Deep variational quantum eigensolver for excited states and its
application to quantum chemistry calculation of periodic materials
- URL: http://arxiv.org/abs/2104.00855v2
- Date: Fri, 27 Aug 2021 03:14:51 GMT
- Title: Deep variational quantum eigensolver for excited states and its
application to quantum chemistry calculation of periodic materials
- Authors: Kaoru Mizuta, Mikiya Fujii, Shigeki Fujii, Kazuhide Ichikawa, Yutaka
Imamura, Yukihiro Okuno, and Yuya O. Nakagawa
- Abstract summary: Variational Quantum Eigensolver (VQE) is one of the most promising ways to utilize the computational power of quantum devices.
We extend the original proposal of Deep VQE to obtain the excited states and apply it to quantum chemistry calculation of a periodic material.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A programmable quantum device that has a large number of qubits without
fault-tolerance has emerged recently. Variational Quantum Eigensolver (VQE) is
one of the most promising ways to utilize the computational power of such
devices to solve problems in condensed matter physics and quantum chemistry. As
the size of the current quantum devices is still not large for rivaling
classical computers at solving practical problems, Fujii et al. proposed a
method called "Deep VQE" which can provide the ground state of a given quantum
system with the smaller number of qubits by combining the VQE and the technique
of coarse-graining [K. Fujii, et al, arXiv:2007.10917]. In this paper, we
extend the original proposal of Deep VQE to obtain the excited states and apply
it to quantum chemistry calculation of a periodic material, which is one of the
most impactful applications of the VQE. We first propose a modified scheme to
construct quantum states for coarse-graining in Deep VQE to obtain the excited
states. We also present a method to avoid a problem of meaningless eigenvalues
in the original Deep VQE without restricting variational quantum states.
Finally, we classically simulate our modified Deep VQE for quantum chemistry
calculation of a periodic hydrogen chain as a typical periodic material. Our
method reproduces the ground-state energy and the first-excited-state energy
with the errors up to O(1)% despite the decrease in the number of qubits
required for the calculation by two or four compared with the naive VQE. Our
result will serve as a beacon for tackling quantum chemistry problems with
classically-intractable sizes by smaller quantum devices in the near future.
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) - Non-unitary Coupled Cluster Enabled by Mid-circuit Measurements on Quantum Computers [37.69303106863453]
We propose a state preparation method based on coupled cluster (CC) theory, which is a pillar of quantum chemistry on classical computers.
Our approach leads to a reduction of the classical computation overhead, and the number of CNOT and T gates by 28% and 57% on average.
arXiv Detail & Related papers (2024-06-17T14:10:10Z) - Rapidly Achieving Chemical Accuracy with Quantum Computing Enforced Language Model [22.163742052849432]
QiankunNet-VQE is a transformer based language models enforced with quantum computing to learn and generate quantum states.
It has been implemented using up to 12 qubits and attaining an accuracy level competitive with state-of-the-art classical methods.
arXiv Detail & Related papers (2024-05-15T07:50:57Z) - 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) - Mitigating Errors on Superconducting Quantum Processors through Fuzzy
Clustering [38.02852247910155]
A new Quantum Error Mitigation (QEM) technique uses Fuzzy C-Means clustering to specifically identify measurement error patterns.
We report a proof-of-principle validation of the technique on a 2-qubit register, obtained as a subset of a real NISQ 5-qubit superconducting quantum processor.
We demonstrate that the FCM-based QEM technique allows for reasonable improvement of the expectation values of single- and two-qubit gates based quantum circuits.
arXiv Detail & Related papers (2024-02-02T14:02:45Z) - 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) - On the feasibility of performing quantum chemistry calculations on quantum computers [0.0]
We propose two criteria for evaluating two leading quantum approaches for finding the ground state of molecules.
The first criterion applies to the variational quantum eigensolver (VQE) algorithm.
The second criterion applies to the quantum phase estimation (QPE) algorithm.
arXiv Detail & Related papers (2023-06-05T06:41:22Z) - Quantum Imitation Learning [74.15588381240795]
We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL.
We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL)
Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
arXiv Detail & Related papers (2023-04-04T12:47:35Z) - Improved variational quantum eigensolver via quasi-dynamical evolution [0.0]
The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for current and near-term quantum devices.
There are problems with VQE that forbid a favourable scaling towards quantum advantage.
We propose and extensively test a quantum annealing inspired algorithm that supplements VQE.
The improved VQE avoids barren plateaus, exits local minima, and works with low-depth circuits.
arXiv Detail & Related papers (2022-02-21T11:21:44Z) - Molecular Excited State Calculations with Adaptive Wavefunctions on a
Quantum Eigensolver Emulation: Reducing Circuit Depth and Separating Spin
States [0.0]
Variational Quantum Deflation (VQD) is an extension of the Variational Quantum Eigensolver (VQE) for calculating electronic excited state energies.
We investigate the use of adaptive quantum circuit growth (ADAPT-VQE) in excited state VQD calculations.
arXiv Detail & Related papers (2021-05-21T10:59:29Z) - Hybrid quantum-classical algorithms and quantum error mitigation [0.688204255655161]
Google recently achieved quantum supremacy by using a noisy intermediate-scale quantum device with over 50 qubits.
This article reviews the basic results for hybrid quantum-classical algorithms and quantum error mitigation techniques.
arXiv Detail & Related papers (2020-11-02T23:34:22Z)
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.