1.7 Summary

In this first chapter we looked at what is motivating the recent interest in quantum computers. The lone 1s and 0s of classical computing bits are extended and complemented by the infinite states of qubits, also known as quantum bits. The properties of superposition and entanglement give us access to many dimensions of working memory that are unavailable to classical computers.

Industry use cases for quantum computing are nascent but the areas where experts believe it will be applicable sooner are chemistry, materials science, and financial services. AI is another area where quantum may boost performance for some kinds of calculations.

There has been confusion in traditional and social media about the interplay of security, information encryption, and quantum computing. The major areas of misunderstanding are the necessary performance requirements and the timeline.

In the next chapter, we look at classical bit-based computing to more precisely and technically explore how quantum computing may help us attack problems that are otherwise impossible today. In chapter 3 through chapter 6 we work through the mathematics necessary for you to see how quantum computing works. There is a lot to cover, but it is worth it to be able to go deeper than a merely superficial understanding of the ‘‘whats,’’ ‘‘hows,’’ and ‘‘whys’’ of quantum computing.

References

[1]

Lee Braine et al. Quantum Algorithms for Mixed Binary Optimization applied to Transaction Settlement. 2019. url: https://arxiv.org/abs/1910.05788.

[2]

Yudong Cao et al. ‘‘Quantum Chemistry in the Age of Quantum Computing’’. In: Chemical Reviews (2019).

[3]

Daniel J. Egger et al. Credit Risk Analysis using Quantum Computers. 2019. url: https://arxiv.org/abs/1907.03044.

[4]

FermiLab. What is the number of atoms in the world? 2014. url: https://www.fnal.gov/pub/science/inquiring/questions/atoms.html.

[5]

Richard P. Feynman. ‘‘Simulating Physics with Computers’’. In: International Journal of Theoretical Physics 21.6 (June 1, 1982), pp. 467–488.

[6]

Peter Furness. ‘‘Applications of Monte Carlo Simulation in marketing analytics’’. In: Journal of Direct, Data and Digital Marketing Practice 13 (2 Oct. 27, 2011).

[7]

Aram W. Harrow, Avinatan Hassidim, and Seth Lloyd. ‘‘Quantum Algorithm for Linear Systems of Equations’’. In: Physical Review Letters 103 (15 Oct. 2009), p. 150502.

[8]

Vojtěch Havlíček et al. ‘‘Supervised learning with quantum-enhanced feature spaces’’. In: Nature 567.7747 (Mar. 1, 2019), pp. 209–212.

[9]

David B Hertz. ‘‘Risk Analysis in Capital Investment’’. In: Harvard Business Review (Sept. 1979).

[10]

Abhinav Kandala et al. ‘‘Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets’’. In: Nature 549 (Sept. 13, 2017), pp. 242–247.

[11]

Rachel Link. How Much Caffeine Do Coke and Diet Coke Contain? 2018. url: https://www.healthline.com/nutrition/caffeine-in-coke.

[12]

Ashley Montanaro. ‘‘Quantum speedup of Monte Carlo methods’’. In: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471.2181 (2015), p. 20150301.

[13]

Michele Mosca and Marco Piani. Quantum Threat Timeline. 2019. url: https://globalriskinstitute.org/publications/quantum-threat-timeline/.

[14]

John Preskill. Quantum Computing in the NISQ era and beyond. url: https://arxiv.org/abs/1801.00862.

[15]

R. L. Rivest, A. Shamir, and L. Adleman. ‘‘A Method for Obtaining Digital Signatures and Public-key Cryptosystems’’. In: Commun. ACM 21.2 (Feb. 1978), pp. 120–126.

[16]

Nikitas Stamatopoulos et al. Option Pricing using Quantum Computers. 2019. url: https://arxiv.org/abs/1905.02666.

[17]

Ewin Tang. A quantum-inspired classical algorithm for recommendation systems. 2019. url: https://arxiv.org/pdf/1807.04271.pdf.

[18]

Giacomo Torlai et al. Precise measurement of quantum observables with neural-network estimators. url: https://arxiv.org/abs/1910.07596.

[19]

P. Wittek. Quantum Machine Learning. What quantum computing means to data mining. Elsevier Science, 2016.

[20]

Stefan Woerner and Daniel J. Egger. ‘‘Quantum risk analysis’’. In: npj Quantum Information 5 (1 Feb. 8, 2019), pp. 198–201.