Unitary Partitioning and the Contextual Subspace Variational Quantum
Eigensolver
- URL: http://arxiv.org/abs/2207.03451v2
- Date: Sat, 13 Aug 2022 10:08:48 GMT
- Title: Unitary Partitioning and the Contextual Subspace Variational Quantum
Eigensolver
- Authors: Alexis Ralli and Tim Weaving and Andrew Tranter and William M. Kirby
and Peter J. Love and Peter V. Coveney
- Abstract summary: The contextual subspace variational quantum eigensolver (CS-VQE) is a hybrid quantum-classical algorithm that approximates the ground state energy of a given qubit Hamiltonian.
We show that CS-VQE combined with measurement reduction is a promising approach to allow feasible eigenvalue computations on noisy intermediate-scale quantum devices.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The contextual subspace variational quantum eigensolver (CS-VQE) is a hybrid
quantum-classical algorithm that approximates the ground state energy of a
given qubit Hamiltonian. It achieves this by separating the Hamiltonian into
contextual and noncontextual parts. The ground state energy is approximated by
classically solving the noncontextual problem, followed by solving the
contextual problem using VQE, constrained by the noncontexual solution. In
general, computation of the contextual correction needs fewer qubits and
measurements compared to solving the full Hamiltonian via traditional VQE. We
simulate CS-VQE on different tapered molecular Hamiltonians and apply the
unitary partitioning measurement reduction strategy to further reduce the
number of measurements required to obtain the contextual correction. Our
results indicate that CS-VQE combined with measurement reduction is a promising
approach to allow feasible eigenvalue computations on noisy intermediate-scale
quantum devices. We also provide a modification to the CS-VQE algorithm, that
previously could cause an exponential increase in Hamiltonian terms, that now
at worst will scale quadratically.
Related papers
- Efficient charge-preserving excited state preparation with variational quantum algorithms [33.03471460050495]
We introduce a charge-preserving VQD (CPVQD) algorithm, designed to incorporate symmetry and the corresponding conserved charge into the VQD framework.
Results show applications in high-energy physics, nuclear physics, and quantum chemistry.
arXiv Detail & Related papers (2024-10-18T10:30:14Z) - Benchmarking Variational Quantum Eigensolvers for Entanglement Detection in Many-Body Hamiltonian Ground States [37.69303106863453]
Variational quantum algorithms (VQAs) have emerged in recent years as a promise to obtain quantum advantage.
We use a specific class of VQA named variational quantum eigensolvers (VQEs) to benchmark them at entanglement witnessing and entangled ground state detection.
Quantum circuits whose structure is inspired by the Hamiltonian interactions presented better results on cost function estimation than problem-agnostic circuits.
arXiv Detail & Related papers (2024-07-05T12:06:40Z) - Hamiltonian-reconstruction distance as a success metric for the Variational Quantum Eigensolver [1.0916270449935084]
Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm for quantum simulation that can run on near-term quantum hardware.
A challenge in VQE is to know how close the algorithm's output solution is to the true ground state, when the true ground state and ground-state energy are unknown.
Recent developments in Hamiltonian reconstruction give a metric can be used to assess the quality of a variational solution to a Hamiltonian-eigensolving problem.
arXiv Detail & Related papers (2024-03-18T17:28:06Z) - Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the
Quantum Many-Body Schr\"odinger Equation [56.9919517199927]
"Wasserstein Quantum Monte Carlo" (WQMC) uses the gradient flow induced by the Wasserstein metric, rather than Fisher-Rao metric, and corresponds to transporting the probability mass, rather than teleporting it.
We demonstrate empirically that the dynamics of WQMC results in faster convergence to the ground state of molecular systems.
arXiv Detail & Related papers (2023-07-06T17:54:08Z) - 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) - 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) - A stabilizer framework for Contextual Subspace VQE and the noncontextual
projection ansatz [0.0]
We discuss a method of ground state energy estimation predicated on a partitioning the molecular Hamiltonian into two parts.
This approach has been termed Contextual Subspace VQE (CS-VQE), but there are obstacles to overcome before it can be deployed on NISQ devices.
We propose a 'noncontextual projection' approach that is illuminated by a reformulation of CS-VQE in the stabilizer formalism.
arXiv Detail & Related papers (2022-04-05T12:19:37Z) - 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) - Contextual Subspace Variational Quantum Eigensolver [0.0]
We describe a hybrid quantum-classical algorithm for approximating the ground state energy of a Hamiltonian.
The number of qubits required to reach chemical accuracy can be reduced by more than a factor of two.
This indicates that CS-VQE is a promising approach for eigenvalue computations on noisy intermediate-scale quantum devices.
arXiv Detail & Related papers (2020-11-19T18:49:30Z) - 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) - 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.