Nuclear Many-Body Systems as Benchmarks for Quantum Computing
- URL: http://arxiv.org/abs/2607.08047v1
- Date: Thu, 09 Jul 2026 01:53:39 GMT
- Title: Nuclear Many-Body Systems as Benchmarks for Quantum Computing
- Abstract summary: We present a framework for benchmarking quantum algorithms for nuclear many-body systems.<n>We map nuclear interactions in second quantization formalism to qubit Hamiltonians.<n>We provide resource estimates for three representative eigenvalue algorithms.
- Score: 39.146761527401424
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a framework for benchmarking quantum algorithms for nuclear many-body systems based on realistic nuclear Hamiltonians such as chiral effective field theory. To this effect we introduce a workflow that maps nuclear interactions in second quantization formalism to qubit Hamiltonians. This enables the systematic construction of benchmark instances spanning no-core and valence-space formulations with two-body (NN) and selected three-body (3N) interactions. Then, we proceed to provide resource estimates for three representative eigenvalue algorithms: Quantum Phase Estimation, Quantum Krylov methods, and Observable Dynamic Mode Decomposition. We compare their resource requirements in terms of T-gate counts and system size, and examine the impact of model-space choices and many-body interactions. The primitives included in our analysis are Trotterization, Qubitization, and Quantum Singular Value Transformation. Our results quantify scaling trends across algorithms and problem classes, and provide a basis for consistent comparisons of quantum approaches to nuclear many-body problems. The implementation is provided by the NuQuLib software stack.
Related papers
- Fault-tolerant quantum algorithms for simulating atomic nuclei [0.0]
We construct and compile quantum algorithms to simulate the structure of atomic nuclei.<n>These algorithms are the first such estimates for fault-tolerant quantum simulation of atomic nuclei.<n>We hope this work will spur long-term collaborations between the nuclear and quantum computing community.
arXiv Detail & Related papers (2026-07-23T17:47:56Z) - Quantum Simulation of Nuclear Dynamics in First Quantization [41.99844472131922]
We provide the first complete characterization of the resource requirements for studying nuclear dynamics with the full Leading Order (LO) pionless Hamiltonian in first quantization.<n>We find that interesting simulations for low energy nuclear scattering could be achievable with tens of millions of T gates and few hundred logical qubits.
arXiv Detail & Related papers (2025-07-30T16:31:49Z) - Comparison of variational quantum eigensolvers in light nuclei [0.0]
We present classical simulations of the ground states of light atomic nuclei within the $p$ shell.<n>We compare the performance of two leading variational quantum eigensolver algorithms.
arXiv Detail & Related papers (2025-07-18T11:08:57Z) - Quantum simulations of nuclear resonances with variational methods [1.740992908651449]
This work aims to simulate nuclear resonances using quantum algorithms by developing a variational framework compatible with non-Hermitian Hamiltonians.<n>We employ the complex scaling technique to extract resonance positions classically and adapt it for quantum simulations using a two-step algorithm.<n>The results establish a scalable and efficient quantum framework for simulating resonance phenomena in nuclear systems.
arXiv Detail & Related papers (2025-04-16T01:01:56Z) - Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML)
At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
arXiv Detail & Related papers (2024-11-13T23:56:20Z) - Neutron-nucleus dynamics simulations for quantum computers [49.369935809497214]
We develop a novel quantum algorithm for neutron-nucleus simulations with general potentials.
It provides acceptable bound-state energies even in the presence of noise, through the noise-resilient training method.
We introduce a new commutativity scheme called distance-grouped commutativity (DGC) and compare its performance with the well-known qubit-commutativity scheme.
arXiv Detail & Related papers (2024-02-22T16:33:48Z) - Towards Neural Variational Monte Carlo That Scales Linearly with System
Size [67.09349921751341]
Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors.
The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems.
We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
arXiv Detail & Related papers (2022-12-21T19:00:04Z) - Grid-based methods for chemistry simulations on a quantum computer [0.0]
We employ exactly-emulated quantum computers with up to 36 qubits to execute deep yet resource-frugal algorithms.
A range of tasks is explored, from ground state preparation and energy estimation to the dynamics of scattering and ionisation.
While we identify certain restrictions and caveats, generally the grid-based method is found to perform very well.
arXiv Detail & Related papers (2022-02-11T19:11:47Z) - Assessing the Precision of Quantum Simulation of Many-Body Effects in
Atomic Systems using the Variational Quantum Eigensolver Algorithm [0.0]
This study investigates the physical effects beyond the mean-field approximation, known as electron correlation, in the ground state energies of atomic systems.
We use the classical-quantum hybrid variational quantum eigensolver (VQE) algorithm.
When more qubits become available, our study will serve as among the first steps taken towards computing other properties of interest to various applications.
arXiv Detail & Related papers (2021-01-14T11:26:32Z) - Variational Monte Carlo calculations of $\mathbf{A\leq 4}$ nuclei with
an artificial neural-network correlator ansatz [62.997667081978825]
We introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei.
We compute the binding energies and point-nucleon densities of $Aleq 4$ nuclei as emerging from a leading-order pionless effective field theory Hamiltonian.
arXiv Detail & Related papers (2020-07-28T14:52:28Z) - State preparation and measurement in a quantum simulation of the O(3)
sigma model [65.01359242860215]
We show that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site.
We apply Trotter methods to obtain results for the complexity of adiabatic ground state preparation in both the weak-coupling and quantum-critical regimes.
We present and analyze a quantum algorithm based on non-unitary randomized simulation methods.
arXiv Detail & Related papers (2020-06-28T23:44:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.