Pub Date : 2023-06-06DOI: 10.1142/s2010324723500170
Z. Fadil, Chaitany Jayprakash Raorane, R. El Fdil, Seong Cheol Kim
{"title":"Dielectric properties of a phenanthrene-like nanoisland B14A10: Monte Carlo study","authors":"Z. Fadil, Chaitany Jayprakash Raorane, R. El Fdil, Seong Cheol Kim","doi":"10.1142/s2010324723500170","DOIUrl":"https://doi.org/10.1142/s2010324723500170","url":null,"abstract":"","PeriodicalId":54319,"journal":{"name":"Spin","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42852033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-30DOI: 10.1142/s2010324723500169
L. B. Chandrasekar, M. Karunakaran
{"title":"A theoretical analysis of spin-dependent tunneling in ZnO-based heterostructures","authors":"L. B. Chandrasekar, M. Karunakaran","doi":"10.1142/s2010324723500169","DOIUrl":"https://doi.org/10.1142/s2010324723500169","url":null,"abstract":"","PeriodicalId":54319,"journal":{"name":"Spin","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42799516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-19DOI: 10.1142/s2010324723500133
Wenjie Liu, Jing Xu, Bosi Wang
Due to the large state space of the two-qubit system, and the adoption of ladder reward function in the existing quantum state preparation methods, the convergence speed is slow and it is difficult to prepare the desired target quantum state with high fidelity under limited conditions. To solve the above problems, a difference-driven reinforcement learning (RL) algorithm for quantum state preparation of two-qubit system is proposed by improving the reward function and action selection strategy. Firstly, a model is constructed for the problem of preparing quantum states of a two-qubit system, with restrictions on the type of quantum gates and the time for quantum state evolution. In the preparation process, a weighted differential dynamic reward function is designed to assist the algorithm quickly obtain the maximum expected cumulative reward. Then, an adaptive [Formula: see text]-greedy action selection strategy is adopted to achieve a balance between exploration and utilization to a certain extent, thereby improving the fidelity of the final quantum state. The simulation results show that the proposed algorithm can prepare quantum state with high fidelity under limited conditions. Compared with other algorithms, it has different degrees of improvement in convergence speed and fidelity of the final quantum state.
{"title":"A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning","authors":"Wenjie Liu, Jing Xu, Bosi Wang","doi":"10.1142/s2010324723500133","DOIUrl":"https://doi.org/10.1142/s2010324723500133","url":null,"abstract":"Due to the large state space of the two-qubit system, and the adoption of ladder reward function in the existing quantum state preparation methods, the convergence speed is slow and it is difficult to prepare the desired target quantum state with high fidelity under limited conditions. To solve the above problems, a difference-driven reinforcement learning (RL) algorithm for quantum state preparation of two-qubit system is proposed by improving the reward function and action selection strategy. Firstly, a model is constructed for the problem of preparing quantum states of a two-qubit system, with restrictions on the type of quantum gates and the time for quantum state evolution. In the preparation process, a weighted differential dynamic reward function is designed to assist the algorithm quickly obtain the maximum expected cumulative reward. Then, an adaptive [Formula: see text]-greedy action selection strategy is adopted to achieve a balance between exploration and utilization to a certain extent, thereby improving the fidelity of the final quantum state. The simulation results show that the proposed algorithm can prepare quantum state with high fidelity under limited conditions. Compared with other algorithms, it has different degrees of improvement in convergence speed and fidelity of the final quantum state.","PeriodicalId":54319,"journal":{"name":"Spin","volume":"abs/2309.16972 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"63845344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1142/s201032472340012x
Kacho Imtiyaz Ali Khan, Pankhuri Gupta, R. Agarwal, N. Chowdhury, P. Muduli
{"title":"Comparative study of spin pumping in epitaxial- and polycrystalline-NiO/Ni80Fe20","authors":"Kacho Imtiyaz Ali Khan, Pankhuri Gupta, R. Agarwal, N. Chowdhury, P. Muduli","doi":"10.1142/s201032472340012x","DOIUrl":"https://doi.org/10.1142/s201032472340012x","url":null,"abstract":"","PeriodicalId":54319,"journal":{"name":"Spin","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44952940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-08DOI: 10.48550/arXiv.2305.04438
Noah G. Singer
Motivated by recent works on streaming algorithms for constraint satisfaction problems (CSPs), we define and analyze oblivious algorithms for the Max-$k$AND problem. This generalizes the definition by Feige and Jozeph (Algorithmica '15) of oblivious algorithms for Max-DICUT, a special case of Max-$2$AND. Oblivious algorithms round each variable with probability depending only on a quantity called the variable's bias. For each oblivious algorithm, we design a so-called"factor-revealing linear program"(LP) which captures its worst-case instance, generalizing one of Feige and Jozeph for Max-DICUT. Then, departing from their work, we perform a fully explicit analysis of these (infinitely many!) LPs. In particular, we show that for all $k$, oblivious algorithms for Max-$k$AND provably outperform a special subclass of algorithms we call"superoblivious"algorithms. Our result has implications for streaming algorithms: Generalizing the result for Max-DICUT of Saxena, Singer, Sudan, and Velusamy (SODA'23), we prove that certain separation results hold between streaming models for infinitely many CSPs: for every $k$, $O(log n)$-space sketching algorithms for Max-$k$AND known to be optimal in $o(sqrt n)$-space can be beaten in (a) $O(log n)$-space under a random-ordering assumption, and (b) $O(n^{1-1/k} D^{1/k})$ space under a maximum-degree-$D$ assumption. Even in the previously-known case of Max-DICUT, our analytic proof gives a fuller, computer-free picture of these separation results.
{"title":"Oblivious algorithms for the Max-kAND Problem","authors":"Noah G. Singer","doi":"10.48550/arXiv.2305.04438","DOIUrl":"https://doi.org/10.48550/arXiv.2305.04438","url":null,"abstract":"Motivated by recent works on streaming algorithms for constraint satisfaction problems (CSPs), we define and analyze oblivious algorithms for the Max-$k$AND problem. This generalizes the definition by Feige and Jozeph (Algorithmica '15) of oblivious algorithms for Max-DICUT, a special case of Max-$2$AND. Oblivious algorithms round each variable with probability depending only on a quantity called the variable's bias. For each oblivious algorithm, we design a so-called\"factor-revealing linear program\"(LP) which captures its worst-case instance, generalizing one of Feige and Jozeph for Max-DICUT. Then, departing from their work, we perform a fully explicit analysis of these (infinitely many!) LPs. In particular, we show that for all $k$, oblivious algorithms for Max-$k$AND provably outperform a special subclass of algorithms we call\"superoblivious\"algorithms. Our result has implications for streaming algorithms: Generalizing the result for Max-DICUT of Saxena, Singer, Sudan, and Velusamy (SODA'23), we prove that certain separation results hold between streaming models for infinitely many CSPs: for every $k$, $O(log n)$-space sketching algorithms for Max-$k$AND known to be optimal in $o(sqrt n)$-space can be beaten in (a) $O(log n)$-space under a random-ordering assumption, and (b) $O(n^{1-1/k} D^{1/k})$ space under a maximum-degree-$D$ assumption. Even in the previously-known case of Max-DICUT, our analytic proof gives a fuller, computer-free picture of these separation results.","PeriodicalId":54319,"journal":{"name":"Spin","volume":"24 1","pages":"15:1-15:19"},"PeriodicalIF":1.8,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83499538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-05DOI: 10.1142/s2010324723400118
S. Nagar, G. S. Mukherjee, M. Banerjee
{"title":"Structural and Magnetic properties of Ni Nanoparticles Embedded Vinyl Polymer Nanocomposite Films","authors":"S. Nagar, G. S. Mukherjee, M. Banerjee","doi":"10.1142/s2010324723400118","DOIUrl":"https://doi.org/10.1142/s2010324723400118","url":null,"abstract":"","PeriodicalId":54319,"journal":{"name":"Spin","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44534346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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