Authors :
Jayesh Hire; Vaidehi Gawande; Sagar Dhande
Volume/Issue :
Volume 9 - 2024, Issue 8 - August
Google Scholar :
https://tinyurl.com/4vvabvbw
Scribd :
https://tinyurl.com/33xhwken
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24AUG998
Abstract :
Current flight search platforms primarily
consider four essential factors when planning a trip:
departure/arrival dates, as well as the origin and
destination locations. However, when additional
parameters are added to this search, the problem shifts
from a simple to a complex search, as the engine must sift
through a massive dataset of flights, including
information on airlines, flight routes, fees, and more. To
address this challenge and improve flight search
efficiency amidst data-intensive and resource-demanding
environments, this paper proposes the use of Grover's
search algorithm. This algorithm is demonstrated as the
optimal solution for searches with increased constraints.
The paper highlights the practical application of Grover's
search algorithm across three datasets of assorted sizes,
as well as various quantum hardware and simulators
available in the NISQ era. Furthermore, this paper
provides an in-depth understanding of the complexity of
quantum circuit design, including the key phases of state
encoding and amplitude amplification. The effectiveness
of these approaches is evaluated through analysis of
execution times and quantum measurement results. The
aim is to showcase the potential of quantum computing in
revolutionizing real-world search tasks, particularly in
the realm of flight selection.
Keywords :
Quantum Computing, Quantum Search, Quantum Algorithm, Grovers Algorithm, Quantum Application.
References :
- Ladd, T. D., Jelezko, F., Laflamme, R., Nakamura, Y., Monroe, C., & O’Brien, J. L. (2010). Quantum computers. Nature 2010 464:7285, 464(7285), 45–53. https://doi.org/10.1038/nature08812
- Szalay, A. S., Gray, J., & Vandenberg, J. (n.d.). Petabyte Scale Data Mining: Dream or Reality?
- de Wolf, R. (2017). The Potential Impact of Quantum Computers on Society.
- Harrow, A. W., Hassidim, A., & Lloyd, S. (2009). Quantum Algorithm for Linear Systems of Equations. Physical Review Letters, 103(15), 150502. https://doi.org/10.1103/PhysRevLett.103.150502
- Shor, P. W. (2006). Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer. 41(2), 303–332. https://doi.org/10.1137/S0036144598347011
- Rivest, R. L., Shamir, A., & Adleman, L. (1978). A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 21(2), 120–126. https://doi.org/10.1145/359340.359342
- Grover, L. K. (1996). A fast quantum mechanical algorithm for database search.
- Yanofsky, N. S. (2007). AN INTRODUCTION TO QUANTUM COMPUTING.
- Nielsen, M. A. ., & Chuang, I. L. . (2023). Quantum computation and quantum information. Cambridge University Press.
- Adedoyin, A., Ambrosiano, J., Anisimov, P., et al. (2022). Quantum Algorithm Implementations for Beginners. ACM Trans. Quantum Comput., 3(4), Article 18. https://doi.org/10.1145/3517340
- Szabłowski, P. J., Paweł, B., Szabłowski, J., & Szabłowski, P. J. (2021). Understanding mathematics of Grover’s algorithm. Quantum Information Processing, 20, 191. https://doi.org/10.1007/s11128-021-03125-w
- IBM, Ibm quantum experience, 2023. Available: https: //quantum-computing.ibm.com/
- Google, Cirq, 2023. Available: https://quantumai. google/cirq.
- Bergholm, V., Izaac, J., Schuld, M., et al. (2022). PennyLane: Automatic differentiation of hybrid quantum-classical computations. PennyLaneAI. https://github.com/PennyLaneAI/pennylane/
- Steiger, D. S., Häner, T., & Troyer, M. (2018). ProjectQ: An Open Source Software Framework for Quantum Computing. https://doi.org/10.22331/q-2018-01-31-49
- Mandviwalla, A., Ohshiro, K., & Ji, B. (2018). Implementing Grover’s Algorithm on the IBM Quantum Computers.
- Johnstun, S., & van Huele, J.-F. (2021). Understanding and compensating for noise on IBM quantum computers. American Journal of Physics, 89(10), 935–942. https://doi.org/10.1119/10.0006204
Current flight search platforms primarily
consider four essential factors when planning a trip:
departure/arrival dates, as well as the origin and
destination locations. However, when additional
parameters are added to this search, the problem shifts
from a simple to a complex search, as the engine must sift
through a massive dataset of flights, including
information on airlines, flight routes, fees, and more. To
address this challenge and improve flight search
efficiency amidst data-intensive and resource-demanding
environments, this paper proposes the use of Grover's
search algorithm. This algorithm is demonstrated as the
optimal solution for searches with increased constraints.
The paper highlights the practical application of Grover's
search algorithm across three datasets of assorted sizes,
as well as various quantum hardware and simulators
available in the NISQ era. Furthermore, this paper
provides an in-depth understanding of the complexity of
quantum circuit design, including the key phases of state
encoding and amplitude amplification. The effectiveness
of these approaches is evaluated through analysis of
execution times and quantum measurement results. The
aim is to showcase the potential of quantum computing in
revolutionizing real-world search tasks, particularly in
the realm of flight selection.
Keywords :
Quantum Computing, Quantum Search, Quantum Algorithm, Grovers Algorithm, Quantum Application.