing, obtained by the new quantum machines. It is therefore natural to ask ourselves some questions about these machines, in particular how do they operate, where are they physically and what is needed for their running. Actually, we must ask ourselves such questions because the news headlines – sometime bombastic - could lead us to understand quantum machines as actually existing supercomputers, able of solving in a few seconds some calculation problems that classical computers would take millennia to process. To answer questions, this work is proposing a review of existing Quantum Computers. In the panorama of Quantum Computing, we intend to investigate how the future of their infrastructures will be, such as the related basic algorithms. We will try to understand what their logistics will be, where logistics is understood as a new quantum "art of computing". Specific attention will be paid to the quantum advantage, the medium-term strategy related to design algorithms for the next generations of quantum computers. Such algorithms are required by a wide range of strategic applications. Currently, they are mainly simulation algorithms of quantum physics and chemistry, but also algorithms related to quadratic unconstrained binary optimization exist. Here we will illustrate in particular two methods: one based on abiabatic quantum computation, which is obtained through quantum annealing (D-Wave systems), the other based on arrays of quantum gates (IBM, Google and others). We will see, in greater detail, the simulations concerning the Variational Quantum Eigensolver method. Some notions related to qubits, quantum gates and their Hamiltonians will be proposed too. Besides talking of qubits, we will also report about qumodes. In fact, the art of quantum computing has two faces, one based on qubits, the other based on qumodes. Xanadu's Borealis quantum computer is an example of qumode computer. It is capable of developing a hybrid system, with GKP-type qubit emulation along with squeezed light. SpinQ's first three qubits desktop will also be mentioned. A hint of quantum communication will also be provided.
Newspapers and websites are reporting with strong emphasys the successes of quantum computing, obtained by the new quantum machines. It is therefore natural to ask ourselves some questions about these machines, in particular how do they operate, where are they physically and what is needed for their running. Actually, we must ask ourselves such questions because the news headlines – sometime bombastic - could lead us to understand quantum machines as actually existing supercomputers, able of solving in a few seconds some calculation problems that classical computers would take millennia to process. To answer questions, this work is proposing a review of existing Quantum Computers. In the panorama of Quantum Computing, we intend to investigate how the future of their infrastructures will be, such as the related basic algorithms. We will try to understand what their logistics will be, where logistics is understood as a new quantum "art of computing". Specific attention will be paid to the quantum advantage, the medium-term strategy related to design algorithms for the next generations of quantum computers. Such algorithms are required by a wide range of strategic applications. Currently, they are mainly simulation algorithms of quantum physics and chemistry, but also algorithms related to quadratic unconstrained binary optimization exist. Here we will illustrate in particular two methods: one based on abiabatic quantum computation, which is obtained through quantum annealing (D-Wave systems), the other based on arrays of quantum gates (IBM, Google and others). We will see, in greater detail, the simulations concerning the Variational Quantum Eigensolver method. Some notions related to qubits, quantum gates and their Hamiltonians will be proposed too. Besides talking of qubits, we will also report about qumodes. In fact, the art of quantum computing has two faces, one based on qubits, the other based on qumodes. Xanadu's Borealis quantum computer is an example of qumode computer. It is capable of developing a hybrid system, with GKP-type qubit emulation along with squeezed light. SpinQ's first three qubits desktop will also be mentioned. A hint of quantum communication will also be provided.
The year is 2022. Scientists and engineers inch ever closer to building a practical quantum computer. The excitement in the research community, that we might soon fulfil Feynman’s dream to leverage quantum mechanics in machines capable of exponentially quicker computation, is steadily growing. With promised revolutions in chemistry, condensed matter physics and machine learning, and an expected market of 1.8 billion USD by 2026, quantum computing fights for the spotlight amid international pandemics and climate catastrophe. But the journey ahead is not an easy one. Quantum computers, requiring precise control of extraordinarily delicate quantum systems, are unsurprisingly challenging to engineer and equally difficult to design. A demonstration of quantum advantage through the quantum solving of a practical problem faster than available classical means, remains a research ambition. At the forefront of this research are classical computers: the machines which crunch the numbers in our calculations, which interface with our quantum experiments, and which render this very document. Without them, quantum computation would have remained a fanciful whim on Feynman’s chalkboard. Behind the rapidly growing repertoire of quantum algorithms lies an equally impressive and expanding corpus of classical simulation strategies. This thesis is about utilising first generation quantum computers and predicting their behaviour using classical computers. It develops novel quantum algorithms to perform variational minimisation, Hamiltonian diagonalisation, and approximate circuit recompilation, all intendedly compatible with near-future machines. It also devises classical algorithms for efficiently simulating quantum variational algorithms, emulating quantum computers using high-performance supercomputing facilities, and showcases the author’s efforts in scientific software development. Incidentally, this thesis makes no direct use of current day quantum hardware facilities. We hope to convince the disappointed reader that such an endeavour is presently pointless.
In the field of ab initio polaritonic chemistry, the use of quantum computers may offer, in the long term, a promising computational pathway for simulating and exploring novel cavity-induced light-matter phenomena. Addressing such problems on emerging platforms, however, already presents significant challenges that need to be considered for near-term applications. Within the framework of the variational quantum eigensolver (VQE), a key question concerns the development of quantum circuit Ans\"atze capable of encoding strongly correlated electron-photon states at a reasonable resource cost. Motivated by this question, in this work we develop different Ansatz strategies covering not only conventional qubit-based approaches but also higher-dimensional information units. Specifically, we introduce a set of circuits tailored to three different quantum computing architectures: (i) qubits, (ii) qudits, and (iii) hybrid qubit-qumode units of information. All circuits are designed to be both compact and physically motivated, with a central element being the development of spin-adapted electron-photon entangling fragment circuits tailored to the native capabilities of each platform. Integrating the different Ans\"atze within the state-averaged VQE, we numerically benchmark the three strategies on a cavity-embedded H2 molecule using state-vector simulations. Our results show that each proposed Ansatz achieves high accuracy in encoding the ground, first, and second polaritonic eigenstates of the system, even reproducing characteristic light-matter phenomena such as light-induced avoided crossings and conical intersections. This prospective work, beyond introducing multiple computational strategies adapted to different platforms, also highlights the potential of higher-dimensional quantum architectures to address hybrid fermion-boson systems.
Quantum computing is a paradigm shift in computational science, where computational tasks that cannot be computationally tackled with binary computing systems can be tackled using quantum mechanics principles, including superposition, entanglement, and quantum interference. The present paper gives a comprehensive summary of the present condition of quantum computing, including the most advanced hardware systems, such as topological qubits, photonics processors, trapped-ion systems and superconducting qubits, and their corresponding advantages and technical challenges. Also, quantum algorithms, including the Quantum Approximate Optimization Algorithm (QAOA) and the variational quantum eigen solvers (VQE), include a summary of the first algorithm, Shor's factorization method, and the second algorithm, Grover's search algorithm. Applications of such algorithms are evaluated in such areas as financial modeling, machine learning, material science, drug discovery, and cryptography. Taking into account the information provided by large research facilities and technology firms, the conditions under which quantum systems can outperform conventional technology, such a concept as quantum advantage is given specific focus in this review. Moreover, this paper pays particular attention to the key barriers to effective implementation, such as qubit decoherence, large error rates, scalability issues, and the lack of fault-tolerant quantum devices that can be sold commercially. Regarding the noisy intermediate-scale quantum (NISQ) devices that are expected to be available in the near future, we discuss novel approaches to quantum error correction, especially surface code and concatenated code. The quantum software industry, such as IBM Qiskit, Google Cirq, and Microsoft Q, and an evaluation of quantum workforce and research facility readiness across the globe is also discussed. In the end, this work highlights essential potential future paths, such as standards for post-quantum cryptography, hybrid quantum-classical computation, quantum networking and the quantum internet, and the long-term aims for universal fault- tolerant quantum computers. The review should provide researchers, graduate students, and practitioners with a systematic and up-to-date base to understand the fast-growing discipline of quantum computing and its extensive implications on science and business.
Quantum computing represents a transformative approach to computation leveraging fundamental principles of quantum mechanics such as superposition, entanglement and interference to store and process information in ways that classical computers cannot achieve.This paper attempts to provide a bona fide bibliographic overview of the principles, computational models, hardware technologies, and potential applications of quantum computing.It analyses how quantum algorithms could outperform classical algorithms in cryptographic analysis, optimization, and big data analysis.The survey integrates viewpoints from recent works; it presents system architectures, software development challenges, and quantum-assisted applications in healthcare, artificial intelligence, cybersecurity, and industrial optimization.Meanwhile, it examines hybrid quantum-classical systems that would allow quantum technologies to be deployed in the near term skirted by the limitations currently posed by quantum hardware.Moreover, the paper discusses foremost hurdles in scaling, error-correction, and maintaining coherence.From reviewing the recent developments and whether possible, this present review work informs researchers, engineers, and practitioners alike on the current state, as well as on the long-run viability, of quantum computing.
Quantum Computing (QC) was born as a purely theoretical subject. Thus, in the beginning, the main category of researchers in the area was physicists and mathematicians. Since IBM introduced the first quantum computer, global interest in this emerging technology has been growing more and more, involving computer science and engineering researchers. Machine learning is another rapidly growing technology, used in a wide range of applications where traditional computing is not sufficient. The two technologies are merged in the thesis work. The different ways in which a combination of them can occur are discussed, as well as the explanation why there is not a single definition of Quantum Machine Learning (QML). Firstly, quantum computing is examined in depth. The basic elements of quantum computation theory, based on the fundamental principles of quantum mechanics, are introduced. The attention is focused on the qubit description on the Bloch sphere, the fundamental quantum gates, and examples of quantum algorithms. Therefore, the differences between quantum simulators and quantum processors are analysed, including the frameworks that provide tools to interact with them. Focusing on Quantum Machine Learning, the different definitions of the technology are described. There are four distinct approaches that are differentiated by the type of data used (quantum or classical) and by the device processing them (quantum or classical). In this thesis, QML is defined as the approach that uses classical data to feed a quantum device. The two possible methods for developing QML algorithms are: 1) to translate existing classical models into the terms of quantum computing; 2) to leave the existing models of classical machine learning completely, and explore new possible ways of doing QML to take full advantage of the physical characteristics of a quantum computer. The strategy adopted is the first one, thus it is used a state-of-the-art hybrid model of Quanvolutional Neural Network (QNN), composed of both quanvolutional and classical convolutional layers. The quanvolutional layers reproduce the same concept of the classical ones: they are made of quantum filters that generate feature maps through locally transforming input data. The transformation can be performed by variational quantum circuits or random quantum circuits. The specific field where Quantum Machine Learning is applied is image classification. The considered case study is a labeled dataset with X-ray images of lungs (labels: pneumonia, normal). The images are pre-processed and cropped for being used by quantum circuits, thus some information is lost. The main aim of the thesis is to understand whether and in which ways Quantum Machine Learning could bring advantages in the mentioned classification in terms of timing, accuracy, or dataset reduction, despite the large decrease in images' resolution. Different configurations of the hybrid model (with a different number of quantum filters and different settings of convolutional layers) are analysed and tested. The current quantum devices have many limitations, thus the time is still immature to observe a significant change in perspective. Nevertheless, the obtained results are promising for the future of QML in terms of dataset and hyperparameters reduction.
Over the past several decades, quantum information science has emerged to seek answers to the question: can we gain some advantage by storing, transmitting and processing information encoded in systems that exhibit unique quantum properties? Today it is understood that the answer is yes, and many research groups around the world are working towards the highly ambitious technological goal of building a quantum computer, which would dramatically improve computational power for particular tasks. A number of physical systems, spanning much of modern physics, are being developed for quantum computation. However, it remains unclear which technology, if any, will ultimately prove successful. Here we describe the latest developments for each of the leading approaches and explain the major challenges for the future. This review outlines the principles of operation of quantum computers and their elements. The theory of ideal computers that do not interact with the environment and are immune to quantum decohering processes is presented. Decohering processes in quantum computers are investigated. The review considers methods for correcting quantum computing errors arising from the decoherence of the state of the quantum computer, as well as possible methods for the suppression of the decohering processes. A brief enumeration of proposed quantum computer realizations concludes the review.