An Advanced CAD Tool for Quantum Device Simulation

1999 ◽  
Author(s):  
P. Stout ◽  
A. J. Przekwas
Keyword(s):  
Device Simulation ◽  
Quantum Device ◽  
Cad Tool

Quantum device simulation with a generalized tunneling formula

1995 ◽  
Vol 67 (17) ◽  
pp. 2539-2541 ◽  
Author(s):  
Gerhard Klimeck ◽  
Roger Lake ◽  
R. Chris Bowen ◽  
William R. Frensley ◽  
Ted S. Moise
Keyword(s):  
Device Simulation ◽  
Quantum Device

PARTICLE MODELS FOR DEVICE SIMULATION

2003 ◽  
Vol 13 (03) ◽  
pp. 727-769 ◽  
Author(s):  
HANS KOSINA ◽  
MIHAIL NEDJALKOV
Keyword(s):  
Device Simulation ◽  
Neumann Series ◽  
Integral Representations ◽  
Formal Approach ◽  
Boltzmann Equations ◽  
Small Signal Analysis ◽  
Quantum Device ◽  
Physically Based ◽  
Transport Problems ◽  
Mc Method

A theoretical analysis of the Monte Carlo (MC) method for both semiclassical and quantum device simulation is presented. A link between physically-based MC methods for semiclassical transport calculations and the numerical MC method for solving integrals and integral equations is established. The integral representations of the transient and the stationary Boltzmann equations are presented as well as the respective conjugate equations. The structure of the terms of the Neumann series and their evaluation by MC integration is discussed. Using this formal approach the standard MC algorithms and a variety of new algorithms is derived, such as the backward and the weighted algorithms, and algorithms for linear small-signal analysis. Applying this theoretical framework to the Wigner-Boltzmann equation enables the development of particle models for quantum transport problems.

A new method for quantum device simulation

1999 ◽  
Vol 86 (9) ◽  
pp. 1051-1062 ◽  
Author(s):  
Mohammad J. SHARIFI ◽  
A. ADIBI
Keyword(s):  
Device Simulation ◽  
New Method ◽  
Quantum Device

scholarly journals A divide-and-conquer algorithm for quantum state preparation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Israel F. Araujo ◽  
Daniel K. Park ◽  
Francesco Petruccione ◽  
Adenilton J. da Silva
Keyword(s):  
Computational Cost ◽  
Quantum Circuit ◽  
Divide And Conquer ◽  
Computational Time ◽  
Quantum Computers ◽  
Dimensional Vector ◽  
Quantum Devices ◽  
Divide And Conquer Algorithm ◽  
Quantum State Preparation ◽  
Quantum Device

AbstractAdvantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices.

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