Scalable nuclear shell model calculations on noisy quantum computers

• URL: http://arxiv.org/abs/2608.16371v1
• Date: Mon, 17 Aug 2026 10:23:18 GMT
• Title: Scalable nuclear shell model calculations on noisy quantum computers
• Abstract summary: Hybrid quantum algorithms like the Variational Quantum Eigensolver (VQE) aim to overcome limits in quantum computing.<n>The noise inherent in the current Noisy Intermediate-Scale Quantum (NISQ) hardware renders several algorithms, such as the VQE, impractical for large-scale calculations.<n>We apply the Sample-based Quantum Diagonalization (SQD) framework to nuclear shell models for the first time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The exact diagonalization of the nuclear shell model scales exponentially, leading to severe memory bottlenecks in classical high-performance computing (HPC). While hybrid quantum algorithms like the Variational Quantum Eigensolver (VQE) aim to overcome these limits, their deep quantum circuits and iterative feedback loops are susceptible to substantial noise inherent in the current Noisy Intermediate-Scale Quantum (NISQ) hardware. This noise renders several algorithms, such as the VQE, impractical for large-scale calculations despite sophisticated noise-mitigation techniques. As a pragmatic approach tolerant to these issues, we apply the Sample-based Quantum Diagonalization (SQD) framework to nuclear shell models for the first time. Using $^{38}\text{Ar}$ as a benchmark to confirm the numerical accuracy, we extend SQD to $^{32}\text{Mg}$, solving a nuclear shell-model Hamiltonian whose underlying Hilbert space cannot be directly diagonalized using conventional classical methods in a given HPC system. We present a systematic comparison of SQD with standard variational quantum schemes and exact classical solvers. By leveraging NISQ hardware connected via the cloud to classical HPC clusters, the SQD-based scheme could outperform conventional supercomputers in memory scaling and total execution time, enabling more rigorous large-scale shell model calculations.

Related papers

• Hybrid Quantum Algorithms for Computational Chemistry: Application to the Pyridine-Li ion Complex [0.0]
  Three quantum algorithms are investigated for capturing electron correlation in large-scale molecular systems.<n>Results show how new generations of hybrid quantum-classical frameworks overcome the scalability and noise sensitivity that constrain conventional VQE approaches.<n>Both SQD and HI-VQE exhibit robustness against hardware noise, a critical improvement over earlier approaches.
  arXiv  Detail & Related papers  (2026-01-15T02:23:09Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations [44.13836547616739]
  Variational Quantum Algorithms (VQAs) are a promising approach to leverage Noisy Intermediate-Scale Quantum (NISQ) computers.<n> choosing optimal quantum circuits that efficiently solve a given VQA problem is a non-trivial task.<n>Quantum Architecture Search (QAS) algorithms enable automatic generation of quantum circuits tailored to the provided problem.
  arXiv  Detail & Related papers  (2025-06-04T08:30:35Z)
• Practical Application of the Quantum Carleman Lattice Boltzmann Method in Industrial CFD Simulations [44.99833362998488]
  This work presents a practical numerical assessment of a hybrid quantum-classical approach to CFD based on the Lattice Boltzmann Method (LBM)<n>We evaluate this method on three benchmark cases featuring different boundary conditions, periodic, bounceback, and moving wall.<n>Our results confirm the validity of the approach, achieving median error fidelities on the order of $10-3$ and success probabilities sufficient for practical quantum state sampling.
  arXiv  Detail & Related papers  (2025-04-17T15:41:48Z)
• 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)
• Memory-Augmented Hybrid Quantum Reservoir Computing [0.0]
  We present a hybrid quantum-classical approach that implements memory through classical post-processing of quantum measurements. We tested our model on two physical platforms: a fully connected Ising model and a Rydberg atom array.
  arXiv  Detail & Related papers  (2024-09-15T22:44:09Z)
• Sparse Quantum State Preparation for Strongly Correlated Systems [0.0]
  In principle, the encoding of the exponentially scaling many-electron wave function onto a linearly scaling qubit register offers a promising solution to overcome the limitations of traditional quantum chemistry methods. An essential requirement for ground state quantum algorithms to be practical is the initialisation of the qubits to a high-quality approximation of the sought-after ground state. Quantum State Preparation (QSP) allows the preparation of approximate eigenstates obtained from classical calculations, but it is frequently treated as an oracle in quantum information.
  arXiv  Detail & Related papers  (2023-11-06T18:53:50Z)
• Fighting noise with noise: a stochastic projective quantum eigensolver [0.0]
  We present a novel approach to estimating physical observables which leads to a two order of magnitude reduction in the required sampling of the quantum state. The method can be applied to excited-state calculations and simulation for general chemistry on quantum devices.
  arXiv  Detail & Related papers  (2023-06-26T09:22:06Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• Hybrid Quantum Classical Simulations [0.0]
  We report on two major hybrid applications of quantum computing, namely, the quantum approximate optimisation algorithm (QAOA) and the variational quantum eigensolver (VQE) Both are hybrid quantum classical algorithms as they require incremental communication between a classical central processing unit and a quantum processing unit to solve a problem.
  arXiv  Detail & Related papers  (2022-10-06T10:49:15Z)
• Reducing Unitary Coupled Cluster Circuit Depth by Classical Stochastic Amplitude Pre-Screening [0.0]
  Unitary Coupled Cluster (UCC) approaches are an appealing route to utilising quantum hardware to perform quantum chemistry calculations. We present a combined classical-quantum approach where a classical UCC pre-processing step is used to determine the important excitations in the UCC ansatz.
  arXiv  Detail & Related papers  (2021-08-24T18:34:14Z)
• Accelerating variational quantum algorithms with multiple quantum processors [78.36566711543476]
  Variational quantum algorithms (VQAs) have the potential of utilizing near-term quantum machines to gain certain computational advantages. Modern VQAs suffer from cumbersome computational overhead, hampered by the tradition of employing a solitary quantum processor to handle large data. Here we devise an efficient distributed optimization scheme, called QUDIO, to address this issue.
  arXiv  Detail & Related papers  (2021-06-24T08:18:42Z)

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.