Quantum Circuits For Two-Dimensional Isometric Tensor Networks

• URL: http://arxiv.org/abs/2108.02792v1
• Date: Thu, 5 Aug 2021 18:00:26 GMT
• Title: Quantum Circuits For Two-Dimensional Isometric Tensor Networks
• Authors: Lucas Slattery, Bryan K. Clark
• Abstract summary: We give a detailed description of a quantum circuit version of the 2D tensor network (isoTNS) ansatz which we call qisoTNS. We benchmark the performance of qisoTNS on two different 2D spin 1/2 Hamiltonians.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The variational quantum eigensolver (VQE) combines classical and quantum resources in order simulate classically intractable quantum states. Amongst other variables, successful VQE depends on the choice of variational ansatz for a problem Hamiltonian. We give a detailed description of a quantum circuit version of the 2D isometric tensor network (isoTNS) ansatz which we call qisoTNS. We benchmark the performance of qisoTNS on two different 2D spin 1/2 Hamiltonians. We find that the ansatz has several advantages. It is qubit efficient with the number of qubits allowing for access to some exponentially large bond-dimension tensors at polynomial quantum cost. In addition, the ansatz is robust to the barren plateau problem due emergent layerwise training. We further explore the effect of noise on the efficacy of the ansatz. Overall, we find that qisoTNS is a suitable variational ansatz for 2D Hamiltonians with local interactions.

Related papers

• Quantum Deep Equilibrium Models [1.5853439776721878]
  We present Quantum Deep Equilibrium Models (QDEQ), a training paradigm that learns parameters of a quantum machine learning model. We find that QDEQ is not only competitive with comparable existing baseline models, but also achieves higher performance than a network with 5 times more layers. This demonstrates that the QDEQ paradigm can be used to develop significantly more shallow quantum circuits for a given task.
  arXiv  Detail & Related papers  (2024-10-31T13:54:37Z)
• Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
  We use FNOs to model the evolution of random quantum spin systems. We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
  arXiv  Detail & Related papers  (2024-09-05T07:18:09Z)
• 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 quantum eigensolver for closed-shell molecules with non-bosonic corrections [6.3235499003745455]
  We introduce a simple correction scheme in the electron correlation model approximated by the geometrical mean of the bosonic terms. We find our non-bosonic correction method reaches reliable quantum chemistry simulations at least for the tested systems.
  arXiv  Detail & Related papers  (2023-10-11T16:47:45Z)
• Modeling Non-Covalent Interatomic Interactions on a Photonic Quantum Computer [50.24983453990065]
  We show that the cQDO model lends itself naturally to simulation on a photonic quantum computer. We calculate the binding energy curve of diatomic systems by leveraging Xanadu's Strawberry Fields photonics library. Remarkably, we find that two coupled bosonic QDOs exhibit a stable bond.
  arXiv  Detail & Related papers  (2023-06-14T14:44:12Z)
• 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)
• Variational quantum algorithms for local Hamiltonian problems [0.0]
  Variational quantum algorithms (VQAs) are a modern family of quantum algorithms designed to solve optimization problems using a quantum computer. We primarily focus on the algorithm called variational quantum eigensolver (VQE), which takes a qubit Hamiltonian and returns its approximate ground state.
  arXiv  Detail & Related papers  (2022-08-23T22:32:56Z)
• Parameterized Two-Qubit Gates for Enhanced Variational Quantum Eigensolver [0.0]
  We simulate a variational quantum eigensolver algorithm using fixed and parameterized two-qubit gates in the circuit ansatz. We show that the parameterized versions outperform the fixed versions, both when it comes to best energy and reducing outliers, for a range of Hamiltonians with applications in quantum chemistry and materials science.
  arXiv  Detail & Related papers  (2022-03-09T19:00:01Z)
• Quantum-optimal-control-inspired ansatz for variational quantum algorithms [105.54048699217668]
  A central component of variational quantum algorithms (VQA) is the state-preparation circuit, also known as ansatz or variational form. Here, we show that this approach is not always advantageous by introducing ans"atze that incorporate symmetry-breaking unitaries. This work constitutes a first step towards the development of a more general class of symmetry-breaking ans"atze with applications to physics and chemistry problems.
  arXiv  Detail & Related papers  (2020-08-03T18:00:05Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)
• Variational Quantum Eigensolver for Frustrated Quantum Systems [0.0]
  A variational quantum eigensolver, or VQE, is designed to determine a global minimum in an energy landscape specified by a quantum Hamiltonian. Here we consider the performance of the VQE technique for a Hubbard-like model describing a one-dimensional chain of fermions. We also study the barren plateau phenomenon for the Hamiltonian in question and find that the severity of this effect depends on the encoding of fermions to qubits.
  arXiv  Detail & Related papers  (2020-05-01T18:00:01Z)

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.