A stabilizer framework for Contextual Subspace VQE and the noncontextual
projection ansatz
- URL: http://arxiv.org/abs/2204.02150v2
- Date: Tue, 30 Aug 2022 16:29:12 GMT
- Title: A stabilizer framework for Contextual Subspace VQE and the noncontextual
projection ansatz
- Authors: Tim Weaving, Alexis Ralli, William M. Kirby, Andrew Tranter, Peter J.
Love and Peter V. Coveney
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum chemistry is a promising application for noisy intermediate-scale
quantum (NISQ) devices. However, quantum computers have thus far not succeeded
in providing solutions to problems of real scientific significance, with
algorithmic advances being necessary to fully utilise even the modest NISQ
machines available today. We discuss a method of ground state energy estimation
predicated on a partitioning the molecular Hamiltonian into two parts: one that
is noncontextual and can be solved classically, supplemented by a contextual
component that yields quantum corrections obtained via a Variational Quantum
Eigensolver (VQE) routine. This approach has been termed Contextual Subspace
VQE (CS-VQE), but there are obstacles to overcome before it can be deployed on
NISQ devices. The problem we address here is that of the ansatz - a
parametrized quantum state over which we optimize during VQE. It is not
initially clear how a splitting of the Hamiltonian should be reflected in our
CS-VQE ans\"atze. We propose a 'noncontextual projection' approach that is
illuminated by a reformulation of CS-VQE in the stabilizer formalism. This
defines an ansatz restriction from the full electronic structure problem to the
contextual subspace and facilitates an implementation of CS-VQE that may be
deployed on NISQ devices. We validate the noncontextual projection ansatz using
a quantum simulator, with results obtained herein for a suite of trial
molecules.
Related papers
- Compact fermionic quantum state preparation with a natural-orbitalizing variational quantum eigensolving scheme [0.0]
Near-term quantum state preparation is typically realized by means of the variational quantum eigensolver (VQE) algorithm.
We present a refined VQE scheme that consists in topping VQE with state-informed updates of the elementary fermionic modes.
For a fixed circuit structure, the method is shown to enhance the capabilities of the circuit to reach a state close to the target state without incurring too much overhead from shot noise.
arXiv Detail & Related papers (2024-06-20T10:23:28Z) - Quantum subspace expansion in the presence of hardware noise [0.0]
Finding ground state energies on current quantum processing units (QPUs) continues to pose challenges.
Hardware noise severely affects both the expressivity and trainability of parametrized quantum circuits.
We show how to integrate VQE with a quantum subspace expansion, allowing for an optimal balance between quantum and classical computing capabilities and costs.
arXiv Detail & Related papers (2024-04-14T02:48:42Z) - Particle track reconstruction with noisy intermediate-scale quantum
computers [0.0]
Reconstruction of trajectories of charged particles is a key computational challenge for current and future collider experiments.
The problem can be formulated as a quadratic unconstrained binary optimization (QUBO) and solved using the variational quantum eigensolver (VQE) algorithm.
This work serves as a proof of principle that the VQE could be used for particle tracking and investigates modifications of the VQE to make it more suitable for optimization.
arXiv Detail & Related papers (2023-03-23T13:29:20Z) - 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) - Unitary Partitioning and the Contextual Subspace Variational Quantum
Eigensolver [0.0]
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.
arXiv Detail & Related papers (2022-07-07T17:28:36Z) - Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.
As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware.
We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
arXiv Detail & Related papers (2022-02-17T18:59:20Z) - 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) - On exploring practical potentials of quantum auto-encoder with
advantages [92.19792304214303]
Quantum auto-encoder (QAE) is a powerful tool to relieve the curse of dimensionality encountered in quantum physics.
We prove that QAE can be used to efficiently calculate the eigenvalues and prepare the corresponding eigenvectors of a high-dimensional quantum state.
We devise three effective QAE-based learning protocols to solve the low-rank state fidelity estimation, the quantum Gibbs state preparation, and the quantum metrology tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z) - 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) - Einselection from incompatible decoherence channels [62.997667081978825]
We analyze an open quantum dynamics inspired by CQED experiments with two non-commuting Lindblad operators.
We show that Fock states remain the most robust states to decoherence up to a critical coupling.
arXiv Detail & Related papers (2020-01-29T14:15:19Z)
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.