Doubling the size of quantum selected configuration interaction based on seniority-zero space and its application to QC-QSCI-AFQMC

• URL: http://arxiv.org/abs/2602.07912v1
• Date: Sun, 08 Feb 2026 11:16:39 GMT
• Title: Doubling the size of quantum selected configuration interaction based on seniority-zero space and its application to QC-QSCI-AFQMC
• Authors: Yuichiro Yoshida, Takuma Murokoshi, Naoya Kuroda, Wataru Mizukami,
• Abstract summary: We propose a doubly occupied configuration interaction-quantum selected configuration interaction (DOCI-QSCI)<n>It samples from the seniority-zero space, but sector restriction can compromise quantitative accuracy.<n>We show that the DOCI-QSCI doubles the orbital space accessible to conventional QSCI and subsequent ph-AFQMC post-processing delivers reasonably high accuracy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose doubly occupied configuration interaction-quantum selected configuration interaction (DOCI-QSCI), which samples from the seniority-zero space. While the use of this space effectively doubles the qubit budget, equaling the number of spatial orbitals, this sector restriction can compromise quantitative accuracy. To compensate for this, we expand sampled bitstrings via their Cartesian product into a larger space that includes seniority-breaking determinants. The resulting wave function is also proposed using the trial state in phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) to recover dynamical correlations across the full orbital space (DOCI-QSCI-AFQMC). We evaluate the proposed methods on the H6 chain, N2 dissociation, and the addition of singlet O2 to a BODIPY dye. For the H6 chain, DOCI-QSCI-AFQMC reproduces the accuracy of the level of the complete-active-space counterpart with the quantum device ibm kobe. For N2 and BODIPY-O2, with (14e, 28o) and up to (20e, 20o) active spaces, it yields reasonable results, whereas single-reference CCSD(T) fails qualitatively. These results demonstrate that the DOCI-QSCI doubles the orbital space accessible to conventional QSCI and subsequent ph-AFQMC post-processing delivers reasonably high accuracy.

Related papers

• Noise-Resilient Quantum Chemistry with Half the Qubits [0.0]
  We introduce HSQD, a novel half-qubit quantum diagonalization approach that halves the qubit requirement for simulating a chemical system.<n>HSQD matches the accuracy of SQD on IBM quantum hardware using only half the number of qubits and 40% fewer measurements.<n>Results establish half-qubit SQD as a noise-resilient and resource-efficient pathway toward practical quantum advantage in strongly correlated chemistry.
  arXiv  Detail & Related papers  (2026-02-01T11:34:37Z)
• Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning [45.92935470813908]
  Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
  arXiv  Detail & Related papers  (2025-10-15T12:17:23Z)
• Hybrid Sequential Quantum Computing [33.72751145910978]
  We introduce hybrid sequential quantum computing (HSQC)<n>HSQC systematically integrates classical and quantum methods within a structured, stage-wise workflow.<n>Compared to standalone classical solvers, HSQC achieves a speedup of up to 700 times over SA and up to 9 times over runtime.
  arXiv  Detail & Related papers  (2025-10-07T12:15:43Z)
• Quantum Sequential Universal Hypothesis Testing [62.751483592497806]
  Quantum hypothesis testing (QHT) concerns the statistical inference of unknown quantum states.<n>We introduce the quantum sequential universal test (QSUT), a novel framework for sequential QHT in the general case of composite hypotheses.<n> QSUT builds on universal inference, and it alternates between adaptive local measurements aimed at exploring the hypothesis space and joint measurements optimized for maximal discrimination.
  arXiv  Detail & Related papers  (2025-08-29T12:50:04Z)
• Solving the Hubbard model with Neural Quantum States [66.55653324211542]
  We study the state-of-the-art results for the doped two-dimensional (2D) Hubbard model.<n>We find different attention heads in the NQS ansatz can directly encode correlations at different scales.<n>Our work establishes NQS as a powerful tool for solving challenging many-fermions systems.
  arXiv  Detail & Related papers  (2025-07-03T14:08:25Z)
• Coupled cluster method tailored by quantum selected configuration interaction [0.0]
  We present a hybrid quantum-classical scheme that tailors coupled-cluster (CC) theory with a quantum-selected configuration interaction (QSCI) wave function.<n>QSCI provides a scalable, shot-efficient approach to reconstructing the many-electron state prepared on quantum hardware.
  arXiv  Detail & Related papers  (2025-06-20T11:07:26Z)
• Auxiliary-field quantum Monte Carlo method with quantum selected configuration interaction [0.0]
  We propose using the wave function generated by the quantum selected configuration interaction (QSCI) method as the trial wave function in phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC)<n>We call this integrated approach QC-QSCI-AFQMC, or QSCI-AFQMC for short. This method is validated across several molecular systems.
  arXiv  Detail & Related papers  (2025-02-28T14:12:37Z)
• Non-unitary Variational Quantum Eigensolver with the Localized Active Space Method and Cost Mitigation [0.33923727961771083]
  Hardware-efficient localized active space self-consistent field (SCFS) approximates complete active space self-consistent field (CFF)<n>HEA offer affordable and shallower circuits, yet they often fail to capture the necessary correlation.<n>LAS-nuVQE is shown to recover interfragment correlations, reach chemical accuracy with a small number of gates (70) in both H4 and square cyclobutadiene.
  arXiv  Detail & Related papers  (2025-01-23T04:18:57Z)
• 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)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Quantum Computing in Pharma: A Multilayer Embedding Approach for Near Future Applications [0.0]
  The quantum computer excels at treating a moderate number of orbitals within an active space in a fully quantum mechanical manner. We present a quantum phase estimation calculation on F$enzi$ in a (2,2) active space on Rigetti's Aspen-11 QPU.
  arXiv  Detail & Related papers  (2022-02-09T13:39:22Z)

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.