arXiv subjects

Shahnawaz Ahmed

Publications and source records attributed to Shahnawaz Ahmed.

23 records · Page 2Linked to original sources

arXiv2020

Quantum State Tomography with Conditional Generative Adversarial Networks

Quantum state tomography (QST) is a challenging task in intermediate-scale quantum devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the CGAN framework, two duelling neural networks, a generator and a discriminator, learn multi-modal models from data. We augment a CGAN with custom neural-network layers that enable conversion of output from any standard neural network into a physical density matrix. To reconstruct the density matrix, the generator and discriminator networks train each other on data using standard gradient-based methods. We demonstrate that our QST-CGAN reconstructs optical quantum states with high fidelity orders of magnitude faster, and from less data, than a standard maximum-likelihood method. We also show that the QST-CGAN can reconstruct a quantum state in a single evaluation of the generator network if it has been pre-trained on similar quantum states.

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arXiv2020

Classification and reconstruction of optical quantum states with deep neural networks

We apply deep-neural-network-based techniques to quantum state classification and reconstruction. We demonstrate high classification accuracies and reconstruction fidelities, even in the presence of noise and with little data. Using optical quantum states as examples, we first demonstrate how convolutional neural networks (CNNs) can successfully classify several types of states distorted by, e.g., additive Gaussian noise or photon loss. We further show that a CNN trained on noisy inputs can learn to identify the most important regions in the data, which potentially can reduce the cost of tomography by guiding adaptive data collection. Secondly, we demonstrate reconstruction of quantum-state density matrices using neural networks that incorporate quantum-physics knowledge. The knowledge is implemented as custom neural-network layers that convert outputs from standard feedforward neural networks to valid descriptions of quantum states. Any standard feed-forward neural-network architecture can be adapted for quantum state tomography (QST) with our method. We present further demonstrations of our proposed [arXiv:2008.03240] QST technique with conditional generative adversarial networks (QST-CGAN). We motivate our choice of a learnable loss function within an adversarial framework by demonstrating that the QST-CGAN outperforms, across a range of scenarios, generative networks trained with standard loss functions. For pure states with additive or convolutional Gaussian noise, the QST-CGAN is able to adapt to the noise and reconstruct the underlying state. The QST-CGAN reconstructs states using up to two orders of magnitude fewer iterative steps than a standard iterative maximum likelihood (iMLE) method. Further, the QST-CGAN can reconstruct both pure and mixed states from two orders of magnitude fewer randomly chosen data points than iMLE.

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arXiv2020

Fitting IVIM with Variable Projection and Simplicial Optimization

Fitting multi-exponential models to Diffusion MRI (dMRI) data has always been challenging due to various underlying complexities. In this work, we introduce a novel and robust fitting framework for the standard two-compartment IVIM microstructural model. This framework provides a significant improvement over the existing methods and helps estimate the associated diffusion and perfusion parameters of IVIM in an automatic manner. As a part of this work we provide capabilities to switch between more advanced global optimization methods such as simplicial homology (SH) and differential evolution (DE). Our experiments show that the results obtained from this simultaneous fitting procedure disentangle the model parameters in a reduced subspace. The proposed framework extends the seminal work originated in the MIX framework, with improved procedures for multi-stage fitting. This framework has been made available as an open-source Python implementation and disseminated to the community through the DIPY project.

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arXiv2019

Open quantum systems with local and collective incoherent processes: Efficient numerical simulation using permutational invariance

The permutational invariance of identical two-level systems allows for an exponential reduction in the computational resources required to study the Lindblad dynamics of coupled spin-boson ensembles evolving under the effect of both local and collective noise. Here we take advantage of this speedup to study several important physical phenomena in the presence of local incoherent processes, in which each degree of freedom couples to its own reservoir. Assessing the robustness of collective effects against local dissipation is paramount to predict their presence in different physical implementations. We have developed an open-source library in Python, the Permutational-Invariant Quantum Solver (PIQS), which we use to study a variety of phenomena in driven-dissipative open quantum systems. We consider both local and collective incoherent processes in the weak, strong, and ultrastrong-coupling regimes. Using PIQS, we reproduced a series of known physical results concerning collective quantum effects and extended their study to the local driven-dissipative scenario. Our work addresses the robustness of various collective phenomena, e.g., spin squeezing, superradiance, quantum phase transitions, against local dissipation processes.

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arXiv2019

Virtual excitations in the ultra-strongly-coupled spin-boson model: physical results from unphysical modes

Here we show how, in the ultra-strongly-coupled spin-boson model, apparently unphysical "Matsubara modes" are required not only to regulate detailed balance, but also to arrive at a correct and physical description of the non-perturbative dynamics and steady-state. In particular, in the zero-temperature limit, we show that neglecting the Matsubara modes results in an erroneous emission of virtual photons from the collective ground state. To explore this difficult-to-model regime we start by using a non-perturbative hierarchical equations of motion (HEOM) approach, based on a partial fitting of the bath correlation-function which takes into account the infinite sum of Matsubara frequencies using only a biexponential function. We compare the HEOM method to both a pseudo-mode model, and the reaction coordinate (RC) mapping, which help explain the nature of the aberrations observed when Matsubara frequencies are neglected. For the pseudo-mode method we present a general proof of validity, which allows for negative Matsubara-contributions to the decomposition of the bath correlation functions to be described by zero-frequency Matsubara-modes with non-Hermitian coupling to the system. The latter obey a non-Hermitian pseudo-Schrödinger equation, ultimately justifying why superficially unphysical modes can give rise to physical system behavior.

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