Multiscale quantum algorithms for quantum chemistry

Ma, Huan; Liu, Jie; Shang, Honghui; Fan, Yi; Li, Zhenyu; Yang, Jinlong

doi:10.1039/d2sc06875c

Cited by 18 publications

(12 citation statements)

References 94 publications

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“…HF HF (6) Within this transformation, the HEA circuit reaches HF energy by setting θ = 0, which serves as a good starting point for subsequent parameter optimization.…”

Section: Theory and Methodsmentioning

confidence: 99%

“…In the past decade, quantum computing has been on an extraordinary trajectory of growth and development, paving the way for the quantum simulations of molecular properties. − A groundbreaking study in 2014 successfully simulated the HeH + molecule using a 2-qubit photonic quantum processor . This pioneering work introduced the Variational Quantum Eigensolver (VQE) algorithm, , which has become the most widely adopted algorithm for the quantum simulation of molecules in the noisy intermediate-scale quantum (NISQ) era. , Building on this foundation, a research team from IBM in 2017 expanded the scope of quantum simulations to larger molecules, such as BeH 2 , by employing a 6-qubit superconducting quantum processor .…”

Section: Introductionmentioning

confidence: 99%

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Toward Chemical Accuracy with Shallow Quantum Circuits: A Clifford-Based Hamiltonian Engineering Approach

Sun,

Cheng,

2024

J. Chem. Theory Comput.

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Achieving chemical accuracy with shallow quantum circuits is a significant challenge in quantum computational chemistry, particularly for near-term quantum devices. In this work, we present a Clifford-based Hamiltonian engineering algorithm, namely CHEM, that addresses the trade-off between circuit depth and accuracy. Based on a variational quantum eigensolver and hardware-efficient ansatz, our method designs the Clifford-based Hamiltonian transformation that (1) ensures a set of initial circuit parameters corresponding to the Hartree−Fock energy can be generated, (2) effectively maximizes the initial energy gradient with respect to circuit parameters, (3) imposes negligible overhead for classical processing and does not require additional quantum resources, and ( 4) is compatible with any circuit topology. We demonstrate the efficacy of our approach using a quantum hardware emulator, achieving chemical accuracy for systems as large as 12 qubits with fewer than 30 two-qubit gates. Our Clifford-based Hamiltonian engineering approach offers a promising avenue for practical quantum computational chemistry on near-term quantum devices.

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“…HF HF (6) Within this transformation, the HEA circuit reaches HF energy by setting θ = 0, which serves as a good starting point for subsequent parameter optimization.…”

Section: Theory and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward Chemical Accuracy with Shallow Quantum Circuits: A Clifford-Based Hamiltonian Engineering Approach

Sun,

Cheng,

2024

J. Chem. Theory Comput.

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“…We then conclude this section with an overview of the state of the art of new techniques such as Machine Learning (ML) and Quantum Computing (QC), showing their potentialities and highlighting how they are already impacting the field. 32,33 The following part of this work is devoted to illustrate our vision about the evolution of multiscale modeling in the next decades. Here, we propose a novel calculation approach based on the use of multiple mobile QM centers, which can move and extend accordingly to the phenomenon under investigation, while properly interacting with the other simulation domains.…”

Section: Introductionmentioning

confidence: 99%

A Vision for the Future of Multiscale Modeling

Capone,

Romanelli,

Castaldo

et al. 2024

ACS Phys. Chem Au

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The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.

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“…Chemistry has emerged as a promising field for the application of noisy intermediate-scale quantum (NISQ) devices. − The key component for the success is the variational quantum eigensolver (VQE) framework, which leverages quantum computers as specialized devices for storing the wave function of molecular systems. − More specifically, a quantum circuit, characterized by a parametrized unitary transformation Û (θ⃗) and the initial state |ϕ 0 ⟩, is viewed as an ansatz |ϕ(θ⃗)⟩ = Û (θ⃗)|ϕ 0 ⟩ analog to the ansatz in classical computational chemistry. Leveraging the Rayleigh-Ritz variational principle, the parameters in the quantum circuit θ⃗ are optimized on a classical computer using either gradient-based or gradient-free optimizer, with the energy expectation E (θ⃗) = ⟨ϕ| Ĥ |ϕ⟩ measured on quantum computers.…”

Section: Introductionmentioning

confidence: 99%

Efficient and Robust Parameter Optimization of the Unitary Coupled-Cluster Ansatz

Li,

Ge,

Zhang

et al. 2024

J. Chem. Theory Comput.

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The variational quantum eigensolver (VQE) framework has been instrumental in advancing near-term quantum algorithms. However, parameter optimization remains a significant bottleneck for VQE, requiring a large number of measurements for successful algorithm execution. In this paper, we propose sequential optimization with approximate parabola (SOAP) as an efficient and robust optimizer specifically designed for parameter optimization of the unitary coupled-cluster ansatz on quantum computers. SOAP leverages sequential optimization and approximates the energy landscape as quadratic functions, minimizing the number of energy evaluations required to optimize each parameter. To capture parameter correlations, SOAP incorporates the average direction from previous iterations into the optimization direction set. Numerical benchmark studies on molecular systems demonstrate that SOAP achieves significantly faster convergence and greater robustness to noise compared with traditional optimization methods. Furthermore, numerical simulations of up to 20 qubits reveal that SOAP scales well with the number of parameters in the ansatz. The exceptional performance of SOAP is further validated through experiments on a superconducting quantum computer using a 2-qubit model system.

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Multiscale quantum algorithms for quantum chemistry

Abstract: Exploring the potential applications of quantum computers in material design and drug discovery is attracting more and more attention after quantum advantage has been demonstrated using Gaussian boson sampling. However, the...

Cited by 18 publications

References 94 publications

Toward Chemical Accuracy with Shallow Quantum Circuits: A Clifford-Based Hamiltonian Engineering Approach

Toward Chemical Accuracy with Shallow Quantum Circuits: A Clifford-Based Hamiltonian Engineering Approach

A Vision for the Future of Multiscale Modeling

Efficient and Robust Parameter Optimization of the Unitary Coupled-Cluster Ansatz

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