Quantum computing (QC) is an emerging computing paradigm with potential to revolutionize the field of computing. QC is a field that is quickly developing globally and has high barriers of entry. In this paper we explore both successful contributors to the field as well as wider QC community with the goal of understanding the backgrounds and training that helped them succeed. We gather data on 148 contributors to open-source quantum computing projects hosted on GitHub and survey 46 members of QC community. Our findings show that QC practitioners and enthusiasts have diverse backgrounds, with most of them having a PhD and trained in physics or computer science. We observe a lack of educational resources on quantum computing. Our goal for these findings is to start a conversation about how best to prepare the next generation of QC researchers and practitioners.
Unlike in classical computation, where the computation happens by manipulating bits, the fundamental computational unit in QC is qubit. A bit can have one of two states: 0 or 1. Similarly, qubit state is a unit vector in a two-dimensional complex vector space @cite_31 . A qubit state can be encoded in a state of a quantum mechanical object, for example as polarization of a single photon @cite_31 . The field of quantum computing is in a state of constant change, and is generally expected to continue changing in the foreseeable future. In the past few years multiple Near-term Intermediate-Scale Quantum (NISQ) hardware implementations have been developed @cite_16 and demonstrated to provide a potential for quantum speedups @cite_27 . Naturally, different implementations come with certain trade-offs. For example, trapped ion qubits are generally less noisy and offer better connectivity, whereas superconducting qubits offer faster gate clock speeds and more clear path to scalability @cite_20 . This diversity of hardware introduces an additional degree of complexity for the development of QC algorithms and software, forcing algorithm developers to stay aware of the trade-offs presented by hardware.
@cite_27 @cite_31 @cite_16 @cite_20
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Suppose we have a small quantum computer with only M qubits. Can such a device genuinely speed up certain algorithms, even when the problem size is much larger than M? Here we answer this question to the affirmative. We present a hybrid quantum-classical algorithm to solve 3SAT problems involving n>>M variables that significantly speeds up its fully classical counterpart. This question may be relevant in view of the current quest to build small quantum computers. Preface Acknowledgement Nomenclature and notation Part I. Fundamental Concepts: 1. Introduction and overview 2. Introduction to quantum mechanics 3. Introduction to computer science Part II. Quantum Computation: 4. Quantum circuits 5. The quantum Fourier transform and its applications 6. Quantum search algorithms 7. Quantum computers: physical realisation Part III. Quantum Information: 8. Quantum noise, open quantum systems, and quantum operations 9. Distance measurement for quantum information 10. Quantum error-correction 11. Entropy and information 12. Quantum information theory Appendix A. Notes on basic probability theory Appendix B. Group theory Appendix C. Approximating quantum gates: the Solvay-Kitaev theorem Appendix D. Number theory Appendix E. Public-key cryptography and the RSA cryptosystem Appendix F. Proof of Lieb's theorem References Index. Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably. NISQ devices will be useful tools for exploring many-body quantum physics, and may have other useful applications, but the 100-qubit quantum computer will not change the world right away --- we should regard it as a significant step toward the more powerful quantum technologies of the future. Quantum technologists should continue to strive for more accurate quantum gates and, eventually, fully fault-tolerant quantum computing. We run a selection of algorithms on two state-of-the-art 5-qubit quantum computers that are based on different technology platforms. One is a publicly accessible superconducting transmon device (www.research.ibm.com ibm-q) with limited connectivity, and the other is a fully connected trapped-ion system. Even though the two systems have different native quantum interactions, both can be programed in a way that is blind to the underlying hardware, thus allowing a comparison of identical quantum algorithms between different physical systems. We show that quantum algorithms and circuits that use more connectivity clearly benefit from a better-connected system of qubits. Although the quantum systems here are not yet large enough to eclipse classical computers, this experiment exposes critical factors of scaling quantum computers, such as qubit connectivity and gate expressivity. In addition, the results suggest that codesigning particular quantum applications with the hardware itself will be paramount in successfully using quantum computers in the future.