Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Subramanian, Sathyawageeswar"'
We study the complexity of testing properties of quantum channels. First, we show that testing identity to any channel $\mathcal N: \mathbb C^{d_{\mathrm{in}} \times d_{\mathrm{in}}} \to \mathbb C^{d_{\mathrm{out}} \times d_{\mathrm{out}}}$ in diamon
Externí odkaz:
http://arxiv.org/abs/2409.12566
We study classes of constant-depth circuits with gates that compute restricted polynomial threshold functions, recently introduced by [Kum23] as a family that strictly generalizes $\mathsf{AC}^0$. Denoting these circuit families $\mathsf{bPTFC}^0[k]$
Externí odkaz:
http://arxiv.org/abs/2408.16378
Autor:
Caro, Matthias, Gur, Tom, Rouzé, Cambyse, França, Daniel Stilck, Subramanian, Sathyawageeswar
Learning tasks play an increasingly prominent role in quantum information and computation. They range from fundamental problems such as state discrimination and metrology over the framework of quantum probably approximately correct (PAC) learning, to
Externí odkaz:
http://arxiv.org/abs/2311.05529
We prove that the computation of the Kronecker coefficients of the symmetric group is contained in the complexity class #BQP. This improves a recent result of Bravyi, Chowdhury, Gosset, Havlicek, and Zhu. We use only the quantum computing tools that
Externí odkaz:
http://arxiv.org/abs/2307.02389
Publikováno v:
Proceedings of the 40th International Conference on Machine Learning (ICML2023) https://proceedings.mlr.press/v202/yamasaki23a.html
A significant challenge in the field of quantum machine learning (QML) is to establish applications of quantum computation to accelerate common tasks in machine learning such as those for neural networks. Ridgelet transform has been a fundamental mat
Externí odkaz:
http://arxiv.org/abs/2301.11936
We study the problem of designing worst-case to average-case reductions for quantum algorithms. For all linear problems, we provide an explicit and efficient transformation of quantum algorithms that are only correct on a small (even sub-constant) fr
Externí odkaz:
http://arxiv.org/abs/2212.03348
Fault-tolerant measurement-based quantum computation (MBQC) with recent progress on quantum technologies leads to a promising scalable platform for realizing quantum computation, conducted by preparing a large-scale graph state over many qubits and p
Externí odkaz:
http://arxiv.org/abs/2201.11127
Entropy is a fundamental property of both classical and quantum systems, spanning myriad theoretical and practical applications in physics and computer science. We study the problem of obtaining estimates to within a multiplicative factor $\gamma>1$
Externí odkaz:
http://arxiv.org/abs/2111.11139
Kernel methods augmented with random features give scalable algorithms for learning from big data. But it has been computationally hard to sample random features according to a probability distribution that is optimized for the data, so as to minimiz
Externí odkaz:
http://arxiv.org/abs/2004.10756
We study the potential utility of classical techniques of spectral sparsification of graphs as a preprocessing step for digital quantum algorithms, in particular, for Hamiltonian simulation. Our results indicate that spectral sparsification of a grap
Externí odkaz:
http://arxiv.org/abs/1910.02861