Quantum Searching via Entanglement and Partial Diffusion

In this paper, we will define a quantum operator that performs the inversion about the mean only on a subspace of the system (Partial Diffusion Operator). This operator is used in a quantum search algorithm that runs in O(sqrt{N/M}) for searching an unstructured list of size N with M matches such that 1<= M<=N. We will show that the performance of the algorithm is more reliable than known {fixed operators quantum search algorithms} especially for multiple matches where we can get a solution after a single iteration with probability over 90% if the number of matches is approximately more than one-third of the search space. We will show that the algorithm will be able to handle the case where the number of matches M is unknown in advance such that 1<=M<=N in O(sqrt{N/M}). A performance comparison with Grover's algorithm will be provided.Comment: 19 pages. Submitted to IJQI. Please forward comments/enquires for the first author to [email protected]

Outpatient Commitment. Some Reflections on Ideology, Practice and Implications for Research

Over the recent years, increased attention has been paid to non-compliance by mentally disordered patients living in the community with outpatient treatment. To deal with this problem many countries are now revising relevant legislation, to introduce a broader base for involuntary treatment in the community. This paper focuses both on the problems concerning the ideology and implementation of involuntary outpatient treatment, and on some of the research problems related to the evaluation of both the efficacy and effectiveness of outpatient commitment

Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data

The ice surface temperature (IST) is an important boundary condition for both atmospheric and ocean and sea ice models and for coupled systems. An operational ice surface temperature product using satellite Metop AVHRR infra-red data was developed for MyOcean. The IST can be mapped in clear sky regions using a split window algorithm specially tuned for sea ice. Clear sky conditions prevail during spring in the Arctic, while persistent cloud cover limits data coverage during summer. The cloud covered regions are detected using the EUMETSAT cloud mask. The Metop IST compares to 2 m temperature at the Greenland ice cap Summit within STD error of 3.14 °C and to Arctic drifting buoy temperature data within STD error of 3.69 °C. A case study reveals that the in situ radiometer data versus satellite IST STD error can be much lower (0.73 °C) and that the different in situ measurements complicate the validation. Differences and variability between Metop IST and in situ data are analysed and discussed. An inter-comparison of Metop IST, numerical weather prediction temperatures and in situ observation indicates large biases between the different quantities. Because of the scarcity of conventional surface temperature or surface air temperature data in the Arctic, the satellite IST data with its relatively good coverage can potentially add valuable information to model analysis for the Arctic atmosphere

An Algorithmic Argument for Nonadaptive Query Complexity Lower Bounds on Advised Quantum Computation

This paper employs a powerful argument, called an algorithmic argument, to prove lower bounds of the quantum query complexity of a multiple-block ordered search problem in which, given a block number i, we are to find a location of a target keyword in an ordered list of the i-th block. Apart from much studied polynomial and adversary methods for quantum query complexity lower bounds, our argument shows that the multiple-block ordered search needs a large number of nonadaptive oracle queries on a black-box model of quantum computation that is also supplemented with advice. Our argument is also applied to the notions of computational complexity theory: quantum truth-table reducibility and quantum truth-table autoreducibility.Comment: 16 pages. An extended abstract will appear in the Proceedings of the 29th International Symposium on Mathematical Foundations of Computer Science, Lecture Notes in Computer Science, Springer-Verlag, Prague, August 22-27, 200

Quantum rejection sampling

Rejection sampling is a well-known method to sample from a target distribution, given the ability to sample from a given distribution. The method has been first formalized by von Neumann (1951) and has many applications in classical computing. We define a quantum analogue of rejection sampling: given a black box producing a coherent superposition of (possibly unknown) quantum states with some amplitudes, the problem is to prepare a coherent superposition of the same states, albeit with different target amplitudes. The main result of this paper is a tight characterization of the query complexity of this quantum state generation problem. We exhibit an algorithm, which we call quantum rejection sampling, and analyze its cost using semidefinite programming. Our proof of a matching lower bound is based on the automorphism principle which allows to symmetrize any algorithm over the automorphism group of the problem. Our main technical innovation is an extension of the automorphism principle to continuous groups that arise for quantum state generation problems where the oracle encodes unknown quantum states, instead of just classical data. Furthermore, we illustrate how quantum rejection sampling may be used as a primitive in designing quantum algorithms, by providing three different applications. We first show that it was implicitly used in the quantum algorithm for linear systems of equations by Harrow, Hassidim and Lloyd. Secondly, we show that it can be used to speed up the main step in the quantum Metropolis sampling algorithm by Temme et al.. Finally, we derive a new quantum algorithm for the hidden shift problem of an arbitrary Boolean function and relate its query complexity to "water-filling" of the Fourier spectrum.Comment: 19 pages, 5 figures, minor changes and a more compact style (to appear in proceedings of ITCS 2012

PENGGUNAAN MODEL PEMBELAJARAN AKTIF TEAM QUIZ DALAM MENINGKATKAN KEMAMPUAN MEMBACA PEMAHAMAN BAHASA JEPANG

Penelitian ini mengambil judul “penggunaan model pembelajaran aktif team quiz dalam meningkatkan kemampuan membaca pemahaman bahasa Jepang” . membaca pemahaman adalah memahami suatu informasi dari sebuah tulisan. membaca pemahaman merupakan salah satu pembelajaran yang sulit. Disebabkan karena metode yang digunakan oleh pengajar monoton dan kurang memotivasi siswa dalam pembelajaran membaca pemahaman. Penelitian ini bertujuan untuk mengetahui bagaimana penggunaan model pembelajaran aktif team quiz dalam meningkatkan kemampuan membaca pemahaman bahasa Jepang. Penelitian ini menggunakan metode quasi eksperiment dengan one group before-after (pretest dan posttest) design. Sampel dalam penelitian ini adalah siswa kelas XI Bahasa SMA Negeri 1 Parongpong tahun ajaran 2015/2016. Instrument penelitiannya adalah tes dan angket. Berdasarkan analisis data dengan perhitungan statistic yang menggunakan t hitung diketahui bahwa t hitung > t tabel maka Ho ditolak dan Hk diterima yang dapat disimpulkan bahwa penggunaan model pembelajaran aktif team quiz dalam meningkatkan kemampuan membaca pemahaman bahasa jepang efektif. Sedangkan hasil analisis data angket diketahui bahwa penggunaan model pembelajaran aktif team quiz dalam meningkatkan kemampuan membaca pemahaman bahasa Jepang mendapat respon positif dari siswa. ; Reading comprehension is the act of understanding information from a presented text. It is one of difficulties found in learning. It caused by monotonous method conducted by teacher and in turn, students have less motivation on learning reading comprehension. This research aims for how the Team Quiz active learning method can improve reading comprehension in Japanese. The quasi-experiment method with “one group before-after (pretest and posttest)” design is conducted in this research, by taking XI language students from 2015/2016 academic year of SMA Negeri 1 Parongpong as sample. Test and questionnaire is instrument used in this research. According to data analysis with statistical calculation using t count, noting that t count > t table, therefore Ho is rejected and Hk is accepted. To conclude, Team Quiz active learning method is effective in improving reading comprehension in Japanese. Moreover, according to data analysis conducted in questionnaire, the Team Quiz active learning method gains positive responses from students