Circuit-Based Quantum Random Access Memory for Classical Data

Daniel K. Park, Francesco Petruccione, June Koo Kevin Rhee

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)


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.

Original languageEnglish
Article number3949
JournalScientific reports
Issue number1
Publication statusPublished - 2019 Dec 1

Bibliographical note

Funding Information:
This research is supported by the National Research Foundation of Korea (Grant No. 2016R1A5A1008184), by the MSIT, Korea, under the ITRC support program (IITP-2018-2015-0-00385 and IITP-2018-2018-0-01402), by the KI Science Technology Leading Primary Research Program of KAIST, by Brain Korea 21 Plus, and by the South African Research Chair Initiative of the Department of Science and Technology and the National Research Foundation. We thank Maria Schuld and Carsten Blank for stimulating discussions.

Publisher Copyright:
© 2019, The Author(s).

All Science Journal Classification (ASJC) codes

  • General


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