The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Circuit-Based Quantum Random Access Memory for Classical Data
Open accessAbstract
A prerequisite for many quantum information processing tasks to truly surpass classical approaches is an efficient procedure to encode classical data in quantum superposition states. In this work, we present a circuit-based flip-flop quantum random access memory to construct a quantum database of classical information in a systematic and flexible way. For registering or updating classical data consisting of M entries, each represented by n bits, the method requires O(n) qubits and O(Mn) steps. With post-selection at an additional cost, our method can also store continuous data as probability amplitudes. As an example, we present a procedure to convert classical training data for a quantum supervised learning algorithm to a quantum state. Further improvements can be achieved by reducing the number of state preparation queries with the introduction of quantum forking.
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“For example, a recent circuit-based proposal for QRAM [113] shows an unavoidable linear cost on the number of states that would outweigh any logarithmic-time algorithm.”
From The prospects of quantum computing in computational molecular biology (2021) · cites this paper as [113]
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Cites
Quantum Computation and Quantum Information
Nielsen, M. A., Chuang, I., & Grover, L. K. (2002) · American Journal of Physics · doi:10.1119/1.1463744 · Cited by 21,895
Quantum computation and quantum information
Law, J. (2001) · ACM SIGSOFT Software Engineering Notes · doi:10.1145/505482.505499 · Cited by 20,450
A fast quantum mechanical algorithm for database search
Grover, L. K. (1996) · Proceedings of the twenty-eighth annual ACM symposium on Theory of computing - STOC '96 · doi:10.1145/237814.237866 · Cited by 8,830
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
Elementary gates for quantum computation
Barenco, A., Bennett, C. H., Cleve, R., DiVincenzo, D. P., Margolus, N., Shor, P., Sleator, T., Smolin, J. A., & Weinfurter, H. (1995) · Physical Review A · doi:10.1103/physreva.52.3457 · Cited by 4,311
Quantum Algorithm for Linear Systems of Equations
Harrow, A. W., Hassidim, A., & Lloyd, S. (2009) · Physical Review Letters · doi:10.1103/physrevlett.103.150502 · Cited by 3,325
Quantum Support Vector Machine for Big Data Classification
Rebentrost, P., Mohseni, M., & Lloyd, S. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.130503 · Cited by 1,951
Batens, D., & Bendegem, J. P. V. (1988) · Springer eBooks · doi:10.1007/978-94-009-2875-6 · Cited by 1,712
Quantum principal component analysis
Lloyd, S., Mohseni, M., & Rebentrost, P. (2014) · Nature Physics · doi:10.1038/nphys3029 · Cited by 1,385
Buhrman, H., Cleve, R., Watrous, J., & de Wolf, R. (2001) · Physical Review Letters · doi:10.1103/physrevlett.87.167902 · Cited by 1,190
Research works printed but not linked: 1
- Park, D. K., Sinayskiy, I., Fingerhuth, M., Petruccione, F. & Rhee, J. K. Quantum forking for fast weighted power summation. arXiv:1902.07959 [quant-ph] (2019). · arXiv
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Noisy intermediate-scale quantum algorithms
Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.015004 · Cited by 2,231
Quantum convolutional neural network for classical data classification
Hur, T., Kim, L., & Park, D. K. (2022) · Quantum Machine Intelligence · doi:10.1007/s42484-021-00061-x · Cited by 448
Self-citationThe bitter truth about gate-based quantum algorithms in the NISQ era
Leymann, F., & Barzen, J. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/abae7d · Cited by 251
A quantum deep convolutional neural network for image recognition
Li, Y., Zhou, R.-G., Xu, R., Luo, J., & Hu, W. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/ab9f93 · Cited by 225
The prospects of quantum computing in computational molecular biology
Outeiral, C., Strahm, M., Shi, J., Morris, G. M., Benjamin, S. C., & Deane, C. M. (2021) · WIREs Computational Molecular Science · doi:10.1002/wcms.1481 · Cited by 232
Metadata
- Authors
- Park, D. K., Petruccione, F., & Rhee, J.-K. K.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Corresponding author
- Rhee, June-Koo Kevin
- Venue
- Scientific Reports
- Published
- 2019-03-08
- Volume
- 9
- Issue
- 1
- Pages
- 3949-3949
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6408577
- OpenAlex
- W2909846583
- Cited by
- 151
- References
- 34
- Citations by year
- 2026: 11 · 2025: 28 · 2024: 22 · 2023: 22 · 2022: 26 · 2021: 24 · 2020: 15 · 2019: 3
- FWCI
- 7.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Research Foundation (2016R1A5A1008184)
- National Research Foundation of Korea (2016R1A5A1008184)
- Korea Advanced Institute of Science and Technology (2016R1A5A1008184)
- Iran Telecommunication Research Center
- Ministry of Science and ICT, South Korea (IITP-2018, 2016R1A5A1008184)
- Institute for Information and Communications Technology Promotion (IITP-2018-2015-0-00385, IITP-2018-2018-0-01402)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Keywords
- quantum random access memory, flip-flops, quantum superposition, post-selection
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