Solving nuclear structure problems with the adaptive variational quantum algorithm

Romero, A. M.; Engel, J.; Tang, Ho Lun; Economou, Sophia E.

doi:10.1103/physrevc.105.064317

Cited by 27 publications

(19 citation statements)

References 29 publications

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“…Due to the geometry of the system, no lenght scale of the correlation can be defined, this makes the fullyconnected spin models interesting systems to look for unconventional results at finite-size [25,39] or to give a quantitative evaluation of the quality of a new computational or experimental technique [26,30,35]. Recently, several papers [52][53][54][55] addressed the Lipkin model on a quantum computer to question whether techniques and methods proper of quantum machine learning can be employed in nuclear physics. In general, the authors rely on system size of relatively small dimensions N ≤ 4 performing a preliminary noiseless analysis for the isotropic model that can be analytically solved exactly.…”

Section: Discussion and Outlooksmentioning

confidence: 99%

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Finite-size criticality in fully connected spin models on superconducting quantum hardware

Grossi¹,

Kiss²,

Luca³

et al. 2022

Preprint

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Section: Discussion and Outlooksmentioning

confidence: 99%

“…The VQE has been widely applied in quantum chemistry [43][44][45][46], nuclear physics [47][48][49] and in spin systems [50][51][52][53][54][55][56].…”

Section: B Variational Quantum Eigensolvermentioning

confidence: 99%

Finite-size criticality in fully connected spin models on superconducting quantum hardware

Grossi¹,

Kiss²,

Luca³

et al. 2022

Preprint

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“…The separation of scales in realistic systems means that the ideas developed within the LMG model have also been helpful in those systems. Its rich phenomenology from a simple Hamiltonian provides sufficient complexity that has led to a number of previous studies that explore quantum correlations and entanglement [33][34][35][40][41][42], quantum algorithms [43][44][45][46], and more, to develop understanding and techniques that can be applied to quantum simulations of nuclei and multi-nucleon systems. Previous quantum simulations of the LMG model [43][44][45][46] have directly mapped the elementary SU(2)-spaces associated with each fermion to qubits in the quantum computer (or classical simulator).…”

Section: Introductionmentioning

confidence: 99%

“…Its rich phenomenology from a simple Hamiltonian provides sufficient complexity that has led to a number of previous studies that explore quantum correlations and entanglement [33][34][35][40][41][42], quantum algorithms [43][44][45][46], and more, to develop understanding and techniques that can be applied to quantum simulations of nuclei and multi-nucleon systems. Previous quantum simulations of the LMG model [43][44][45][46] have directly mapped the elementary SU(2)-spaces associated with each fermion to qubits in the quantum computer (or classical simulator). In this way VQE has been used to determine the ground state of few-nucleon systems in the model [43] using IBM's quantum computers [47], and ADAPT-VQE [48,49] has been used to examine systems of up to N = 12 nucleons using a classical simulator [45].…”

Section: Introductionmentioning

confidence: 99%

“…Previous quantum simulations of the LMG model [43][44][45][46] have directly mapped the elementary SU(2)-spaces associated with each fermion to qubits in the quantum computer (or classical simulator). In this way VQE has been used to determine the ground state of few-nucleon systems in the model [43] using IBM's quantum computers [47], and ADAPT-VQE [48,49] has been used to examine systems of up to N = 12 nucleons using a classical simulator [45]. That extensive study explored the behavior of ADAPT-VQE building upon the trivial 0p-0h state and also the Hartee-Fock ground state.…”

Section: Introductionmentioning

confidence: 99%

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Quantum Simulations in Effective Model Spaces (I): Hamiltonian Learning-VQE using Digital Quantum Computers and Application to the Lipkin-Meshkov-Glick Model

P.¹,

Savage²

2023

Preprint

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The utility of effective model spaces in quantum simulations of non-relativistic quantum manybody systems is explored in the context of the Lipkin-Meshkov-Glick model of interacting fermions. We introduce an iterative hybrid-classical-quantum algorithm, Hamiltonian learning variational quantum eigensolver (HL-VQE), that simultaneously optimizes an effective Hamiltonian, thereby rearranging entanglement into the effective model space, and the associated ground-state wavefunction. HL-VQE is found to provide an exponential improvement in Lipkin-Meshkov-Glick model calculations, compared to a naive truncation without Hamiltonian learning, throughout a significant fraction of the Hilbert space. Quantum simulations are performed to demonstrate the HL-VQE algorithm, using an efficient mapping where the number of qubits scales with the log of the size of the effective model space, rather than the particle number, allowing for the description of large systems with small quantum circuits. Implementations on IBM's QExperience quantum computers and simulators for 1-and 2-qubit effective model spaces are shown to provide accurate and precise results, reproducing classical predictions. This work constitutes a step in the development of entanglement-driven quantum algorithms for the description of nuclear systems, that leverages the potential of noisy intermediate-scale quantum (NISQ) devices.

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Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning

García‐Ramos,

Sáiz,

Arias

et al. 2024

Adv Quantum Tech

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In this paper, the application of quantum simulations and quantum machine learning is explored to solve problems in low‐energy nuclear physics. The use of quantum computing to address nuclear physics problems is still in its infancy, and particularly, the application of quantum machine learning (QML) in the realm of low‐energy nuclear physics is almost nonexistent. Three specific examples are presented where the utilization of quantum computing and QML provides, or can potentially provide in the future, a computational advantage: i) determining the phase/shape in schematic nuclear models, ii) calculating the ground state energy of a nuclear shell model‐type Hamiltonian, and iii) identifying particles or determining trajectories in nuclear physics experiments.

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Solving nuclear structure problems with the adaptive variational quantum algorithm

Cited by 27 publications

References 29 publications

Finite-size criticality in fully connected spin models on superconducting quantum hardware

Finite-size criticality in fully connected spin models on superconducting quantum hardware

Quantum Simulations in Effective Model Spaces (I): Hamiltonian Learning-VQE using Digital Quantum Computers and Application to the Lipkin-Meshkov-Glick Model

Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning

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