Quantum Locally Testable Codes

We initiate the study of quantum Locally Testable Codes (qLTCs). We provide a definition together with a simplification, denoted sLTCs, for the special case of stabilizer codes, together with some basic results using those definitions. The most crucial parameter of such codes is their soundness, $R(\delta)$, namely, the probability that a randomly chosen constraint is violated as a function of the distance of a word from the code ($\delta$, the relative distance from the code, is called the proximity). We then proceed to study limitations on qLTCs. In our first main result we prove a surprising, inherently quantum, property of sLTCs: for small values of proximity, the better the small-set expansion of the interaction graph of the constraints, the less sound the qLTC becomes. This phenomenon, which can be attributed to monogamy of entanglement, stands in sharp contrast to the classical setting. The complementary, more intuitive, result also holds: an upper bound on the soundness when the code is defined on poor small-set expanders (a bound which turns out to be far more difficult to show in the quantum case). Together we arrive at a quantum upper-bound on the soundness of stabilizer qLTCs set on any graph, which does not hold in the classical case. Many open questions are raised regarding what possible parameters are achievable for qLTCs. In the appendix we also define a quantum analogue of PCPs of proximity (PCPPs) and point out that the result of Ben-Sasson et. al. by which PCPPs imply LTCs with related parameters, carries over to the sLTCs. This creates a first link between qLTCs and quantum PCPs.Comment: Some of the results presented here appeared in an initial form in our quant-ph submission arXiv:1301.3407. This is a much extended and improved version. 30 pages, no figure

Red Onion Customer Relationship Management System Business Process Design Using BPR LC Method

Customer Relationship Management merupakan sistem yang membantu proses bisnis dalam mengelola hubungan antara perusahaan atau organisasi dengan pelanggan. Akan tetapi Customer Relationship (CRM) jarang ditemui dalam sektor pertanian, terutama pada pertanian bawang merah di Jawa Tengah. Penelitian ini bertujuan untuk merekayasa ulang dan memperbarui proses bisnis yang sedang berjalan guna memperbaiki permasalahan tersebut dengan menggunakan teknologi Machine Learning dan memodelkan proses bisnis dengan Business Procces Modeling Notation (BPMN). Untuk memperlancar tujuan penelitan, penelitian ini menggunakan metode Business Process Reengineering Life Cycle untuk menghasilkan CRM bawang merah. Pada penelitian ini menghasilkan sebuah temuan yaitu proses bisnis yang baru dengan menyertakan teknologi Machine Learning yang ditampung pada aplikasi cluster petani yang telah digambarkan pada BPMN, hal tersebut dilakukan agar menunjang kekurangan dalam kegiatan petani agar lebih menjadi efisien dan optimal serta mendapatkan hasil panen yang diinginkan