Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Sutter, David"'
Quasiprobabilistic cutting techniques allow us to partition large quantum circuits into smaller subcircuits by replacing non-local gates with probabilistic mixtures of local gates. The cost of this method is a sampling overhead that scales exponentia
Externí odkaz:
http://arxiv.org/abs/2312.11638
Publikováno v:
2023 IEEE International Conference on Quantum Computing and Engineering (QCE)
Quantum support vector machines have the potential to achieve a quantum speedup for solving certain machine learning problems. The key challenge for doing so is finding good quantum kernels for a given data set -- a task called kernel alignment. In t
Externí odkaz:
http://arxiv.org/abs/2304.09899
Circuit knitting is the process of partitioning large quantum circuits into smaller subcircuits such that the result of the original circuits can be deduced by only running the subcircuits. Such techniques will be crucial for near-term and early faul
Externí odkaz:
http://arxiv.org/abs/2302.03366
Autor:
Piveteau, Christophe, Sutter, David
Publikováno v:
IEEE Transactions on Information Theory, 2023
The scarcity of qubits is a major obstacle to the practical usage of quantum computers in the near future. To circumvent this problem, various circuit knitting techniques have been developed to partition large quantum circuits into subcircuits that f
Externí odkaz:
http://arxiv.org/abs/2205.00016
Publikováno v:
2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS), Denver, CO, USA, 2022, pp. 844-850
Consider a sequential process in which each step outputs a system $A_i$ and updates a side information register $E$. We prove that if this process satisfies a natural "non-signalling" condition between past outputs and future side information, the mi
Externí odkaz:
http://arxiv.org/abs/2203.04989
Publikováno v:
Quantum 8, 1225 (2024)
Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any known classical algorithm for certain data sets. The training of such mo
Externí odkaz:
http://arxiv.org/abs/2203.00031
Making statements about the performance of trained models on tasks involving new data is one of the primary goals of machine learning, i.e., to understand the generalization power of a model. Various capacity measures try to capture this ability, but
Externí odkaz:
http://arxiv.org/abs/2112.04807
Publikováno v:
Physical Review Applied, 2023
Variational quantum time evolution allows us to simulate the time dynamics of quantum systems with near-term compatible quantum circuits. Due to the variational nature of this method the accuracy of the simulation is a priori unknown. We derive globa
Externí odkaz:
http://arxiv.org/abs/2108.00022