Improving the runtime of quantum phase estimation for chemistry through basis set optimization

• URL: http://arxiv.org/abs/2509.05733v1
• Date: Sat, 06 Sep 2025 14:47:31 GMT
• Title: Improving the runtime of quantum phase estimation for chemistry through basis set optimization
• Authors: Pauline J. Ollitrault, Jerome F. Gonthier, Dario Rocca, Gian-Luca Anselmetti, Matthias Degroote, Nikolaj Moll, Raffaele Santagati, Michael Streif,
• Abstract summary: Quantum phase estimation (QPE) is a promising quantum algorithm for obtaining molecular ground-state energies with chemical accuracy.<n> computational cost, dominated by the Hamiltonian 1-norm $lambda$ and the cost of the block encoding, scales at least quadratically with the number of molecular orbitals.<n>We investigate two strategies to mitigate this cost through the optimization of the basis set.
• Score: 0.11242503819703259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum phase estimation (QPE) is a promising quantum algorithm for obtaining molecular ground-state energies with chemical accuracy. However, its computational cost, dominated by the Hamiltonian 1-norm $\lambda$ and the cost of the block encoding, scales at least quadratically with the number of molecular orbitals, making it challenging to incorporate dynamic correlation by enlarging the active space. In this work, we investigate two strategies to mitigate this cost through the optimization of the basis set. First, we investigate whether adjusting the coefficients of Gaussian basis functions can minimize the 1-norm while preserving the accuracy of the ground state energy. Although this method leads to a reduction in the 1-norm up to 10%, this reduction is system-dependent and diminishes with increasing molecular size. Second, we demonstrate that employing a large-basis-set frozen natural orbital (FNO) strategy results in a substantial reduction in QPE resources without compromising accuracy. We study a dataset of 58 small organic molecules and the dissociation curve of N2, and demonstrate that an active space constructed from orbitals derived from larger basis sets captures correlation effects more effectively. This approach yields up to an 80% reduction in the 1-norm $\lambda$ and also leads to a 55% reduction in the number of orbitals. Our results highlight that improving the quality, not just the size, of the orbital basis is a viable strategy for extending QPE to include dynamical correlation, making progress toward scalable and chemically accurate quantum simulations with tractable resource requirements.

Related papers

• Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions [34.53033480184221]
  Density functional theory (DFT) is the most widely used method for calculating molecular properties.<n> Coupled-cluster (CC) theory is the most successful method for achieving accuracy beyond DFT.<n>We present the Molecular Orbital Learning (MLe) architecture, an equivariant machine learning model that directly predicts CC's core mathematical objects.
  arXiv  Detail & Related papers  (2026-02-23T18:54:46Z)
• Synergistic Computational Approaches for Accelerated Drug Discovery: Integrating Quantum Mechanics, Statistical Thermodynamics, and Quantum Computing [0.0]
  Accurately predicting protein-ligand binding free energies (Bs) remains a central challenge in drug discovery.<n>Here, we introduce a hybrid quantum-classical framework that combines Mining Minima sampling with quantum mechanically refined ligand partial charges.<n>Across 23 protein targets and 543, the method achieves a mean absolute error of about 1.10 kcal/mol with strong rank-order fidelity.
  arXiv  Detail & Related papers  (2025-12-05T20:47:34Z)
• Estimating ground-state properties in quantum simulators with global control [35.3616472951301]
  Accurately determining ground-state properties of quantum many-body systems remains one of the major challenges of quantum simulation.<n>We present a protocol for estimating the ground-state energy using only global time evolution under a target Hamiltonian.
  arXiv  Detail & Related papers  (2025-11-06T15:08:00Z)
• When Less is More: Approximating the Quantum Geometric Tensor with Block Structures [43.39839287869652]
  We introduce a block-diagonal quantum geometric tensor that partitions the metric by network layers, analogous to block-structured Fisher methods such as K-FAC.<n> Experiments on Heisenberg and frustrated $J_1$-$J$ models show faster convergence, lower energy, and improved stability.
  arXiv  Detail & Related papers  (2025-10-09T16:44:30Z)
• Large-scale Efficient Molecule Geometry Optimization with Hybrid Quantum-Classical Computing [1.1463843149836523]
  Accurately and efficiently predicting the equilibrium geometries of large molecules remains a central challenge in quantum computational chemistry.<n>Two major obstacles hinder progress: the large number of qubits required and the prohibitive cost of conventional nested optimization.<n>We introduce a co-optimization framework that combines Density Matrix Embedding Theory (DMET) with Variational Quantum Eigensolver (VQE) to address these limitations.
  arXiv  Detail & Related papers  (2025-09-09T07:31:29Z)
• Entanglement-minimized orbitals enable faster quantum simulation of molecules [1.7042264000899534]
  We introduce an efficient classical algorithm to find entanglement-minimized orbitals (EMOs) using spin-adapted matrix product states (MPS)<n>Our algorithm improves initial state overlap by nearly an order of magnitude over prior orbital optimization approaches for an iron-sulfur cluster with four irons.<n>Our results show that initial state preparation for these challenging systems requires far fewer resources than prior estimates suggested.
  arXiv  Detail & Related papers  (2025-06-16T11:49:20Z)
• Variational Quantum Imaginary Time Evolution for Matrix Product State Ansatz with Tests on Transcorrelated Hamiltonians [11.985673663540688]
  The matrix product state (MPS) ansatz offers a promising approach for finding the ground state of molecular Hamiltonians. We enhance the optimization performance of the QCMPS ansatz by employing the variational quantum imaginary time evolution (VarQITE) approach.
  arXiv  Detail & Related papers  (2024-07-15T08:28:52Z)
• Shortcut to Chemically Accurate Quantum Computing via Density-based Basis-set Correction [0.4909687476363595]
  We embed a quantum computing ansatz into density-functional theory via density-based basis-set corrections (DBBSC) We provide a shortcut towards chemically accurate quantum computations by approaching the complete-basis-set limit. The resulting approach self-consistently accelerates the basis-set convergence, improving electronic densities, ground-state energies, and first-order properties.
  arXiv  Detail & Related papers  (2024-05-19T14:31:01Z)
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive [52.77024349608834]
  We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE) This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
  arXiv  Detail & Related papers  (2022-12-21T23:15:17Z)
• Iterative Qubit Coupled Cluster using only Clifford circuits [36.136619420474766]
  An ideal state preparation protocol can be characterized by being easily generated classically. We propose a method that meets these requirements by introducing a variant of the iterative qubit coupled cluster (iQCC) We demonstrate the algorithm's correctness in ground-state simulations and extend our study to complex systems like the titanium-based compound Ti(C5H5)(CH3)3 with a (20, 20) active space.
  arXiv  Detail & Related papers  (2022-11-18T20:31:10Z)
• Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
  We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves. We find both methods provide good estimates of the energy and ground state. gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
  arXiv  Detail & Related papers  (2020-11-02T19:52:04Z)
• The impacts of optimization algorithm and basis size on the accuracy and efficiency of variational quantum eigensolver [8.94838505400535]
  Variational quantum eigensolver (VQE) is demonstrated to be the promising methodology for quantum chemistry based on near-term quantum devices. In this work, five molecules (H2, LiH, HF, N2 and F2) are studied based on the VQE method using unitary coupled cluster (UCC) ansatz. The performance of the gradient optimization L-BFGS-B is compared with that of the direct search method COBYLA. For practical applications of VQE, complete active space (CAS) is suggested based on limited quantum resources.
  arXiv  Detail & Related papers  (2020-06-29T07:50:24Z)
• Resource Efficient Chemistry on Quantum Computers with the Variational Quantum Eigensolver and The Double Unitary Coupled-Cluster approach [0.0]
  We show that the number of qubits scales linearly with the size of molecular basis. We employ the double unitary coupled-cluster (DUCC) method to effectively downfold correlation effects into the reduced-size orbital space. Using downfolding techniques, we demonstrate that properly constructed effective Hamiltonians can capture the effect of the whole orbital space in small-size active spaces.
  arXiv  Detail & Related papers  (2020-04-16T15:59:15Z)
• Hartree-Fock on a superconducting qubit quantum computer [30.152226344347064]
  Here, we perform a series of quantum simulations of chemistry the largest of which involved a dozen qubits, 78 two-qubit gates, and 114 one-qubit gates. We model the binding energy of $rm H_6$, $rm H_8$, $rm H_10$ and $rm H_12$ chains as well as the isomerization of diazene. We also demonstrate error-mitigation strategies based on $N$-representability which dramatically improve the effective fidelity of our experiments.
  arXiv  Detail & Related papers  (2020-04-08T18:00:06Z)

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.