Quantum Optics based Algorithm for Measuring the Similarity between Images Article Swipe
Vivek Mehta
,
Sonali Jana
,
Utpal Roy
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.09789
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.09789
We report an algorithm, based on quantum optics formulation, where a coherent state is used as the elementary quantum resource for the image representation. We provide an architecture with constituent optical elements in linear order with respect to the image resolution. The obtained phase-distributed multimode coherent state is fed into an image retrieval scheme and we identify the appropriate laser intensity parameter for similarity measurement. The use of the principle of quantum superposition in the similarity measurement protocol enables us to encode multiple input images. We demonstrate the viability of the protocol through an objective quality assessment of images by adding consecutive layers of noises. The results are in good agreement with the expected outcome. The image distortion-sensitivity analysis of the metric establishes the further merit of the model. Our quantum algorithm has wider applicability also in supervised machine learning tasks.
Related Topics
Concepts
Superposition principle
Algorithm
Metric (unit)
Computer science
Distortion (music)
Quantum
Figure of merit
Similarity (geometry)
Quantum computer
Quantum superposition
Image quality
Quantum imaging
Quantum algorithm
Artificial intelligence
Image (mathematics)
Mathematics
Computer vision
Quantum error correction
Physics
Quantum mechanics
Mathematical analysis
Amplifier
Economics
Bandwidth (computing)
Operations management
Computer network
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.09789
- https://arxiv.org/pdf/2307.09789
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384918953
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4384918953Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2307.09789Digital Object Identifier
- Title
-
Quantum Optics based Algorithm for Measuring the Similarity between ImagesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-19Full publication date if available
- Authors
-
Vivek Mehta, Sonali Jana, Utpal RoyList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.09789Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2307.09789Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2307.09789Direct OA link when available
- Concepts
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Superposition principle, Algorithm, Metric (unit), Computer science, Distortion (music), Quantum, Figure of merit, Similarity (geometry), Quantum computer, Quantum superposition, Image quality, Quantum imaging, Quantum algorithm, Artificial intelligence, Image (mathematics), Mathematics, Computer vision, Quantum error correction, Physics, Quantum mechanics, Mathematical analysis, Amplifier, Economics, Bandwidth (computing), Operations management, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.also | 135 |
| abstract_inverted_index.good | 109 |
| abstract_inverted_index.into | 49 |
| abstract_inverted_index.used | 14 |
| abstract_inverted_index.with | 28, 35, 111 |
| abstract_inverted_index.based | 4 |
| abstract_inverted_index.image | 22, 39, 51, 116 |
| abstract_inverted_index.input | 83 |
| abstract_inverted_index.laser | 59 |
| abstract_inverted_index.merit | 125 |
| abstract_inverted_index.order | 34 |
| abstract_inverted_index.state | 12, 46 |
| abstract_inverted_index.where | 9 |
| abstract_inverted_index.wider | 133 |
| abstract_inverted_index.adding | 100 |
| abstract_inverted_index.encode | 81 |
| abstract_inverted_index.images | 98 |
| abstract_inverted_index.layers | 102 |
| abstract_inverted_index.linear | 33 |
| abstract_inverted_index.metric | 121 |
| abstract_inverted_index.model. | 128 |
| abstract_inverted_index.optics | 7 |
| abstract_inverted_index.report | 1 |
| abstract_inverted_index.scheme | 53 |
| abstract_inverted_index.tasks. | 140 |
| abstract_inverted_index.enables | 78 |
| abstract_inverted_index.further | 124 |
| abstract_inverted_index.images. | 84 |
| abstract_inverted_index.machine | 138 |
| abstract_inverted_index.noises. | 104 |
| abstract_inverted_index.optical | 30 |
| abstract_inverted_index.provide | 25 |
| abstract_inverted_index.quality | 95 |
| abstract_inverted_index.quantum | 6, 18, 71, 130 |
| abstract_inverted_index.respect | 36 |
| abstract_inverted_index.results | 106 |
| abstract_inverted_index.through | 92 |
| abstract_inverted_index.analysis | 118 |
| abstract_inverted_index.coherent | 11, 45 |
| abstract_inverted_index.elements | 31 |
| abstract_inverted_index.expected | 113 |
| abstract_inverted_index.identify | 56 |
| abstract_inverted_index.learning | 139 |
| abstract_inverted_index.multiple | 82 |
| abstract_inverted_index.obtained | 42 |
| abstract_inverted_index.outcome. | 114 |
| abstract_inverted_index.protocol | 77, 91 |
| abstract_inverted_index.resource | 19 |
| abstract_inverted_index.agreement | 110 |
| abstract_inverted_index.algorithm | 131 |
| abstract_inverted_index.intensity | 60 |
| abstract_inverted_index.multimode | 44 |
| abstract_inverted_index.objective | 94 |
| abstract_inverted_index.parameter | 61 |
| abstract_inverted_index.principle | 69 |
| abstract_inverted_index.retrieval | 52 |
| abstract_inverted_index.viability | 88 |
| abstract_inverted_index.algorithm, | 3 |
| abstract_inverted_index.assessment | 96 |
| abstract_inverted_index.elementary | 17 |
| abstract_inverted_index.similarity | 63, 75 |
| abstract_inverted_index.supervised | 137 |
| abstract_inverted_index.appropriate | 58 |
| abstract_inverted_index.consecutive | 101 |
| abstract_inverted_index.constituent | 29 |
| abstract_inverted_index.demonstrate | 86 |
| abstract_inverted_index.establishes | 122 |
| abstract_inverted_index.measurement | 76 |
| abstract_inverted_index.resolution. | 40 |
| abstract_inverted_index.architecture | 27 |
| abstract_inverted_index.formulation, | 8 |
| abstract_inverted_index.measurement. | 64 |
| abstract_inverted_index.applicability | 134 |
| abstract_inverted_index.superposition | 72 |
| abstract_inverted_index.representation. | 23 |
| abstract_inverted_index.phase-distributed | 43 |
| abstract_inverted_index.distortion-sensitivity | 117 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.0931377 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |