Noise-Mitigated Variational Quantum Eigensolver with Pre-training and Zero-Noise Extrapolation

• URL: http://arxiv.org/abs/2501.01646v2
• Date: Mon, 27 Jan 2025 02:01:48 GMT
• Title: Noise-Mitigated Variational Quantum Eigensolver with Pre-training and Zero-Noise Extrapolation
• Authors: Wanqi Sun, Jungang Xu, Chenghua Duan,
• Abstract summary: We develop an efficient noise-mitigating variational quantum eigensolver for accurate computation of molecular ground state energies in noisy environments. We employ zero-noise extrapolation to mitigate quantum noise and combine it with neural networks to improve the accuracy of the noise-fitting function. Results show that our algorithm can constrain noise errors within the range of $mathcalO(10-2) sim mathcalO(10-1)$, outperforming mainstream variational quantum eigensolvers.
• Score: 3.205475178870021
• License:
• Abstract: As a hybrid quantum-classical algorithm, the variational quantum eigensolver is widely applied in quantum chemistry simulations, especially in computing the electronic structure of complex molecular systems. However, on existing noisy intermediate-scale quantum devices, some factors such as quantum decoherence, measurement errors, and gate operation imprecisions are unavoidable. To overcome these challenges, this study proposes an efficient noise-mitigating variational quantum eigensolver for accurate computation of molecular ground state energies in noisy environments. We design the quantum circuit with reference to the structure of matrix product states and utilize it to pre-train the circuit parameters, which ensures circuit stability and mitigates fluctuations caused by initialization. We also employ zero-noise extrapolation to mitigate quantum noise and combine it with neural networks to improve the accuracy of the noise-fitting function, which significantly eliminates noise interference. Furthermore, we implement an intelligent grouping strategy for measuring Hamiltonian Pauli strings, which not only reduces measurement errors but also improves sampling efficiency. We perform numerical simulations to solve the ground state energy of the $H_4$ molecule by using MindSpore Quantum framework, and the results demonstrate that our algorithm can constrain noise errors within the range of $\mathcal{O}(10^{-2}) \sim \mathcal{O}(10^{-1})$, outperforming mainstream variational quantum eigensolvers. This work provides a new strategy for high-precision quantum chemistry calculations on near-term noisy quantum hardware. The updated code is available at https://gitee.com/mindspore/mindquantum/tree/research/paper_with_code/eigensolver_with_mps_and_ZNE or https://github.com/mindspore-lab/models/tree/master/research/arxiv_papers/MPS_eigensolver_with_ZNE.

Related papers

• Full Band Structure Calculation of Semiconducting Materials on a Noisy Quantum Processor [2.6327434138210095]
  We propose the Reduced Quantum Equation-of-Motion method to find the entire energy spectrum of a quantum system. We analyse the performance of our method on two noise models and calculate the excitation energies of a bulk Silicon and Gallium Arsenide using our method on an IBM quantum processor.
  arXiv  Detail & Related papers  (2024-05-15T06:35:39Z)
• Simulating electronic structure on bosonic quantum computers [34.84696943963362]
  We propose an approach to map the electronic Hamiltonian into a qumode bosonic problem that can be solved on bosonic quantum devices. This work establishes a new pathway for simulating many-fermion systems, highlighting the potential of hybrid qubit-qumode quantum devices.
  arXiv  Detail & Related papers  (2024-04-16T02:04:11Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Variational Denoising for Variational Quantum Eigensolver [0.28675177318965045]
  The variational quantum eigensolver (VQE) is a hybrid algorithm that has the potential to provide a quantum advantage in practical chemistry problems. VQE faces challenges in task-specific design and machine-specific architecture, particularly when running on noisy quantum devices. We propose variational denoising, an unsupervised learning method that employs a parameterized quantum neural network to improve the solution of VQE.
  arXiv  Detail & Related papers  (2023-04-02T14:56:15Z)
• Matrix product channel: Variationally optimized quantum tensor network to mitigate noise and reduce errors for the variational quantum eigensolver [0.0]
  We develop a method to exploit the quantum-classical interface provided by informationally complete measurements. We argue that a hybrid strategy of using the quantum hardware together with the classical software outperforms a purely classical strategy. The algorithm can be applied as the final postprocessing step in the quantum hardware simulation of protein-ligand complexes in the context of drug design.
  arXiv  Detail & Related papers  (2022-12-20T13:03:48Z)
• Numerical Simulations of Noisy Quantum Circuits for Computational Chemistry [51.827942608832025]
  Near-term quantum computers can calculate the ground-state properties of small molecules. We show how the structure of the computational ansatz as well as the errors induced by device noise affect the calculation.
  arXiv  Detail & Related papers  (2021-12-31T16:33:10Z)
• Simulating the Mott transition on a noisy digital quantum computer via Cartan-based fast-forwarding circuits [62.73367618671969]
  Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model. Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models. This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
  arXiv  Detail & Related papers  (2021-12-10T17:32:15Z)
• Model-Independent Error Mitigation in Parametric Quantum Circuits and Depolarizing Projection of Quantum Noise [1.5162649964542718]
  Finding ground states and low-lying excitations of a given Hamiltonian is one of the most important problems in many fields of physics. quantum computing on Noisy Intermediate-Scale Quantum (NISQ) devices offers the prospect to efficiently perform such computations. Current quantum devices still suffer from inherent quantum noise.
  arXiv  Detail & Related papers  (2021-11-30T16:08:01Z)
• 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)
• An Algebraic Quantum Circuit Compression Algorithm for Hamiltonian Simulation [55.41644538483948]
  Current generation noisy intermediate-scale quantum (NISQ) computers are severely limited in chip size and error rates. We derive localized circuit transformations to efficiently compress quantum circuits for simulation of certain spin Hamiltonians known as free fermions. The proposed numerical circuit compression algorithm behaves backward stable and scales cubically in the number of spins enabling circuit synthesis beyond $mathcalO(103)$ spins.
  arXiv  Detail & Related papers  (2021-08-06T19:38:03Z)
• Pulse-level noisy quantum circuits with QuTiP [53.356579534933765]
  We introduce new tools in qutip-qip, QuTiP's quantum information processing package. These tools simulate quantum circuits at the pulse level, leveraging QuTiP's quantum dynamics solvers and control optimization features. We show how quantum circuits can be compiled on simulated processors, with control pulses acting on a target Hamiltonian.
  arXiv  Detail & Related papers  (2021-05-20T17:06:52Z)

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.