Pratten, DR & Mathieson, L 2024, 'Relational Expressions for Data Transformation and Computation' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 241-255.
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Sharma, R, Saqib, M, Lin, CT & Blumenstein, M 2024, 'Maritime Surveillance Using Instance Segmentation Techniques' in Studies in Autonomic, Data-driven and Industrial Computing, Springer Nature Singapore, pp. 31-47.
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Berta, M & Tomamichel, M 2024, 'Entanglement Monogamy via Multivariate Trace Inequalities', Communications in Mathematical Physics, vol. 405, no. 2.
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AbstractEntropy is a fundamental concept in quantum information theory that allows to quantify entanglement and investigate its properties, for example its monogamy over multipartite systems. Here, we derive variational formulas for relative entropies based on restricted measurements of multipartite quantum systems. By combining these with multivariate matrix trace inequalities, we recover and sometimes strengthen various existing entanglement monogamy inequalities. In particular, we give direct, matrix-analysis-based proofs for the faithfulness of squashed entanglement by relating it to the relative entropy of entanglement measured with one-way local operations and classical communication, as well as for the faithfulness of conditional entanglement of mutual information by relating it to the separably measured relative entropy of entanglement. We discuss variations of these results using the relative entropy to states with positive partial transpose, and multipartite setups. Our results simplify and generalize previous derivations in the literature that employed operational arguments about the asymptotic achievability of information-theoretic tasks.
Berta, M, Brandão, FGSL, Gour, G, Lami, L, Plenio, MB, Regula, B & Tomamichel, M 2024, 'The tangled state of quantum hypothesis testing', Nature Physics, vol. 20, no. 2, pp. 172-175.
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Farooq, MU, Fritz, T, Haapasalo, E & Tomamichel, M 2024, 'Matrix Majorization in Large Samples', IEEE Transactions on Information Theory, pp. 1-1.
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Grochow, JA & Qiao, Y 2024, 'On p -Group Isomorphism: Search-to-Decision, Counting-to-Decision, and Nilpotency Class Reductions via Tensors', ACM Transactions on Computation Theory, vol. 16, no. 1, pp. 1-39.
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In this article, we study some classical complexity-theoretic questions regarding Group Isomorphism ( GpI ). We focus on p -groups (groups of prime power order) with odd p , which are believed to be a bottleneckcase for GpI , and work in the model of matrix groups over finite fields. Our main results are as follows: • Although search-to-decision and counting-to-decision reductions have been known for more than four decades for Graph Isomorphism , they had remained open for GpI , explicitly asked by Arvind and Torán ( EATCS Bull. , 2005). Extending methods from Tensor Isomorphism (TI) (Grochow and Qiao, ITCS 2021), we show moderately exponential-time such reductions within p -groups of class 2 and exponent p . • Despite the widely held belief that p -groups of class 2 and exponent p are the hardest cases of GpI , there was no reduction to these groups from ...
Halder, A, Shivakumara, P, Pal, U, Blumenstein, M & Ghosal, P 2024, 'A Locally Weighted Linear Regression-Based Approach for Arbitrary Moving Shaky and Nonshaky Video Classification', International Journal of Pattern Recognition and Artificial Intelligence, vol. 38, no. 01.
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Classification and identification of objects are complex and challenging in pattern recognition and artificial intelligence if a shaky and nonshaky camera captures the videos at different distances during the day and nighttime. This work presents a model for classifying a given video as a static, uniform, or arbitrarily moving videos so that the complexity of the problem can be reduced. To avoid the threat of different distances between the objects and the camera, the proposed work introduces new steps for estimating the depth of the objects in the video frames. We explore locally weighted linear regression for feature extraction from depth information based on the notion that the regression line fits almost all the points for uniformity and does not fit for arbitrary moving. The extracted features are fed to a random forest classifier to classify static, uniform, or arbitrary moving video. The results on a large dataset, which includes videos captured day and night, show that the proposed method successfully classifies static, uniform and arbitrary videos with 0.86, 1.00 and 0.67 F-measures, respectively. Overall, our method obtains 87% accuracy for classification of static, uniform and arbitrary video, which is superior to the state-of-the-art methods.
Nandanwar, L, Shivakumara, P, Jalab, HA, Ibrahim, RW, Raghavendra, R, Pal, U, Lu, T & Blumenstein, M 2024, 'A Conformable Moments-Based Deep Learning System for Forged Handwriting Detection', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 5407-5420.
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Shivakumara, P, Banerjee, A, Nandanwar, L, Pal, U, Antonacopoulos, A, Lu, T & Blumenstein, M 2024, 'A new deep CNN for 3D text localization in the wild through shadow removal', Computer Vision and Image Understanding, vol. 238, pp. 103863-103863.
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Chen, Z, Grochow, JA, Qiao, Y, Tang, G & Zhang, C 1970, 'On the complexity of isomorphism problems for tensors, groups, and polynomials iii: Actions by classical groups', Leibniz International Proceedings in Informatics, LIPIcs.
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We study the complexity of isomorphism problems for d-way arrays, or tensors, under natural actions by classical groups such as orthogonal, unitary, and symplectic groups. These problems arise naturally in statistical data analysis and quantum information. We study two types of complexitytheoretic questions. First, for a fixed action type (isomorphism, conjugacy, etc.), we relate the complexity of the isomorphism problem over a classical group to that over the general linear group. Second, for a fixed group type (orthogonal, unitary, or symplectic), we compare the complexity of the isomorphism problems for different actions. Our main results are as follows. First, for orthogonal and symplectic groups acting on 3-way arrays, the isomorphism problems reduce to the corresponding problems over the general linear group. Second, for orthogonal and unitary groups, the isomorphism problems of five natural actions on 3-way arrays are polynomial-Time equivalent, and the d-Tensor isomorphism problem reduces to the 3-Tensor isomorphism problem for any fixed d 3. For unitary groups, the preceding result implies that LOCC classification of tripartite quantum states is at least as difficult as LOCC classification of d-partite quantum states for any d. Lastly, we also show that the graph isomorphism problem reduces to the tensor isomorphism problem over orthogonal and unitary groups.
Cao, MX, Jain, R & Tomamichel, M 2024, 'Quantum Channel Simulation under Purified Distance is no more difficult than State Splitting'.
Krishnan Vijayan, M, Paler, A, Gavriel, J, Myers, CR, Rohde, PP & Devitt, SJ 2024, 'Compilation of algorithm-specific graph states for quantum circuits'.
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Abstract We present a quantum circuit compiler that prepares an algorithm-specific graph state from quantum circuits described in high level languages, such as Cirq and Q#. The computation can then be implemented using a series of non-Pauli measurements on this graph state. By compiling the graph state directly instead of starting with a standard lattice cluster state and preparing it over the course of the computation, we are able to better understand the resource costs involved and eliminate wasteful Pauli measurements on the actual quantum device. Access to this algorithm-specific graph state also allows for optimisation over locally equivalent graph states to implement the same quantum circuit. The compiler presented here finds ready application in measurement based quantum computing, NISQ devices and logical level compilation for fault tolerant implementations.
Lumbreras, J & Tomamichel, M 2024, 'Linear bandits with polylogarithmic minimax regret'.
Wu, J, Hu, Y, Bansal, A & Tomamichel, M 2024, 'On the composable security of weak coin flipping'.
