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1. An algebraic characterization of binary CSS-T codes and cyclic CSS-T codes for quantum fault tolerance

   https://doi.org/10.1007/s11128-024-04427-5  

   Camps-Moreno, Eduardo; López, Hiram H; Matthews, Gretchen L; Ruano, Diego; San-José, Rodrigo; Soprunov, Ivan (June 2024, Quantum Information Processing)

   Abstract CSS-T codes were recently introduced as quantum error-correcting codes that respect a transversal gate. A CSS-T code depends on a CSS-T pair, which is a pair of binary codes$$(C_1, C_2)$$ $(C_1,C_2)$such that$$C_1$$ $C_1$contains$$C_2$$ $C_2$,$$C_2$$ $C_2$is even, and the shortening of the dual of$$C_1$$ $C_1$with respect to the support of each codeword of$$C_2$$ $C_2$is self-dual. In this paper, we give new conditions to guarantee that a pair of binary codes$$(C_1, C_2)$$ $(C_1,C_2)$is a CSS-T pair. We define the poset of CSS-T pairs and determine the minimal and maximal elements of the poset. We provide a propagation rule for nondegenerate CSS-T codes. We apply some main results to Reed–Muller, cyclic and extended cyclic codes. We characterize CSS-T pairs of cyclic codes in terms of the defining cyclotomic cosets. We find cyclic and extended cyclic codes to obtain quantum codes with better parameters than those in the literature. 

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2. Quantum Symmetries of Noncommutative Tori

   https://doi.org/10.1007/s00220-025-05337-y  

   Evans, David_E; Jones, Corey (June 2025, Communications in Mathematical Physics)

   Abstract We consider the problem of building non-invertible quantum symmetries (as characterized by actions of unitary fusion categories) on noncommutative tori. We introduce a general method to construct actions of fusion categories on inductive limit C*-algberas using finite dimenionsal data, and then apply it to obtain AT-actions of arbitrary Haagerup-Izumi categories on noncommutative 2-tori, of the even part of the$$E_{8}$$ $E_8$subfactor on a noncommutative 3-torus, and of$$\text {PSU}(2)_{15}$$ $\text{PSU}{\left(2\right)}_{15}$on a noncommutative 4-torus. 

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3. Boosting quantum amplitude exponentially in variational quantum algorithms

   https://doi.org/10.1088/2058-9565/acf4ba  

   Kyaw, Thi Ha; Soley, Micheline B; Allen, Brandon; Bergold, Paul; Sun, Chong; Batista, Victor S; Aspuru-Guzik, Alán (October 2023, Quantum Science and Technology)

   Abstract We introduce a family of variational quantum algorithms, which we coin as quantum iterative power algorithms (QIPAs), and demonstrate their capabilities as applied to global-optimization numerical experiments. Specifically, we demonstrate the QIPA based on a double exponential oracle as applied to ground state optimization of theH₂molecule, search for the transmon qubit ground-state, and biprime factorization. Our results indicate that QIPA outperforms quantum imaginary time evolution (QITE) and requires a polynomial number of queries to reach convergence even with exponentially small overlap between an initial quantum state and the final desired quantum state, under some circumstances. We analytically show that there exists an exponential amplitude amplification at every step of the variational quantum algorithm, provided the initial wavefunction has non-vanishing probability with the desired state and that the unique maximum of the oracle is given by ${\lambda }_1>0$, while all other values are given by the same value $0<{\lambda }_2<{\lambda }_1$(hereλcan be taken as eigenvalues of the problem Hamiltonian). The generality of the global-optimization method presented here invites further application to other problems that currently have not been explored with QITE-based near-term quantum computing algorithms. Such approaches could facilitate identification of reaction pathways and transition states in chemical physics, as well as optimization in a broad range of machine learning applications. The method also provides a general framework for adaptation of a class of classical optimization algorithms to quantum computers to further broaden the range of algorithms amenable to implementation on current noisy intermediate-scale quantum computers. 

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4. Sparse Recovery: The Square of $$\ell _1/\ell _2$$ Norms

   https://doi.org/10.1007/s10915-024-02750-8  

   Jia, Jianqing; Prater-Bennette, Ashley; Shen, Lixin; Tripp, Erin_E (December 2024, Journal of Scientific Computing)

   Abstract This paper introduces a nonconvex approach for sparse signal recovery, proposing a novel model termed the$$\tau _2$$ ${\tau }_2$-model, which utilizes the squared$$\ell _1/\ell _2$$ ${\ell }_1/{\ell }_2$norms for this purpose. Our model offers an advancement over the$$\ell _0$$ ${\ell }_0$norm, which is often computationally intractable and less effective in practical scenarios. Grounded in the concept of effective sparsity, our approach robustly measures the number of significant coordinates in a signal, making it a powerful alternative for sparse signal estimation. The$$\tau _2$$ ${\tau }_2$-model is particularly advantageous due to its computational efficiency and practical applicability. We detail two accompanying algorithms based on Dinkelbach’s procedure and a difference of convex functions strategy. The first algorithm views the model as a linear-constrained quadratic programming problem in noiseless scenarios and as a quadratic-constrained quadratic programming problem in noisy scenarios. The second algorithm, capable of handling both noiseless and noisy cases, is based on the alternating direction linearized proximal method of multipliers. We also explore the model’s properties, including the existence of solutions under certain conditions, and discuss the convergence properties of the algorithms. Numerical experiments with various sensing matrices validate the effectiveness of our proposed model. 

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5. Hamiltonian reconstruction as metric for variational studies

   https://doi.org/10.21468/SciPostPhys.13.3.063  

   Zhang, Kevin; Lederer, Samuel; Choo, Kenny; Neupert, Titus; Carleo, Giuseppe; Kim, Eun-Ah (January 2022, SciPost Physics)

   Variational approaches are among the most powerful techniques toapproximately solve quantum many-body problems. These encompass bothvariational states based on tensor or neural networks, and parameterizedquantum circuits in variational quantum eigensolvers. However,self-consistent evaluation of the quality of variational wavefunctionsis a notoriously hard task. Using a recently developed Hamiltonianreconstruction method, we propose a multi-faceted approach to evaluatingthe quality of neural-network based wavefunctions. Specifically, weconsider convolutional neural network (CNN) and restricted Boltzmannmachine (RBM) states trained on a square latticespin-1/2 $1/2$J_1\!-\!J_2 $J_1-J_2$Heisenberg model. We find that the reconstructed Hamiltonians aretypically less frustrated, and have easy-axis anisotropy near the highfrustration point. In addition, the reconstructed Hamiltonians suppressquantum fluctuations in the largeJ_2 $J_2$limit. Our results highlight the critical importance of thewavefunction’s symmetry. Moreover, the multi-faceted insight from theHamiltonian reconstruction reveals that a variational wave function canfail to capture the true ground state through suppression of quantumfluctuations. 

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