Circuit-Based Quantum Random Access Memory for Classical Data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

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

ABSTRACT

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. More... »

PAGES

3949

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-019-40439-3

DOI

http://dx.doi.org/10.1038/s41598-019-40439-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112641530

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30850658


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