Tangent Vector Variational Quantum Eigensolver: A Robust Variational Quantum Eigensolver against the inaccuracy of derivative

• URL: http://arxiv.org/abs/2105.01141v3
• Date: Wed, 17 May 2023 03:55:56 GMT
• Title: Tangent Vector Variational Quantum Eigensolver: A Robust Variational Quantum Eigensolver against the inaccuracy of derivative
• Authors: Hikaru Wakaura, Andriyan Bayu Suksmono
• Abstract summary: It is no doubt that Fault-Tolerant-Quantum-Computer (FTQC) will be realized in the near future. FTQC requires 10,000 physical qubits for every 100 logical ones. The Variational Quantum Eigensolver (VQE) method will be used until large-scale FTQC with more than 100 logical qubits are realized.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Observing rapid developments of both the number of qubits and quantum volume, especially with recent advances in ion-trap quantum computers, it is no doubt that Fault-Tolerant-Quantum-Computer (FTQC) will be realized in the near future. Since FTQC requires 10,000 physical qubits for every 100 logical ones, it will be used as the first large-scale Noisy-Intermediate-Scale-Quantum (NISQ) . The Variational Quantum Eigensolver (VQE) method will be used until large-scale FTQC with more than 100 logical qubits are realized. Therefore, the VQE method must be improved with respect to both accuracy and time to solution using large resource of the near FTQC . In this paper, we propose Tangent-Vector VQE (TVVQE) method to manage these issues. The method optimizes the norm of tangent vector of trial energy. We demonstrate the calculation of energy levels on Hydrogen molecule, Hubbard model, and Lithium Hydride molecule and reveal that TVVQE has a potential to calculate ground and excited energy levels more accurately than other VQE methods.

Related papers

• Real-time error mitigation for variational optimization on quantum hardware [45.935798913942904]
  We define a Real Time Quantum Error Mitigation (RTQEM) algorithm to assist in fitting functions on quantum chips with VQCs. Our RTQEM routine can enhance VQCs' trainability by reducing the corruption of the loss function.
  arXiv  Detail & Related papers  (2023-11-09T19:00:01Z)
• QNEAT: Natural Evolution of Variational Quantum Circuit Architecture [95.29334926638462]
  We focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks. Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture. We propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC.
  arXiv  Detail & Related papers  (2023-04-14T08:03:20Z)
• Quantum computing quantum Monte Carlo with hybrid tensor network for electronic structure calculations [3.642843803494657]
  We propose an algorithm combining QC-QMC with a hybrid tensor network to extend the applicability of QC-QMC beyond a single quantum device size. Our algorithm is evaluated on the Heisenberg chain model, graphite-based Hubbard model, hydrogen plane model, and MonoArylBiImidazole using full configuration interaction QMC.
  arXiv  Detail & Related papers  (2023-03-31T14:38:40Z)
• TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework [59.07246314484875]
  TeD-Q is an open-source software framework for quantum machine learning. It seamlessly integrates classical machine learning libraries with quantum simulators. It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
  arXiv  Detail & Related papers  (2023-01-13T09:35:05Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding [44.89303833148191]
  We develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs) We propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs. In an image classification task, extensive simulations corroborate the effectiveness of eSQFL.
  arXiv  Detail & Related papers  (2022-12-04T03:18:03Z)
• Exploring the scaling limitations of the variational quantum eigensolver with the bond dissociation of hydride diatomic molecules [0.0]
  Materials simulations involving strongly correlated electrons pose fundamental challenges to state-of-the-art electronic structure methods. No quantum computer has simulated a molecule of a size and complexity relevant to real-world applications, despite the fact that the variational quantum eigensolver algorithm can predict chemically accurate total energies. We show that the inclusion of d-orbitals and the use of the UCCSD ansatz, which are both necessary to capture the correct TiH physics, dramatically increase the cost of this problem.
  arXiv  Detail & Related papers  (2022-08-15T19:21:17Z)
• Improved variational quantum eigensolver via quasi-dynamical evolution [0.0]
  The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for current and near-term quantum devices. There are problems with VQE that forbid a favourable scaling towards quantum advantage. We propose and extensively test a quantum annealing inspired algorithm that supplements VQE. The improved VQE avoids barren plateaus, exits local minima, and works with low-depth circuits.
  arXiv  Detail & Related papers  (2022-02-21T11:21:44Z)
• The Cost of Improving the Precision of the Variational Quantum Eigensolver for Quantum Chemistry [0.0]
  We study how various types of errors affect the variational quantum eigensolver (VQE) We find that the optimal way of running the hybrid classical-quantum optimization is to allow some noise in intermediate energy evaluations.
  arXiv  Detail & Related papers  (2021-11-09T06:24:52Z)
• A prescreening method for variational quantum state eigensolver [0.0]
  We propose a method to derive all of the states with high accuracy by using the Variational Quantum State Eigensolver (VQSE) and Subspace-Search VQE (SSVQE) methods. We show that by using the VQSE and the SSVQE prescreening methods, we can derive all of the hydrogen molecules states correctly.
  arXiv  Detail & Related papers  (2021-11-03T18:13:33Z)
• Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
  We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves. We find both methods provide good estimates of the energy and ground state. gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
  arXiv  Detail & Related papers  (2020-11-02T19:52:04Z)

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.