no code implementations • 21 Jan 2024 • Sanjai Pathak, Ashish Mani, Mayank Sharma, Amlan Chatterjee
Although some optimization algorithms are proposed to deal with the changes in dynamic environments differently, there are still areas of improvement in existing algorithms due to limitations or drawbacks, especially in terms of locating and following the previously identified optima.
no code implementations • 19 Jul 2020 • Arit Kumar Bishwas, Ashish Mani, Vasile Palade
This paper proposes an optimized formulation of the parts of speech tagging in Natural Language Processing with a quantum computing approach and further demonstrates the quantum gate-level runnable optimization with ZX-calculus, keeping the implementation target in the context of Noisy Intermediate Scale Quantum Systems (NISQ).
no code implementations • 21 Sep 2019 • Arit Kumar Bishwas, Ashish Mani, Vasile Palade
We have investigated the run time computational complexity of the proposed quantum deep clustering framework and compared with the possible classical implementation.
no code implementations • 29 Apr 2018 • Arit Kumar Bishwas, Ashish Mani, Vasile Palade
In this paper, we have investigated the performance of support vector clustering algorithm implemented in a quantum paradigm for possible run-time improvements.
no code implementations • 4 Nov 2017 • Arit Kumar Bishwas, Ashish Mani, Vasile Palade
The Gaussian kernel is a very popular kernel function used in many machine learning algorithms, especially in support vector machines (SVMs).
no code implementations • 25 Apr 2017 • Arit Kumar Bishwas, Ashish Mani, Vasile Palade
We have shown that the multiclass support vector machine for big data classification with a quantum all-pair approach can be implemented in logarithm runtime complexity on a quantum computer.
no code implementations • 23 Dec 2016 • Nija Mani, Gursaran, Ashish Mani
However, canonical QEA is one of the few evolutionary algorithms, which uses a search operator with relatively large number of parameters.