Acoustic metamaterials for realizing a scalable multiple phi-bit unitary transformation
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Department of Materials Science and Engineering, University of ArizonaDepartment of Computer Science, The University of Arizona
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2024-02-05
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American Institute of Physics Inc.Citation
K. Runge, P. A. Deymier, M. A. Hasan, T. D. Lata, J. A. Levine; Acoustic metamaterials for realizing a scalable multiple phi-bit unitary transformation. AIP Advances 1 February 2024; 14 (2): 025010. https://doi.org/10.1063/5.0188462Journal
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© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution license.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
The analogy between acoustic modes in nonlinear metamaterials and quantum computing platforms constituted of correlated two-level systems opens new frontiers in information science. We use an inductive procedure to demonstrate scalable initialization of and scalable unitary transformations on superpositions of states of multiple correlated logical phi-bits, classical nonlinear acoustic analog of qubits. A multiple phi-bit state representation as a complex vector in a high-dimensional, exponentially scaling Hilbert space is shown to correspond with the state of logical phi-bits represented in a low-dimensional linearly scaling physical space of an externally driven acoustic metamaterial. Manipulation of the phi-bits in the physical space enables the implementation of a non-trivial multiple phi-bit unitary transformation that scales exponentially. This scalable transformation operates in parallel on the components of the multiple phi-bit complex state vector, requiring only a single physical action on the metamaterial. This work demonstrates that acoustic metamaterials offer a viable path toward achieving massively parallel information processing capabilities that can challenge current quantum computing paradigms. © 2024 Author(s).Note
Open access articleISSN
2158-3226Version
Final Published Versionae974a485f413a2113503eed53cd6c53
10.1063/5.0188462
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Except where otherwise noted, this item's license is described as © 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution license.