Using Quantum Machine Learning to Resolve Ambiguity in Aramaic

 This paper studies the semantic ambiguity inherent in Ancient Aramaic from polysemy. To fix the loss of nuance in translations, we used a new framework based on Quantum Natural Language Processing (QNLP). It uses Variational Quantum Machine Learning (VQML) with a 10-qubit architecture and 300 optimization cycles. The model found a temporal-hierarchical interpretation for qaddamay with a confidence level of 96.15%. In contrast, Bar had a confidence level of 34.5% due to the scarcity of data in ancient corpora. This work connects quantum algorithms to more transparency in the study of historical manuscripts.

Available at Using Quantum Machine Learning to Resolve Ambiguity in Aramaic | European Journal of Applied Science, Engineering and Technology

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CV

CV en Español En 1993 obtiene la licenciatura en Física en la Universidad Central de Venezuela, y posteriormente obtiene el Doctorado en Ciencias Química en la Universidad Complutense de Madrid en 1996, gracias a una beca otorgada por la Agencia de Cooperación Internacional AECI - España. El Postdoctorado lo realiza en Venezuela en el Instituto Venezolano de Investigaciones Científicas - IVIC (1997 hasta 1999), e inmediatamente después en Estados Unidos, en Rice University - Texas (1999-2000). Desde el 2001 hasta Junio de 2007 se desempeño como Investigador Asociado en el Centro de Cálculo Científico Universidad de Los Andes en Mérida - Venezuela. Actualmente es Investigador Asociado en la Fundación Instituto de Estudios Avanzados – IDEA en Baruta, Venezuela. CV in English Raul Isea is currently an associate researcher at the Fundación Instituto de Estudios Avanzados IDEA in Caracas-Venezuela. He got the Ph.D in Chemistry ( Universidad Complutense de Madrid –Spain) in 1996 and the B.S...
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