Quantum computing with and for many-body physics

Ayral, Thomas; Besserve, Pauline; Lacroix, Denis; Ruiz Guzman, Edgar Andres

doi:10.1140/epja/s10050-023-01141-1

Cited by 15 publications

(1 citation statement)

References 333 publications

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“…Finding exact solutions to such problems numerically has a computational cost that scales exponentially with the size of the system, and the computational 'classical' methods are usually unsuitable [6,7]. The fundamental goal in the above-mentioned problems is to calculate the ground-state energy of the corresponding many-body interacting fermionic Hamiltonians [8,9]. Indeed, when this goal is achieved relevant information about the physical properties of the system under investigation may be inferred.…”

Section: Introductionmentioning

confidence: 99%

Ansatz optimization of the variational quantum eigensolver tested on the atomic Anderson model

De Riso,

Cipriani,

Villani

et al. 2024

New J. Phys.

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We present a detailed analysis and optimization of the variational quantum algorithms required to find the ground state of a correlated electron model, using several types of variational ansatz. Specifically, we apply our approach to the atomic limit of the Anderson model, which is widely studied in condensed matter physics since it can simulate fundamental physical phenomena, ranging from magnetism to superconductivity. The method is developed by presenting efficient state preparation circuits that exhibit total spin, spin projection, particle number and time-reversal symmetries. These states contain the minimal number of variational parameters needed to fully span the appropriate symmetry subspace allowing to avoid irrelevant sectors of Hilbert space. Then, we show how to construct quantum circuits, providing explicit decomposition and gate count in terms of standard gate sets. We test these quantum algorithms looking at ideal quantum computer simulations as well as implementing quantum noisy simulations. We finally perform an accurate comparative analysis among the approaches implemented, highlighting their merits and shortcomings.

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Section: Introductionmentioning

confidence: 99%

Ansatz optimization of the variational quantum eigensolver tested on the atomic Anderson model

De Riso,

Cipriani,

Villani

et al. 2024

New J. Phys.

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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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