While addressing Vector Quantization (VQ) as a general paradigm for data representation, the paper adopts the K-winner Machine model as a case study, which provides a reference for analyzing both theoretical and implementation aspects. The design of vector quantizers often requires that the (often overlooked) dichotomy between ‘analogue’ modeling and ‘digital’ implementation be taken in account. In the case of digital VQ systems, optimal design can bring about NP-hard problems that prove intractable in terms of computational complexity. The paper discusses the possibility of using advanced paradigms such as Quantum Computing for digital optimization processes in order to overcome the limitations of conventional machinery. The presented research provides analytical criteria determining the relative advantages of conventional over quantum-computing approaches.
Quantum-computing optimization for K-Winner Machines
RIDELLA, SANDRO;ZUNINO, RODOLFO
2006-01-01
Abstract
While addressing Vector Quantization (VQ) as a general paradigm for data representation, the paper adopts the K-winner Machine model as a case study, which provides a reference for analyzing both theoretical and implementation aspects. The design of vector quantizers often requires that the (often overlooked) dichotomy between ‘analogue’ modeling and ‘digital’ implementation be taken in account. In the case of digital VQ systems, optimal design can bring about NP-hard problems that prove intractable in terms of computational complexity. The paper discusses the possibility of using advanced paradigms such as Quantum Computing for digital optimization processes in order to overcome the limitations of conventional machinery. The presented research provides analytical criteria determining the relative advantages of conventional over quantum-computing approaches.
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