Quantum Power Flow

• URL: http://arxiv.org/abs/2104.04888v1
• Date: Sun, 11 Apr 2021 01:03:18 GMT
• Title: Quantum Power Flow
• Authors: Fei Feng, Yifan Zhou, Peng Zhang
• Abstract summary: This letter is a proof of concept for quantum power flow (QPF) algorithms. It underpins various unprecedentedly efficient power system analytics exploiting quantum computing.
• Score: 11.828274912580074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Demystifying Quantum Power Flow: Unveiling the Limits of Practical Quantum Advantage [2.8498944632323755]
  Quantum computers hold promise for solving problems intractable for classical computers. The speedup due to quantum power flow algorithms is claimed to be exponential when compared to classical PF solved by state-of-the-art algorithms. We investigate the potential for practical quantum advantage (PQA) in solving QPF compared to classical methods on gate-based quantum computers.
  arXiv  Detail & Related papers  (2024-02-13T17:36:18Z)
• QArchSearch: A Scalable Quantum Architecture Search Package [1.725192300740999]
  We present textttQArchSearch, an AI based quantum architecture search package with the textttQTensor library as a backend. We show that the search package is able to efficiently scale the search to large quantum circuits and enables the exploration of more complex models for different quantum applications.
  arXiv  Detail & Related papers  (2023-10-11T20:00:33Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• Reducing the cost of energy estimation in the variational quantum eigensolver algorithm with robust amplitude estimation [50.591267188664666]
  Quantum chemistry and materials is one of the most promising applications of quantum computing. Much work is still to be done in matching industry-relevant problems in these areas with quantum algorithms that can solve them.
  arXiv  Detail & Related papers  (2022-03-14T16:51:36Z)
• Dequantizing the Quantum Singular Value Transformation: Hardness and Applications to Quantum Chemistry and the Quantum PCP Conjecture [0.0]
  We show that the Quantum Singular Value Transformation can be efficiently "dequantized" We show that with inverse-polynomial precision, the same problem becomes BQP-complete. We also discuss how this dequantization technique may help make progress on the central quantum PCP.
  arXiv  Detail & Related papers  (2021-11-17T12:50:13Z)
• Mitigating algorithmic errors in quantum optimization through energy extrapolation [4.426846282723645]
  We present a scalable extrapolation approach to mitigating a non-negligible error in estimates of the ground state energy. We have verified the validity of these approaches through both numerical simulation and experiments on an IBM quantum computer.
  arXiv  Detail & Related papers  (2021-09-16T17:39:11Z)
• Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
  We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems. By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
  arXiv  Detail & Related papers  (2021-08-03T17:49:09Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Efficient Quantum Circuits for Accurate State Preparation of Smooth, Differentiable Functions [0.8315657895474382]
  We show that there exist families of quantum states that can be prepared to high precision with circuits of linear size and depth. We further develop an algorithm that requires only linear classical time to generate accurate linear-depth circuits.
  arXiv  Detail & Related papers  (2020-05-09T02:31:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.