Quantum computing of the 
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 nucleus via ordered unitary coupled clusters

Kiss, Oriel; Grossi, Michele; Lougovski, Pavel; Sánchez, F.; Vallecorsa, S.; Papenbrock, T.

doi:10.1103/physrevc.106.034325

Cited by 38 publications

(25 citation statements)

References 53 publications

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“…This iterative procedure continues until convergence, defined when all the gradient norms in (7) vanish and/or when the energy is close enough to a known solution from, for instance, classical diagonalization benchmarks. While one could consider more complex operators, involving triple or quadruple particle-hole excitations [43,44], our simulations indicate that for the nuclei studied in this work full shell-model correlations can be captured at the two-body level with a commensurate number of ansatz layers, of at most a few hundred. ADAPT-VQE predicts the ground-state energy of the nucleus.…”

Section: Variational Algorithmmentioning

confidence: 82%

“…Quantum computing has the potential to overcome these limitations. Here, we implement the nuclear shell model in a quantum computer following a standard Jordan-Wigner (JW) mapping [43][44][45]. We associate each qubit with a single-particle state in the configuration space, which can either be empty (projection 0) or occupied (projection 1).…”

Section: Nuclear Shell Modelmentioning

confidence: 99%

“…We stress that the adapted wavefunction in ( 5) is free of Trotter-Suzuki approximation errors [65,66]. The ansatz does not require decomposing an exponential map of a sum of excitation operators, as would be the case in algorithms such as UCC-VQE [25,44].…”

Section: Variational Algorithmmentioning

confidence: 99%

“…Several quantum many-body systems have been used as VQE testbeds: the Fermi-Hubbard [28], Ising [29] and Lipkin-Meshkov-Glick models [30][31][32][33][34], superfluid systems [35,36], hadrons [37] or molecules [38][39][40]. VQEs have also been applied to study light nuclei with the unitary Coupled Cluster (UCC) approach [41][42][43][44], and medium-mass nuclei with an adaptive version of VQE (ADAPT-VQE) [45].…”

mentioning

confidence: 99%

“…In general, a VQE implementation requires a series of well-defined stages [24], involving a) a mapping between physical degrees of freedom (eg fermionic operators) and the qubits in a quantum computer; b) the preparation of an initial reference state; c) a (potentially iterative) variational optimization; d) a measurement strategy for expectation values of operators (most importantly, the Hamiltonian); and e) an error mitigation scheme. Previous nuclear shell-model studies have only partially tackled these problems [41][42][43][44]. The aim of this article is to present a circuit design strategy that explicitly addresses all these aspects to solve the nuclear shell model in a quantum computer.…”

mentioning

confidence: 99%

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Nuclear shell-model simulation in digital quantum computers

A.¹,

M.²,

J.³

et al. 2023

Preprint

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The nuclear shell model is one of the prime many-body methods to study the structure of atomic nuclei, but it is hampered by an exponential scaling on the basis size as the number of particles increases. We present a shell-model quantum circuit design strategy to find nuclear ground states that circumvents this limitation by exploiting an adaptive variational quantum eigensolver algorithm. Our circuit implementation is in excellent agreement with classical shell-model simulations for a dozen of light and medium-mass nuclei, including neon and calcium isotopes. We quantify the circuit depth, width and number of gates to encode realistic shell-model wavefunctions. Our strategy also addresses explicitly energy measurements and the required number of circuits to perform them. Our simulated circuits approach the benchmark results exponentially with a polynomial scaling in quantum resources for each nucleus and configuration space. This work paves the way for quantum computing shell-model studies across the nuclear chart.

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Section: Variational Algorithmmentioning

confidence: 82%

Section: Nuclear Shell Modelmentioning

confidence: 99%

Section: Variational Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Nuclear shell-model simulation in digital quantum computers

A.¹,

M.²,

J.³

et al. 2023

Preprint

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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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Clusters in light nuclei: history and recent developments

Lombardo,

Dell’Aquila

2023

Riv. Nuovo Cim.

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In this review, we discuss recent advancements in the study of clustering phenomena occurring in light nuclei, and their influence on nuclear structure, dynamics, and astrophysics. In the introduction, we outline the historical steps leading to the concept of $$\alpha $$ α -clusterization in nuclei and provide a comprehensive description of the evolution of nuclear models capable to describe clustering aspects in nuclear systems and of the main experiments and discoveries leading to the development of this research field. We also describe some spectroscopic techniques that are used to establish the clustered nature of a given nuclear state. Some relevant experimental and theoretical findings recently reported both in experimental and theoretical works are discussed in the text. We put emphasis on recent achievements and remaining problems in the description of the structure of light isotopes, from helium to neon, including both self- and non-self-conjugate nuclei. Particular attention to the implication in the nuclear astrophysics context and to clustering effects in the dynamics of nuclear reactions is pursued all along the review.

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Quantum computing of the Li6 nucleus via ordered unitary coupled clusters

Cited by 38 publications

References 53 publications

Nuclear shell-model simulation in digital quantum computers

Nuclear shell-model simulation in digital quantum computers

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

Clusters in light nuclei: history and recent developments

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