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All Journal Jurnal Pembangunan Pendidikan: Fondasi dan Aplikasi Jurnal Ekonomi dan Pendidikan Cakrawala Pendidikan Jurnal Kependidikan: Penelitian Inovasi Pembelajaran Socia : Jurnal Ilmu-Ilmu Sosial Efisiensi : Kajian Ilmu Administrasi Jurnal Pendidikan Akuntansi Indonesia JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Kompak : Jurnal Ilmiah Komputerisasi Akuntansi E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Penggunaan Media Sains Flipbook dalam Pembelajaran IPA di Sekolah Dasar Jurnal Akuntabilitas Manajemen Pendidikan Gravity : Jurnal Ilmiah Penelitian dan Pembelajaran Fisika Substansi: Sumber Artikel Akuntansi Auditing dan Keuangan Vokasi Islamic Economics Quotient KEUDA : JURNAL KAJIAN EKONOMI DAN KEUANGAN DAERAH Psychology, Evaluation, and Technology in Educational Research GERVASI: Jurnal Pengabdian kepada Masyarakat Social, Humanities, and Educational Studies (SHEs): Conference Series Buletin Penelitian Tanaman Rempah dan Obat Indonesian Journal of Natural Science Education MANAJEMEN Jurnal Komputer, Informasi dan Teknologi Kresna: Jurnal Riset dan Pengabdian Masyarakat Transformasi dan Inovasi : Jurnal Pengabdian Masyarakat Journal of Engineering, Electrical and Informatics (JEEI) Dinamika Proceedings of The International Conference on Business and Economics Jurnal Sains dan Ilmu Terapan Jurnal Penelitian Sistem Informasi Alkhidmah: Jurnal Pengabdian dan Kemitraan Masyarakat Aspirasi : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat JUSTITIABLE - Jurnal Hukum Universitas Bojonegoro Al Itimad: Jurnal Dakwah dan Pengembangan Masyarakat Islam JURNAL MULTIDISIPLIN ILMU AKADEMIK Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Siswanto Siswanto
Universitas Negeri Yogyakarta
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Journal : Journal of Engineering, Electrical and Informatics (JEEI)

 1 
EXPLORATION OF AMPLITUDE CODING CAPACITIES FOR Q-ML MODEL Unang Achlison; Dendy Kurniawan; Toni Wijanarko Adi Putra; Siswanto Siswanto
Journal of Engineering, Electrical and Informatics Vol 2 No 3 (2022): Oktober: Journal of Engineering, Electrical and Informatics:
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v2i3.916

Abstract

Quantum computing implements computation adopting environmental phantasm and the foundation of quantum mechanics to clear up the issues. This design of calculation has been demonstrated to serve the acceleration of some modern processing issues. Current evolution in quantum technology is emerging, and the application of learning design to this current instrument is developing. With enough prospects, the application of quantum development in the area of Machine Learning has come clear. This research develops a TensorFlow Quantum (TF-Q) software framework model for machine learning functions. The two models advanced the application of material coding techniques from amplitude coding to constructing a case in the quantum learning model. This study aimed to explore the scope of amplitude coding to serve enhanced case establishment in learning techniques and in-depth investigation of data sets that bring insight into the practice data adopting the “Variational Quantum Classifier” (VQ-C). The emergence of this current method raises the investigation of how best this tool can be adopted, the aim is to provide several analysis explanations for the element of quantum machine learning that can be applied given the constraints of the actual device. The results of this study indicate there are clear advantages to adopting amplitude coding over another technique as demonstrated by adopting the combination of quantum-humanistic neural networks in TF-Q. In addition, the different preprocessing steps can generate more aspect-affluent data while using VQ-C the no-charge lunch assumption dominance for quantum learning technique for humanistic models. The material even though conceal in quantum by unadvanced data preparation steps but involves new ways of understanding and appreciating these new methods. Future studies will lack expansion into multi-type of analysis models that are sufficiently advanced to be relevant in work similar to this.
EXPLORATION OF AMPLITUDE CODING CAPACITIES FOR Q-ML MODEL Unang Achlison; Dendy Kurniawan; Toni Wijanarko Adi Putra; Siswanto Siswanto
Journal of Engineering, Electrical and Informatics Vol. 2 No. 3 (2022): Oktober: Journal of Engineering, Electrical and Informatics:
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v2i3.916

Abstract

Quantum computing implements computation adopting environmental phantasm and the foundation of quantum mechanics to clear up the issues. This design of calculation has been demonstrated to serve the acceleration of some modern processing issues. Current evolution in quantum technology is emerging, and the application of learning design to this current instrument is developing. With enough prospects, the application of quantum development in the area of Machine Learning has come clear. This research develops a TensorFlow Quantum (TF-Q) software framework model for machine learning functions. The two models advanced the application of material coding techniques from amplitude coding to constructing a case in the quantum learning model. This study aimed to explore the scope of amplitude coding to serve enhanced case establishment in learning techniques and in-depth investigation of data sets that bring insight into the practice data adopting the “Variational Quantum Classifier” (VQ-C). The emergence of this current method raises the investigation of how best this tool can be adopted, the aim is to provide several analysis explanations for the element of quantum machine learning that can be applied given the constraints of the actual device. The results of this study indicate there are clear advantages to adopting amplitude coding over another technique as demonstrated by adopting the combination of quantum-humanistic neural networks in TF-Q. In addition, the different preprocessing steps can generate more aspect-affluent data while using VQ-C the no-charge lunch assumption dominance for quantum learning technique for humanistic models. The material even though conceal in quantum by unadvanced data preparation steps but involves new ways of understanding and appreciating these new methods. Future studies will lack expansion into multi-type of analysis models that are sufficiently advanced to be relevant in work similar to this.
Co-Authors Ade Novitasari Agustinus Budi Santoso Ahmad Muhlisin Alfiana Antoh Alfiani Purnama Dewi Dewi Ali Muhson Aliyah Rasyid Baswedan Amanda Tifani Anak Agung Istri Sri Wiadnyani Andi Kresna Jaya Anggit Permana Ani Widayati Anis Aprilia Arief Rahman Nur Asriyadin Asriyadin Azwar Wardiansyah Badrun Kartowagiran Bambang Setyo Panulisan Chellyana Kusuma Wardani Danang Danang Dedy Khaerudin Dendy Kurniawan Dian Normalitasari Purnama Diana Dayaningsih Diva Natasha Putri Donny Lukfiansah Dwi Aryanti Eka Ary Wibawa Eka Satria Wibawa Erni Maulidiawati F Fajarudin Filma Anggraini Galuh Oktaviani Greget Widhiati Heni Susilowati Heru Khoeruddin Indah Yuliana Ismani Ismani Jordan Napitupulu Laksmita Rosa Lutfi Anggraini M Murni M. Abdul Rohman Kartanagara Maya Utami Dewi Maya Utami Dewi Merinda Noorma Novida Siregar‬ Moch. Malik Al Firdaus Mochammad Imron Awalludin Muhamad Djazuli Nadya Tri Yuwinda Ngadirin Setiawan Nia Puspita Dewi Niken Dwi Rahayu Niken Purnamasari Nirwani Mintanawati Nurman Efrinson Tamba Puspita Aryani Rahma Sangkut Ramaita Ramaita Riko Firmansyah Ristiana Dwi Lestari Rizka Tri Alinse Rizqi Ilyasa Aghni Rosidah Rosidah Shazia Malik SITI KHOLIFAH Sondang Suriati Sri Haryati Sri Wahyuni Su-Chuan Liu Subali Subali Sukanti Sukanti Sukarno Sukarno Sumarsih Sumarsih Suyanto Suyanto Tasya Fajar Putri Tita Pratama Zebua Toni Wijanarko Adi Putra Unang Achlison Untung Bijaksana Virda Mar’atus Sholichah Wartini Wartini Widya Aryani Wiratno Wiratno Yolla Sukma Handayani Yulu Yanti Sitompul Yundy Hafizrianda YUNI ASTUTI Yusiran Yusiran, Yusiran Zufi Anidzar Arbani