Reliability of Noisy Intermediate Scale Quantum Computers: A Network Reliability Approach

2021 ◽  
Author(s):  
Christian Tanguy
Keyword(s):  
Network Reliability ◽  
Quantum Computers ◽  
Intermediate Scale

scholarly journals Comparison of quantum-walk implementations on noisy intermediate-scale quantum computers

2021 ◽  
Vol 103 (2) ◽  
Author(s):  
Konstantinos Georgopoulos ◽  
Clive Emary ◽  
Paolo Zuliani
Keyword(s):  
Quantum Walk ◽  
Quantum Computers ◽  
Intermediate Scale

scholarly journals Decoherence dynamics estimation for superconducting gate-model quantum computers

2020 ◽  
Vol 19 (10) ◽  
Author(s):  
Laszlo Gyongyosi
Keyword(s):  
Gaussian Noise ◽  
Quantum Computer ◽  
Computer Architectures ◽  
Quantum Circuits ◽  
Optimal Placement ◽  
Quantum Computers ◽  
Intermediate Scale ◽  
Layout Generation ◽  
Quantum Technology ◽  
Physical Layout

Abstract Superconducting gate-model quantum computer architectures provide an implementable model for practical quantum computations in the NISQ (noisy intermediate scale quantum) technology era. Due to hardware restrictions and decoherence, generating the physical layout of the quantum circuits of a gate-model quantum computer is a challenge. Here, we define a method for layout generation with a decoherence dynamics estimation in superconducting gate-model quantum computers. We propose an algorithm for the optimal placement of the quantum computational blocks of gate-model quantum circuits. We study the effects of capacitance interference on the distribution of the Gaussian noise in the Josephson energy.

scholarly journals Iterative qubit-excitation based variational quantum eigensolver

2021 ◽  
Author(s):  
Yordan Yordanov ◽  
Vasileios Armaos ◽  
Crispin Barnes ◽  
David Arvidsson-Shukur
Keyword(s):  
Numerical Simulations ◽  
Small Molecules ◽  
Time Complexity ◽  
Molecular Simulations ◽  
Quantum Circuits ◽  
Quantum Computers ◽  
Successful Implementation ◽  
Intermediate Scale ◽  
Promising Application ◽  
Physical Features

Abstract Molecular simulations with the variational quantum eigensolver (VQE) are a promising application for emerging noisy intermediate-scale quantum computers. Constructing accurate molecular ansatze that are easy to optimize and implemented by shallow quantum circuits is crucial for the successful implementation of such simulations. Ansatze are, generally, constructed as series of fermionic-excitation evolutions. Instead, we demonstrate the usefulness of constructing ansatze with ``qubit-excitation evolutions', which, contrary to fermionic excitation evolutions, obey ``qubit commutation relations'. We show that qubit excitation evolutions, despite the lack of some of the physical features of fermionic excitation evolutions, accurately construct ansatze, while requiring asymptotically fewer gates. Utilizing qubit excitation evolutions, we introduce the iterative qubit excitation based VQE (IQEB-VQE) algorithm. The IQEB-VQE performs molecular simulations using a problem-tailored ansatz, grown iteratively by appending evolutions of single and double qubit excitation operators. By performing numerical simulations for small molecules, we benchmark the IQEB-VQE, and compare it against other competitive VQE algorithms. In terms of circuit efficiency and time complexity, we find that the IQEB-VQE systematically outperforms the previously most circuit-efficient, practically scalable VQE algorithms.

Architecting Noisy Intermediate-Scale Quantum Computers: A Real-System Study

IEEE Micro ◽  
2020 ◽  
Vol 40 (3) ◽  
pp. 73-80
Author(s):  
Prakash Murali ◽  
Norbert M. Linke ◽  
Margaret Martonosi ◽  
Ali Javadi Abhari ◽  
Nhung Hong Nguyen ◽  
...  
Keyword(s):  
Real System ◽  
Quantum Computers ◽  
System Study ◽  
Intermediate Scale

scholarly journals Fisher Information in Noisy Intermediate-Scale Quantum Applications

Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 539
Author(s):  
Johannes Jakob Meyer
Keyword(s):  
Fisher Information ◽  
Quantum Algorithms ◽  
Quantum Fisher Information ◽  
Quantum Systems ◽  
Quantum Circuits ◽  
Quantum Computers ◽  
Quantum Devices ◽  
Intermediate Scale ◽  
Quantum Sensing ◽  
Near Term

The recent advent of noisy intermediate-scale quantum devices, especially near-term quantum computers, has sparked extensive research efforts concerned with their possible applications. At the forefront of the considered approaches are variational methods that use parametrized quantum circuits. The classical and quantum Fisher information are firmly rooted in the field of quantum sensing and have proven to be versatile tools to study such parametrized quantum systems. Their utility in the study of other applications of noisy intermediate-scale quantum devices, however, has only been discovered recently. Hoping to stimulate more such applications, this article aims to further popularize classical and quantum Fisher information as useful tools for near-term applications beyond quantum sensing. We start with a tutorial that builds an intuitive understanding of classical and quantum Fisher information and outlines how both quantities can be calculated on near-term devices. We also elucidate their relationship and how they are influenced by noise processes. Next, we give an overview of the core results of the quantum sensing literature and proceed to a comprehensive review of recent applications in variational quantum algorithms and quantum machine learning.

scholarly journals Clinical Data Classification With Noisy Intermediate Scale Quantum Computers

2021 ◽  
Author(s):  
Sasan Moradi ◽  
Christoph Brandner ◽  
Clemens Spielvogel ◽  
Denis Krajnc ◽  
Stefan Hillmich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Classification ◽  
Predictive Performance ◽  
Small Sample ◽  
Inner Product ◽  
Support Vector ◽  
Quantum Computers ◽  
Intermediate Scale ◽  
Quantum Machine Learning ◽  
Feature Count

Abstract Quantum machine learning has experienced a significant progress in both software and hardware development in the recent years and has emerged as an applicable area of near-term quantum computers. In this work, we investigate the feasibility of utilizing quantum machine learning (QML) on real clinical datasets. We propose two QML algorithms for data classification on IBM quantum hardware: a quantum distance classifier (qDS) and a simplified quantum-kernel support vector machine (sqKSVM). We utilize these different methods using the linear time quantum data encoding technique (\({\text{log}}_{2}N\)) for embedding classical data into quantum states and estimating the inner product on 15-qubit IBMQ Melbourne quantum computer. We match the predictive performance of our QML approaches with prior QML methods and with their classical counterpart algorithms for three open-access clinical datasets. Our results imply that the qDS in small sample and feature count datasets outperforms kernel-based methods. In contrast, quantum kernel approaches outperform qDS in high sample and feature count datasets. We demonstrate that the \({\text{log}}_{2}N\) encoding increases predictive performance with up to +2% area under the receiver operator characteristics curve across all quantum machine learning approaches, thus, making it ideal for machine learning tasks executed in Noisy Intermediate Scale Quantum computers.

scholarly journals Qubit-excitation-based adaptive variational quantum eigensolver

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yordan S. Yordanov ◽  
V. Armaos ◽  
Crispin H. W. Barnes ◽  
David R. M. Arvidsson-Shukur
Keyword(s):  
Numerical Simulations ◽  
Small Molecules ◽  
Molecular Simulations ◽  
Quantum Circuits ◽  
Quantum Computers ◽  
Successful Implementation ◽  
Intermediate Scale ◽  
Commutation Relations ◽  
Promising Application ◽  
Physical Features

AbstractMolecular simulations with the variational quantum eigensolver (VQE) are a promising application for emerging noisy intermediate-scale quantum computers. Constructing accurate molecular ansätze that are easy to optimize and implemented by shallow quantum circuits is crucial for the successful implementation of such simulations. Ansätze are, generally, constructed as series of fermionic-excitation evolutions. Instead, we demonstrate the usefulness of constructing ansätze with "qubit-excitation evolutions”, which, contrary to fermionic excitation evolutions, obey "qubit commutation relations”. We show that qubit excitation evolutions, despite the lack of some of the physical features of fermionic excitation evolutions, accurately construct ansätze, while requiring asymptotically fewer gates. Utilizing qubit excitation evolutions, we introduce the qubit-excitation-based adaptive (QEB-ADAPT)-VQE protocol. The QEB-ADAPT-VQE is a modification of the ADAPT-VQE that performs molecular simulations using a problem-tailored ansatz, grown iteratively by appending evolutions of qubit excitation operators. By performing classical numerical simulations for small molecules, we benchmark the QEB-ADAPT-VQE, and compare it against the original fermionic-ADAPT-VQE and the qubit-ADAPT-VQE. In terms of circuit efficiency and convergence speed, we demonstrate that the QEB-ADAPT-VQE outperforms the qubit-ADAPT-VQE, which to our knowledge was the previous most circuit-efficient scalable VQE protocol for molecular simulations.

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