ABSTRACT
Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. It makes certain kinds of problems be solved easier compared to classical computers. This advantage of quantum computing can be used to implement many existing problems in different fields incredibly effectively. One important field that quantum computing has shown great results in machine learning. Until now, many different quantum algorithms have been presented to perform different machine learning approaches. In some special cases, the execution time of these quantum algorithms will be reduced exponentially compared to the classical ones. But at the same time, with increasing data volume and computation time, taking care of systems to prevent unwanted interactions with the environment can be a daunting task and since these algorithms work on machine learning problems, which usually includes big data, their implementation is very costly in terms of quantum resources. Here, in this paper, we have proposed an approach to reduce the cost of quantum circuits and to optimize quantum machine learning circuits in particular. To reduce the number of resources used, in this paper an approach including different optimization algorithms is considered. Our approach is used to optimize quantum machine learning algorithms for big data. In this case, the optimized circuits run quantum machine learning algorithms in less time than the original ones and by preserving the original functionality. Our approach improves the number of quantum gates by 10.7% and 14.9% in different circuits respectively. This is the amount of reduction for one iteration of a given sub-circuit U in the main circuit. For cases where this sub-circuit is repeated more times in the main circuit, the optimization rate is increased. Therefore, by applying the proposed method to circuits with big data, both cost and performance are improved.
ABSTRACT
BACKGROUND: In this retrospective study, data from patients listed in the Korea Central Cancer Registry during 1993-2014 were analysed, to investigate the incidence and survival of second primary cancers (SPCs) after a diagnosis of primary peritoneal, epithelial ovarian, and fallopian tubal (POFT) cancer. METHODS: The standardised incidence ratio (SIR) and survival outcomes of patients with SPCs among POFT cancer survivors were analysed. RESULTS: Among 20,738 POFT cancer survivors, 798 (3.84%) developed SPCs, at an average interval of 5.50 years. SPC risk in POFT survivors (SIR, 1.29) was higher compared to the general population. The most high-risk type of SPC was leukaemia (3.07) followed by the lung and bronchus (1.80), colon (1.58), rectum and rectosigmoid junction (1.42), thyroid (1.34), and breast (1.26). In women aged < 60 years, cancer of the breast (1.30), ascending colon (2.26), and transverse colon (4.07) as SPCs increased. Up to 10 years after POFT cancer treatment, leukaemia risk increased, especially in those < 60 years, with serous histology, and with distant stage, which required aggressive chemotherapy. The median overall survival time was 12.8 years and 14.3 years in women with POFT cancer and SPCs, respectively. Thyroid and breast cancers were favourable prognostic markers among SPCs. CONCLUSIONS: The overall SPC risk increases in POFT cancer survivors, especially in those < 60 years. The cancer risk of breast and the proximal colon increase based on hereditary predisposition, while leukaemia likely develops from aggressive treatment. The median overall survival is favourable in POFT cancer survivors with SPCs.
