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arXiv:2211.10555 · iEnergy

Noise-resilient quantum power flow

Fei Feng, Yifan Zhou, Peng Zhang

Published Mar 1, 2023• 16 citations
arXivHTMLPDF

Abstract

Quantum power flow (QPF) offers an inspiring direction for overcoming the computation challenge of power flow through quantum computing. However, the practical implementation of existing QPF algorithms in today's noisy-intermediate-scale quantum (NISQ) era remains limited because of their sensitivity to noise. This paper establishes an NISQ-QPF algorithm that enables power flow computation on noisy quantum devices. The main contributions include: (1) a variational quantum circuit (VQC)-based alternating current (AC) power flow formulation, which enables QPF using short-depth quantum circuits; (2) NISQ-compatible QPF solvers based on the variational quantum linear solver (VQLS) and modified fast decoupled power flow; and (3) an error-resilient QPF scheme to relieve the QPF iteration deviations caused by noise; (3) a practical NISQ-QPF framework for implementable and reliable power flow analysis on noisy quantum machines. Extensive simulation tests validate the accuracy and generality of NISQ-QPF for solving practical power flow on IBM's real, noisy quantum computers.

DOI
10.23919/ien.2023.0008

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Quantum power system state estimation (QPSSE) offers an inspiring direction for tackling the challenge of state estimation through quantum computing. Nevertheless, the current bottlenecks originate from the scarcity of practical and scalable QPSSE methodologies in the noisy intermediate-scale quantum (NISQ) era. This paper devises a NISQ-QPSSE algorithm that facilitates state estimation on real NISQ devices. Our new contributions include: (1) A variational quantum circuit (VQC)-based QPSSE formulation that empowers QPSSE analysis utilizing shallow-depth quantum circuits; (2) A variational quantum linear solver (VQLS)-based QPSSE solver integrating QPSSE iterations with VQC optimization; (3) An advanced NISQ-compatible QPSSE methodology for tackling the measurement and coefficient matrix issues on real quantum computers; (4) A noise-resilient method to alleviate the detrimental effects of noise disturbances. The encouraging test results on the simulator and real-scale systems affirm the precision, universality, and scalability of our QPSSE algorithm and demonstrate the vast potential of QPSSE in the thriving NISQ era.
Power flow is an indispensable foundation for power system analytics. Under the deep penetration of renewables, modern power system analytics often becomes intractable because it needs to run an enormous amount of power flow analyses to quantify the impact of uncertainties. Unlike classical power flow methods that scale polynomially with the system size, quantum computing enables using logarithmically-scaled number of qubits to solve linear equations in power flow analysis. Thus, quantum power flow (QPF) provides a promising direction to make today's intractable power system analytics tractable. However, a major obstacle to the development of a practical quantum power flow algorithm lies in the fact that today's mainstream quantum computers are still noisy-intermediate-scale quantum (NISQ) devices whose capability is restricted by the limited number of qubits and considerable noises. To bridge this gap, Stony Brook University establishes a variational quantum power flow that allows for practical and noise-resilient power flow analysis on today's NISQ devices.
This letter is a proof of concept for quantum power flow (QPF) algorithms which underpin various unprecedentedly efficient power system analytics exploiting quantum computing. Our contributions are three-fold: 1) Establish a quantum-state-based fast decoupled model empowered by Hermitian and constant Jacobian matrices; 2) Devise an enhanced Harrow-Hassidim-Lloyd (HHL) algorithm to solve the fast decoupled QPF; 3) Further improve the HHL efficiency by parameterizing quantum phase estimation and reciprocal rotation only at the beginning stage. Promising test results validate the accuracy and efficacy of QPF and demonstrate QPF's enormous potential in the era of quantum computing.
This letter is a proof of concept for quantum power flow (QPF) algorithms which underpin various unprecedentedly efficient power system analytics exploiting quantum computing. Our contributions are three-fold: 1) Establish a quantum-state-based fast decoupled model empowered by Hermitian and constant Jacobian matrices; 2) Devise an enhanced Harrow-Hassidim-Lloyd (HHL) algorithm to solve the fast decoupled QPF; 3) Further improve the HHL efficiency by parameterizing quantum phase estimation and reciprocal rotation only at the beginning stage. Promising test results validate the accuracy and efficacy of QPF and demonstrate QPF's enormous potential in the era of quantum computing.
This letter is a proof of concept for quantum power flow (QPF) algorithms which underpin various unprecedentedly efficient power system analytics exploiting quantum computing. Our contributions are three-fold: 1) Establish a quantum-state-based fast decoupled model empowered by Hermitian and constant Jacobian matrices; 2) Devise an enhanced Harrow-Hassidim-Lloyd (HHL) algorithm to solve the fast decoupled QPF; 3) Further improve the HHL efficiency by parameterizing quantum phase estimation and reciprocal rotation only at the beginning stage. Promising test results validate the accuracy and efficacy of QPF and demonstrate QPF's enormous potential in the era of quantum computing.
Interconnection studies require solving numerous instances of the AC load or power flow (AC PF) problem to simulate diverse scenarios as power systems navigate the ongoing energy transition. To expedite such studies, this work leverages recent advances in quantum computing to find or predict AC PF solutions using a variational quantum circuit (VQC). VQCs are trainable models that run on modern-day noisy intermediate-scale quantum (NISQ) hardware to accomplish elaborate optimization and machine learning (ML) tasks. Our first contribution is to pose a single instance of the AC PF as a nonlinear least-squares fit over the VQC trainable parameters (weights) and solve it using a hybrid classical/quantum computing approach. The second contribution is to feed PF specifications as features into a data-embedded VQC and train the resultant quantum ML (QML) model to predict general PF solutions. The third contribution is to develop a novel protocol to efficiently measure AC-PF quantum observables by exploiting the graph structure of a power network. Preliminary numerical tests indicate that the proposed VQC models attain enhanced prediction performance over a deep neural network despite using much fewer weights. The proposed quantum AC-PF framework sets the foundations for addressing more elaborate grid tasks via quantum computing.