Zobrazeno 1 - 10
of 467
pro vyhledávání: '"Sornborger, A."'
Reservoir computing is a promising approach for harnessing the computational power of recurrent neural networks while dramatically simplifying training. This paper investigates the application of integrate-and-fire neurons within reservoir computing
Externí odkaz:
http://arxiv.org/abs/2407.20547
We introduce a framework for simulating, on an $(n+1)$-qubit quantum computer, the action of a Gaussian Bosonic (GB) circuit on a state over $2^n$ modes. Specifically, we encode the initial bosonic state's expectation values over quadrature operators
Externí odkaz:
http://arxiv.org/abs/2407.06290
Autor:
Nguyen-Fotiadis, Nga T. T., Chiodi, Robert, McKerns, Michael, Livescu, Daniel, Sornborger, Andrew
The stable numerical integration of shocks in compressible flow simulations relies on the reduction or elimination of Gibbs phenomena (unstable, spurious oscillations). A popular method to virtually eliminate Gibbs oscillations caused by numerical di
Externí odkaz:
http://arxiv.org/abs/2405.08185
Quantum phase estimation is one of the fundamental primitives that underpins many quantum algorithms, including Shor's algorithm for efficiently factoring large numbers. Due to its significance as a subroutine, in this work, we consider the coherent
Externí odkaz:
http://arxiv.org/abs/2403.18927
Autor:
Adil, Arsalan, Rudolph, Manuel S., Arrasmith, Andrew, Holmes, Zoë, Albrecht, Andreas, Sornborger, Andrew
Decoherence and einselection have been effective in explaining several features of an emergent classical world from an underlying quantum theory. However, the theory assumes a particular factorization of the global Hilbert space into constituent syst
Externí odkaz:
http://arxiv.org/abs/2403.10895
Publikováno v:
PRX Quantum 5, 030306 (2024)
The Schmidt decomposition is the go-to tool for measuring bipartite entanglement of pure quantum states. Similarly, it is possible to study the entangling features of a quantum operation using its operator-Schmidt, or tensor product decomposition. Wh
Externí odkaz:
http://arxiv.org/abs/2402.05018
How well can quantum computers simulate classical dynamical systems? There is increasing effort in developing quantum algorithms to efficiently simulate dynamics beyond Hamiltonian simulation, but so far exact resource estimates are not known. In thi
Externí odkaz:
http://arxiv.org/abs/2309.07881
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-14 (2024)
Abstract The capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has bee
Externí odkaz:
https://doaj.org/article/1bcbe702fd6249d98dfe0d1d908fbef5
Autor:
Timo Eckstein, Refik Mansuroglu, Piotr Czarnik, Jian-Xin Zhu, Michael J. Hartmann, Lukasz Cincio, Andrew T. Sornborger, Zoë Holmes
Publikováno v:
npj Quantum Information, Vol 10, Iss 1, Pp 1-10 (2024)
Abstract Periodically driven quantum systems exhibit a diverse set of phenomena but are more challenging to simulate than their equilibrium counterparts. Here, we introduce the Quantum High-Frequency Floquet Simulation (QHiFFS) algorithm as a method
Externí odkaz:
https://doaj.org/article/0a39ea432fd34fc18da14b863853377e
We study the quantum simulation of mixed quantum-semiclassical (MQS) systems, of fundamental interest in many areas of physics, such as molecular scattering and gravitational backreaction. A basic question for these systems is whether quantum algorit
Externí odkaz:
http://arxiv.org/abs/2308.16147