2023
DOI: 10.1103/physrevc.108.064305
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Prediction of the neutron drip line in oxygen isotopes using quantum computation

Chandan Sarma,
Olivia Di Matteo,
Abhishek Abhishek
et al.
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Cited by 7 publications
(3 citation statements)
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“…Different approaches have been used in preparing initial states for quantum simulations of nuclear systems. One recent study on predicting the neutron drip line in oxygen isotopes with VQE uses the lowest-energy Slater determinant as the reference state, with occupied orbitals represented by X gates for the ground state [25]. Another study presents an excitation-preserving ansatz for VQE, maintaining the correct number of excited orbitals and ensuring efficient quantum circuit implementation to preserve specific proton and neutron numbers [23].…”
Section: Vqe and The Wavefunction Ansatzmentioning
confidence: 99%
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“…Different approaches have been used in preparing initial states for quantum simulations of nuclear systems. One recent study on predicting the neutron drip line in oxygen isotopes with VQE uses the lowest-energy Slater determinant as the reference state, with occupied orbitals represented by X gates for the ground state [25]. Another study presents an excitation-preserving ansatz for VQE, maintaining the correct number of excited orbitals and ensuring efficient quantum circuit implementation to preserve specific proton and neutron numbers [23].…”
Section: Vqe and The Wavefunction Ansatzmentioning
confidence: 99%
“…Sequential application of G 2 can efficiently encode and manipulate information in a quantum system. In reference [25], they have also used a similar design as figure 3(a), a problem-based circuit with a linear combination of 6 computational basis states, starting with the |11 state. They have also tapered the 18 O ground state circuit to 23 CNOT gates using techniques like reducing basis states, Qiskit transpiler enhancements, and manual optimizations such as CNOT parallelization, making it suitable for near-future quantum devices.…”
Section: Vqe and The Wavefunction Ansatzmentioning
confidence: 99%
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