Quantum Classification Outside the Promised Class
This paper studies the important problem of quantum classification of Boolean functions from an entirely novel perspective. Typically, quantum classification algorithms allow us to classify functions with a probability of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML&...
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MDPI AG
2025-06-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/6/228 |
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| author | Theodore Andronikos Constantinos Bitsakos Konstantinos Nikas Georgios I. Goumas Nectarios Koziris |
| author_facet | Theodore Andronikos Constantinos Bitsakos Konstantinos Nikas Georgios I. Goumas Nectarios Koziris |
| author_sort | Theodore Andronikos |
| collection | DOAJ |
| description | This paper studies the important problem of quantum classification of Boolean functions from an entirely novel perspective. Typically, quantum classification algorithms allow us to classify functions with a probability of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.0</mn></mrow></semantics></math></inline-formula>, if we are promised that they meet specific unique properties. The primary objective of this study is to explore whether it is feasible to obtain any insights when the input function deviates from the promised class. For concreteness, we use a recently introduced quantum algorithm that is designed to classify a large class of imbalanced Boolean functions with probability <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.0</mn></mrow></semantics></math></inline-formula> using just a single oracular query. First, we establish a completely new concept characterizing “nearness” between Boolean functions. Utilizing this concept, we show that, as long as the unknown function is close enough to the promised class, it is still possible to obtain useful information about its behavioral pattern from the classification algorithm. In this regard, the current study is among the first to provide evidence that shows how useful it is to apply quantum classification algorithms to functions outside the promised class in order to get a glimpse of important information. |
| format | Article |
| id | doaj-art-c65fc22f342043c0a6bf68238f56b324 |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-c65fc22f342043c0a6bf68238f56b3242025-08-20T03:27:18ZengMDPI AGComputers2073-431X2025-06-0114622810.3390/computers14060228Quantum Classification Outside the Promised ClassTheodore Andronikos0Constantinos Bitsakos1Konstantinos Nikas2Georgios I. Goumas3Nectarios Koziris4Department of Informatics, Ionian University, 7 Tsirigoti Square, 49100 Corfu, GreeceComputing Systems Laboratory, National Technical University of Athens, 15772 Zografou, GreeceComputing Systems Laboratory, National Technical University of Athens, 15772 Zografou, GreeceComputing Systems Laboratory, National Technical University of Athens, 15772 Zografou, GreeceComputing Systems Laboratory, National Technical University of Athens, 15772 Zografou, GreeceThis paper studies the important problem of quantum classification of Boolean functions from an entirely novel perspective. Typically, quantum classification algorithms allow us to classify functions with a probability of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.0</mn></mrow></semantics></math></inline-formula>, if we are promised that they meet specific unique properties. The primary objective of this study is to explore whether it is feasible to obtain any insights when the input function deviates from the promised class. For concreteness, we use a recently introduced quantum algorithm that is designed to classify a large class of imbalanced Boolean functions with probability <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.0</mn></mrow></semantics></math></inline-formula> using just a single oracular query. First, we establish a completely new concept characterizing “nearness” between Boolean functions. Utilizing this concept, we show that, as long as the unknown function is close enough to the promised class, it is still possible to obtain useful information about its behavioral pattern from the classification algorithm. In this regard, the current study is among the first to provide evidence that shows how useful it is to apply quantum classification algorithms to functions outside the promised class in order to get a glimpse of important information.https://www.mdpi.com/2073-431X/14/6/228quantum algorithmBoolean functionpatternoraclethe Deutsch–Jozsa algorithmclassification |
| spellingShingle | Theodore Andronikos Constantinos Bitsakos Konstantinos Nikas Georgios I. Goumas Nectarios Koziris Quantum Classification Outside the Promised Class Computers quantum algorithm Boolean function pattern oracle the Deutsch–Jozsa algorithm classification |
| title | Quantum Classification Outside the Promised Class |
| title_full | Quantum Classification Outside the Promised Class |
| title_fullStr | Quantum Classification Outside the Promised Class |
| title_full_unstemmed | Quantum Classification Outside the Promised Class |
| title_short | Quantum Classification Outside the Promised Class |
| title_sort | quantum classification outside the promised class |
| topic | quantum algorithm Boolean function pattern oracle the Deutsch–Jozsa algorithm classification |
| url | https://www.mdpi.com/2073-431X/14/6/228 |
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