Quantum Algorithm

Quantum Application. Source: ScienceDirect 2021.
Quantum Application. Source: ScienceDirect 2021.

A quantum computer is a machine that employs quantum mechanics to perform tasks that would be quite challenging for a machine based solely on classical physics laws to accomplish. Quantum computing's future applications include everything from cracking cryptographic systems to developing novel treatments. These applications are based on quantum algorithms, which run on a quantum computer and achieve a speedup or other efficiency advantage over any other classical method.

Quantum computers use quantum algorithms to outperform traditional computers. Cryptography, search and optimization, quantum system modelling, and solving huge systems of linear equations are all areas where quantum methods can be used.

Shor's algorithm for factoring and Grover's algorithm for searching an unstructured database or an unordered list are two of the most popular search methods. Shor's algorithms are faster (probably more powerful) than the well-known traditional factoring algorithm, the general number field sieve.

Discussion

  • Why do we need Quantum Algorithm?
    Quantum Algorithm: Applications. Source: ResearchGate 2021.
    Quantum Algorithm: Applications. Source: ResearchGate 2021.

    To do factoring, the well-known quantum algorithm is Shor's Algorithm. This method of quantum factoring takes about the same amount of time to function as a whole number of digits, while the older advanced algorithm takes about the exposure time. The notion that complex factoring is critical to functioning of modern cryptosystems, allows for most e-commerce. In these cryptosystems, the user can encrypt information and "eavesdroppers" can only determine the information by entering a large number. This strategy is useful for keeping information confidential as long as there are no quick fixes.

    Modeling of chemical and physical interactions is the another known quantum computer program. Because the amount of space required to track a quantum system increases rapidly with the number of particles, quantum mechanics are often difficult to duplicate using a standard computer. This has made understanding of the operation of many important systems and interactions, from advanced superconductors to photosynthesis, difficult for scientists and engineers. Quantum computers, for example, are expected to improve and accelerate drug research and industrial chemical processes by allowing 'in silico'(an experiment that is performed on computer) of chemical compounds and performance.

  • What are the Characteristics of a Quantum Algorithm?

    Quantum computing techniques can be accomplished using classical computer infrastructure by applying quantum algorithms. It is critical to analyze and categorise quantum computing technologies using quantum algorithms to describe characteristics. Parallelism, aggregate count of accessible qubits, topologies, algorithms for locating qubits, and qubit operations are all algorithmic aspects of quantum computing technology.

    Because parallel implementation of quantum gates is essential to either prevent or decrease qubit decoherence, parallelism is a key characteristic. Another characteristic that contributes to the quantum computer's stability and scalability is the total number of qubits available. The topologies of a quantum computer's architecture are the multiple conceivable groupings of distinct physical devices.

    The fundamental objective is architecture optimization, which allows for a seamless flow of data and information among the many physical parts of the system. The addressing mechanism for identifying a single qubit is quite complicated logically. This capability allows for a more detailed examination of qubit states in terms of quantum computer physical implementation. Moreover, in order to conduct any operation on qubits, they must be transported from the address where they are stored to the place where the qubit gates are acting on them.

  • How can the Quantum Algorithms be classified?

    Quantum Fourier Transform-based algorithms:

    Quantum Fourier transform is a quantum version of the discrete Fourier transform that is utilised in a variety of quantum techniques. In the field F2, the Hadamard transform is an example of a quantum Fourier transform over an n-dimensional vector space. Only a polynomial number of quantum gates are required to accomplish the quantum Fourier transform on a quantum computer.

    Amplitude Amplification-based algorithms:

    Amplitude amplification is a method that enables a quantum state's subdomain to be amplified. It frequently results in quadratic speedups when compared to traditional techniques. That can be regarded as a generalisation of  Grover's algorithm.

    Quantum Walk-based algorithms:

    Quantum walks are a powerful and general framework for creating rapid quantum algorithms. A quantum walk is based on the simulated coherent quantum evolution of a particle moving on a graph, similar to how a random walk method is based on the simulated motion of a particle moving randomly within some underlying graph structure.

    Hybrid quantum/classical algorithms:

    They are predicted to be the first useful applications for quantum computing, as they are well-suited for execution on NISQ (noisy intermediate-scale quantum) devices by integrating quantum computers with classical computers.

  • What is a Variational Quantum Algorithm?
    Variational Quantum Algorithm. Source: Nature 2022.
    Variational Quantum Algorithm. Source: Nature 2022.

    Variational Quantum Algorithm(VQA) is a Hybrid type of algorithm, as it is a combination quantum state preparation and measurement with classical optimization. This methodology can be used to locate low-energy eigenstates as well as to minimise any objective function.

    The variational method is a classical method for obtaining low energy states of a quantum system in quantum theory. The basic idea behind this method is to build a trial wave function (also known as an ansatz) as a function of some parameters, and then determine the values of these parameters that minimise the energy's expectation value in relation to these parameters. The expectation value serves as an upper bound on the energy of the ground state, and the reduced ansatz is an approximation to the lowest energy eigenstate. As a result, a new type of algorithm called as Variational Quantum Algorithm(VQA) has emerged.

  • How can a Quantum Algorithm be designed?

    Yao is a quantum computation research software that solves real-world challenges. Given the limits of noisy intermediate-scale quantum circuits in the near term, treating quantum devices as co-processors and enhancing their capabilities with classical computing resources is useful. VQAs have emerged as a potential study field. A quantum circuit with configurable gate parameters and a classical optimizer are frequently used in these techniques.

    For designing and testing quantum algorithms in these difficult domains, efficient quantum software is critical. Yao is an open-source framework for designing quantum algorithms with exceptional extensibility and efficiency. Quantum circuits can be programmed in a general and differentiable way with Yao. It simulates tiny to intermediate-sized quantum circuits with state-of-the-art performance that is important to near-term applications.

    The quantum block intermediate representation of quantum circuits, a built-in automated differentiation engine tuned for reversible computing, and batched quantum registers with GPU(graphics processing unit) acceleration are among the design principles and critical methodologies underpinning Yao.

    Besides increasing the number of qubits in experiments, further research objectives ask for quantum software with a low overhead for repetitive feedback control, convenient circuit structure modifications, and rapid gradient calculation.

  • How can Quantum Algorithms solve Dynamic Programming Problems?

    Quantum algorithms can be used to solve dynamic programming issues with finite and infinite horizons. For the similar jobs, the query complexity lower bounds for classical randomised algorithms are examined, and a polynomial separation between the query complexity of quantum algorithms and the best-case query complexity of conventional randomised algorithms can be demonstrated as a result. Quantum algorithms can provide a quadratic advantage in terms of the number of states and actions in the dynamic programming problem up to polylogarithmic factors. However, the speedup achieved comes at the expense of the introduction of other polynomial factors in the scaling of the algorithm, which contribute to the solution's precision. The quantum algorithm framework is concerned with discrete and combinatorial optimization issues that have traditionally been tackled with dynamic programming techniques.

Milestones

1992

The Deutsch–Jozsa algorithm is one of the first examples of a quantum algorithm that is exponentially faster than any possible deterministic classical algorithm. This is perhaps the earliest result in the computational complexity of quantum computers, proving that they were capable of performing some well-defined computational task more efficiently than any classical computer.

1994

Peter Shor of Bell Laboratories develops a quantum algorithm for factoring integers that has the potential to decrypt RSA-encrypted communications, a widely-used method for securing data transmissions. Isaac Chuang and Yoshihisa Yamamoto propose a quantum-optical realization of a quantum computer to implement Deutsch's algorithm. Their work introduces dual-rail encoding for photonic qubits.

1996

Lov Grover of Bell Laboratories invents the quantum database search algorithm. The quadratic speedup is not as dramatic as the speedup for factoring, discrete logs, or physics simulations. However, the algorithm can be applied to a much wider variety of problems. Any problem that has to be solved by random, brute-force search, can take advantage of this quadratic speedup (in the number of search queries).

1998

First experimental demonstration of a quantum algorithm. A working 2-qubit NMR quantum computer is used to solve Deutsch's problem by Jonathan A. Jones and Michele Mosca at Oxford University and shortly after by Isaac L. Chuang at IBM's Almaden Research Center and Mark Kubinec and the University of California, Berkeley together with coworkers at Stanford University and MIT.[Contributors 2005] First execution of Grover's algorithm on an NMR computer. Hidetoshi Nishimori & colleagues from Tokyo Institute of Technology showed that quantum annealing algorithm can perform better than classical simulated annealing.

2003

Implementation of the Deutsch–Jozsa algorithm on an ion-trap quantum computer at the University of Innsbruck.

2007

First use of Deutsch's Algorithm in a cluster state quantum computer.

2009

Quantum algorithm developed for differential equation systems.

2013

First resource analysis of a large-scale quantum algorithm using explicit fault-tolerant, error-correction protocols was developed for factoring.

2016

Physicists led by Rainer Blatt joined forces with scientists at MIT, led by Isaac Chuang, to efficiently implement Shor's algorithm in an ion-trap based quantum computer.

References

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  2. Cerezo, M. et al. 2021. "Variational Quantum Algorithms." Nature Reviews Physics, August. Accessed 2022-01-31.
  3. Chamola, Vinay. 2020. "Forthcoming Applications of Quantum Computing: Peeking into the Future." Institution of Engineering and Technology. Accessed 2022-01-31.
  4. Chinese Journal of Physics. 2021. "Quantum computation: Algorithms and Applications." ScienceDirect. Accessed 2022-01-31.
  5. Chuang, Isaac L, and Yoshihisa Yamamoto. 1995. "A Simple Quantum Computer." APS Physics. Accessed 2022-01-17.
  6. Chuang, Isaac L, Neil Gershenfeld, and Mark Kubinec. 1998. "Experimental Implementation of Fast Quantum Searching." APS Physics, April. Accessed 2022-01-17.
  7. Devitt, Simon J, Ashley M Stephens, William J Munro, and Kae Nemoto. 2013. "Requirements for fault-tolerant factoring on an atom-optics quantum computer." Nature Communications, October. Accessed 2022-01-17.
  8. Endo, Suguru, Zhenyu Cai, Simon C Benjamin, and Xiao Yuan. 2020. "Hybrid quantum-classical algorithms and quantum error mitigation." arxiv.org, November. Accessed 2022-02-03.
  9. Franco, Riccardo. 2008. "Quantum Amplitude Amplification Algorithm: An Explanation of Availability Bias." arxiv.org, November. Accessed 2022-01-18.
  10. Gill, Sukhpal Singh, Adarsh Kumar, Harvinder Singh, Manmeet Singh, Kamalpreet Kaur, Muhammad Usman, and Rajkumar Buyya. 2021. "Quantum Computing: A Taxonomy, System." arxiv.org, October. Accessed 2022-02-03.
  11. Gulde, Stephan, Mark Riebe, Gavin P. T Lancaster, Christoph Becher, Jürgen Eschner, Hartmut Häffner, Ferdinand Schmidt-Kaler, Isaac L Chuang, and Rainer Blatt. 2003. "Implementation of the Deutsch–Jozsa algorithm on an ion-trap quantum computer." Nature, January. Accessed 2022-01-17.
  12. Hardesty, Larry. 2009. "Quantum computing may actually be useful." MIT News, October. Accessed 2022-01-17.
  13. IBM. 2021. "Shor’s algorithm." IBM Quantum Composer. Accessed 2022-01-17.
  14. Jordan, Stephen. 2011. "Algebraic and Number Theoretic Algorithms." Quantum Algorithm Zoo. Accessed 2022-02-03.
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  16. Luo, Xiu-Zhe, Jin-Guo Liu, Pan Zhang, and Lei Wang. 2021. "Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design." arXiv.org, August. Accessed 2022-02-03.
  17. Marquit, Miranda. 2007. "First use of Deutsch's Algorithm in a cluster state quantum computer." Phys.org, April. Accessed 2022-01-17.
  18. Montanaro, Ashley. 2015. "Quantum algorithms: an overview." Nature, January. Accessed 2022-01-18.
  19. Monz, Thomas, Daniel Nigg, Esteban A Martinez, Matthias F Brandl, Philipp Schindler, Richard Rines, Shannon X Wang, Isaac L Chuang, and Rainer Blatt. 2016. "Realization of a scalable Shor algorithm." Science, March. Accessed 2022-01-17.
  20. P.W, Shor. 1994. "Algorithms for quantum computation: Discrete logarithms and factoring. Page: 124–134." IEEE. Accessed 2022-01-31.
  21. Peruzzo, Alberto. et al. 2014. "A variational eigenvalue solver on a photonic quantum processor." Nature communications. Accessed 2022-02-14.
  22. Press, Gil. 2021. "27 Milestones In The History Of Quantum Computing." Forbes, May 18. Accessed 2022-01-17.
  23. Ronagh, Pooya. 2019. "Quantum Algorithms for Solving Dynamic Programming Problems." TRIUMF Science Week. Accessed 2022-02-14.
  24. Wecker, Dave, Matthew B Hastings, and Matthias Troyer. 2015. "Towards Practical Quantum Variational Algorithms." APS Physics. Accessed 2022-02-14.
  25. Wikipedia. 2004. "Quantum algorithm." Wikipedia, the free encyclopedia. Accessed 2022-01-18.
  26. Wikipedia. 2005. "Timeline of quantum computing and communication." Wikipedia, the free encyclopedia, November. Accessed 2022-01-17.

Further Reading

  1. Nielsen, Michael A, and Isaac L Chuang. 2000. "Quantum Computation and Quantum Information." Accessed 2022-01-17.
  2. P.W, Shor. 1994. "Algorithms for quantum computation: Discrete logarithms and factoring." IEEE. Accessed 2022-01-31.
  3. Mosca, Michele. 2008. "Quantum Algorithms." arXiv.org. Accessed 2022-01-31.

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Devopedia. 2022. "Quantum Algorithm." Version 9, February 18. Accessed 2022-09-22. https://devopedia.org/quantum-algorithm
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Last updated on
2022-02-18 03:01:24
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