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Optimization by VarQITE on Adaptive Variational Quantum Kolmogorov-Arnold Network
Hikaru Wakaura, Rahmat Mulyawan, Andriyan B. Suksmono
Quantum imaginary time evolution (QITE) is a powerful method to derive the ground states of the systems. Only the damping of quantum states leads it; hence, reaching the ground sta…
The effect of Quantum Time Crystal Computing to Quantum Machine Learning methods
Hikaru Wakaura, Andriyan B. Suksmono
Many body localization shows the robustness for external perturbations and time reversal symmetry on Time Crystal. This Time Crystal prolongs the coherence time, hence, it is used…
Enhanced Variational Quantum Kolmogorov-Arnold Network
Hikaru Wakaura, Rahmat Mulyawan, Andriyan B. Suksmono
The Kolmogorov-Arnold Network (KAN) places the trainable functions on the synapses rather than on the neurons. Existing quantum implementations either lack accuracy (Variational Qu…
Few-sample regression with an adaptively grown variational quantum Kolmogorov--Arnold network
Hikaru Wakaura, Rahmat Mulyawan, Andriyan B. Suksmono
Kolmogorov-Arnold networks place learnable one-dimensional functions on the edges of a network rather than fixed activations on its nodes, and several quantum realisations have bee…
A Quantum Approximate Optimization Method For Finding Hadamard Matrices
Andriyan Bayu Suksmono
Finding a Hadamard matrix of a specific order using a quantum computer can lead to a demonstration of practical quantum advantage. Earlier efforts using a quantum annealer were imp…
Derivation of Hamiltonians from time propagations using Born machines
Hikaru Wakaura, Andriyan Bayu Suksmono
Recently there are more promising qubit technology such as Majorana fermions Rydberg atoms and Silicon quantum dot have yet to be developed for realizing a quantum computer than Su…