Circuit-Based Quantum Random Access Memory for Classical Data
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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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A recent circuit-based proposal for QRAM shows an unavoidable linear cost on the number of states that would outweigh any logarithmic-time algorithm.
“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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On the quantum simulation of complex networks
Magano, D., Moutinho, J. P., & Coutinho, B. C. (2023) · SciPost Physics Core · doi:10.21468/scipostphyscore.6.3.058 · Cited by 6 · see citing works
Compact and classically preprocessed data-loading quantum circuit as a quantum random access memory
Duan, B., & Hsieh, C. (2024) · Physical Review A · doi:10.1103/physreva.110.012616 · Cited by 6 · see citing works
Quantum circuit generation for amplitude encoding using a transformer decoder
Daimon, S., & Matsushita, Y. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.22.l041001 · Cited by 6 · see citing works
Manko, S., & Frolovtsev, D. N. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-03875-5 · Cited by 6 · see citing works
Quantum-effective exact multiple patterns matching algorithms for biological sequences
Soni, K. K., & Rasool, A. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.957 · Cited by 6 · see citing works
Quantum density peak clustering
Magano, D., Buffoni, L., & Omar, Y. (2023) · Quantum Machine Intelligence · doi:10.1007/s42484-022-00090-0 · Cited by 6 · see citing works
Optimizing Quantum Space Using Spooky Pebble Games
Quist, A.-J., & Laarman, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-38100-3_10 · Cited by 6 · see citing works
Zhang, S., Wu, J., & Li, L. (2023) · Physical Review A · doi:10.1103/physreva.108.022603 · Cited by 7 · see citing works
Quantum algorithm for smoothed particle hydrodynamics
Au-Yeung, R., Williams, A. J., Kendon, V. M., & Lind, S. (2023) · Computer Physics Communications · doi:10.1016/j.cpc.2023.108909 · Cited by 8 · see citing works
Miyamoto, K., Yamazaki, S., Uchida, F., Fujisawa, K., & Yoshida, N. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013200 · Cited by 9 · see citing works
Quantum Multiplexer Simplification for State Preparation
de Carvalho, J. A., Batista, C. A., Veras, T. M. L., Araujo, I. F., & da Silva, A. J. (2025) · ACM Transactions on Quantum Computing · doi:10.1145/3748260 · Cited by 5 · see citing works
Practicality of training a quantum-classical machine in the noisy intermediate-scale quantum era
Dutta, T., Jin, A., Huihong, C. L., Latorre, J. I., & Mukherjee, M. (2025) · iScience · doi:10.1016/j.isci.2025.113058 · Cited by 5 · see citing works
Linear Decomposition of Approximate Multicontrolled Single Qubit Gates
Silva, J. D. S., Azevedo, T. M. D., Araujo, I. F., & da Silva, A. J. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3471905 · Cited by 5 · see citing works
From Problem to Solution: A General Pipeline to Solve Optimisation Problems on Quantum Hardware
Rohe, T., Grätz, S., Kölle, M., Zielinski, S., Stein, J., & Linnhoff–Popien, C. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-84460-7_2 · Cited by 5 · see citing works
On new PageRank computation methods using quantum computing
Chapuis-Chkaiban, T., Toffano, Z., & Valiron, B. (2023) · Quantum Information Processing · doi:10.1007/s11128-023-03856-y · Cited by 5 · see citing works
Quantum Feature Selection with Variance Estimation
Poggiali, A., Bernasconi, A., Berti, A., Corso, G. M. D., & Guidotti, R. (2023) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2023.es2023-99 · Cited by 5 · see citing works
QFCNN: Quantum Fourier Convolutional Neural NetworkPreprint
Shen, F., & Liu, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.10421 · Cited by 5 · see citing works
A quantum random access memory (QRAM) using a polynomial encoding of binary strings
Mukhopadhyay, P. (2025) · Scientific Reports · doi:10.1038/s41598-025-95283-5 · Cited by 5 · see citing works
Emerging Paradigm of Quantum Machine Learning: Knowledge Insights and Future Prospects
Rawat, K. S., & Sharma, T. K. (2026) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2026.3669545 · Cited by 4 · see citing works
Detailed Account of Complexity for Implementation of Circuit-Based Quantum Algorithms
Cardoso, F. R., Akamatsu, D. Y., Campo, V. L., Duzzioni, E. I., Jaramillo, A., & Villas-Bôas, C. J. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.731007 · Cited by 6 · see citing works
Optimization of Quantum Read-Only Memory Circuits
Phalak, K., Alam, M., Saki, A. A., Topaloglu, R. O., & Ghosh, S. (2022) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd56317.2022.00027 · Cited by 5 · see citing works
Automated Quantum Memory Compilation with Improved Dynamic Range
Sinha, A., Henderson, E. R., Henderson, J. M., & Thornton, M. A. (2022) · IEEE/ACM International Workshop on Quantum Computing Software (QCS) · doi:10.1109/qcs56647.2022.00008 · Cited by 4 · see citing works
Quantum subroutine for variance estimation: algorithmic design and applications
Bernasconi, A., Berti, A., Corso, G. M. D., Guidotti, R., & Poggiali, A. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00213-9 · Cited by 4 · see citing works
Multilingual multi-task quantum transfer learning
Buonaiuto, G., Guarasci, R., Pietro, G. D., & Esposito, M. (2025) · Quantum Machine Intelligence · doi:10.1007/s42484-025-00260-w · Cited by 3 · see citing works
Quantum-classical reinforcement learning for decoding noisy classical parity information
Park, D. K., Park, J., & Rhee, J.-K. K. (2020) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00019-5 · Cited by 3 · see citing works
Self-citationBerti, A. (2023) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2023.es2023-93 · Cited by 3 · see citing works
T-depth-optimized quantum search with quantum data-access machine
Park, J. J., Baek, K., Kim, M. S., Nha, H., Kim, J., & Bang, J. (2023) · Quantum Science and Technology · doi:10.1088/2058-9565/ad04e5 · Cited by 3 · see citing works
Song, W., Lim, Y., Jeong, K., Lee, J., Park, J. J., Kim, M. S., & Bang, J. (2022) · New Journal of Physics · doi:10.1088/1367-2630/ac94ef · Cited by 3 · see citing works
- 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 › Computer Science › Artificial Intelligence › 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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