Variational Quantum Eigensolver with Reduced Circuit Complexity
- URL: http://arxiv.org/abs/2106.07619v1
- Date: Mon, 14 Jun 2021 17:23:46 GMT
- Title: Variational Quantum Eigensolver with Reduced Circuit Complexity
- Authors: Yu Zhang, Lukasz Cincio, Christian F. A. Negre, Piotr Czarnik, Patrick
Coles, Petr M. Anisimov, Susan M. Mniszewski, Sergei Tretiak, Pavel A. Dub
- Abstract summary: We present a novel approach to reduce quantum circuit complexity in VQE for electronic structure calculations.
Our algorithm, called ClusterVQE, splits the initial qubit space into subspaces (qubit clusters) which are further distributed on individual quantum circuits.
The new algorithm simultaneously reduces the number of qubits and circuit depth, making it a potential leader for quantum chemistry simulations on NISQ devices.
- Score: 3.1158760235626946
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The variational quantum eigensolver (VQE) is one of the most promising
algorithms to find eigenvalues and eigenvectors of a given Hamiltonian on noisy
intermediate-scale quantum (NISQ) devices. A particular application is to
obtain ground or excited states of molecules. The practical realization is
currently limited by the complexity of quantum circuits. Here we present a
novel approach to reduce quantum circuit complexity in VQE for electronic
structure calculations. Our algorithm, called ClusterVQE, splits the initial
qubit space into subspaces (qubit clusters) which are further distributed on
individual (shallower) quantum circuits. The clusters are obtained based on
quantum mutual information reflecting maximal entanglement between qubits,
whereas entanglement between different clusters is taken into account via a new
"dressed" Hamiltonian. ClusterVQE therefore allows exact simulation of the
problem by using fewer qubits and shallower circuit depth compared to standard
VQE at the cost of additional classical resources. In addition, a new gradient
measurement method without using an ancillary qubit is also developed in this
work. Proof-of-principle demonstrations are presented for several molecular
systems based on quantum simulators as well as an IBM quantum device with
accompanying error mitigation. The efficiency of the new algorithm is
comparable to or even improved over qubit-ADAPT-VQE and iterative Qubit Coupled
Cluster (iQCC), state-of-the-art circuit-efficient VQE methods to obtain
variational ground state energies of molecules on NISQ hardware. Above all, the
new ClusterVQE algorithm simultaneously reduces the number of qubits and
circuit depth, making it a potential leader for quantum chemistry simulations
on NISQ devices.
Related papers
- 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) - Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
We present a model for parallelizing simulation of quantum circuit executions.
The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
arXiv Detail & Related papers (2024-06-05T17:16:07Z) - A circuit-generated quantum subspace algorithm for the variational quantum eigensolver [0.0]
We propose combining quantum subspace techniques with the variational quantum eigensolver (VQE)
In our approach, the parameterized quantum circuit is divided into a series of smaller subcircuits.
The sequential application of these subcircuits to an initial state generates a set of wavefunctions that we use as a quantum subspace to obtain high-accuracy groundstate energies.
arXiv Detail & Related papers (2024-04-09T18:00:01Z) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - 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) - Modular Cluster Circuits for the Variational Quantum Eigensolver [0.0]
In the present work, we introduce a modular 2-qubit cluster circuit that allows for the design of a shallow-depth quantum circuit.
The design was tested on the H2, (H2) and LiH molecules, as well as the finite-size transverse-field Ising model.
arXiv Detail & Related papers (2023-05-08T02:33:33Z) - 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) - 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) - Fast-Forwarding with NISQ Processors without Feedback Loop [0.0]
We present the Classical Quantum Fast Forwarding (CQFF) as an alternative diagonalisation based algorithm for quantum simulation.
CQFF removes the need for a classical-quantum feedback loop and controlled multi-qubit unitaries.
Our work provides a $104$ improvement over the previous record.
arXiv Detail & Related papers (2021-04-05T14:29:33Z) - Supervised Learning Using a Dressed Quantum Network with "Super
Compressed Encoding": Algorithm and Quantum-Hardware-Based Implementation [7.599675376503671]
Implementation of variational Quantum Machine Learning (QML) algorithms on Noisy Intermediate-Scale Quantum (NISQ) devices has issues related to the high number of qubits needed and the noise associated with multi-qubit gates.
We propose a variational QML algorithm using a dressed quantum network to address these issues.
Unlike in most other existing QML algorithms, our quantum circuit consists only of single-qubit gates, making it robust against noise.
arXiv Detail & Related papers (2020-07-20T16:29:32Z)
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.