Frustration-Induced Expressibility Limitations in Variational Quantum Algorithms
- URL: http://arxiv.org/abs/2604.11688v1
- Date: Mon, 13 Apr 2026 16:30:20 GMT
- Title: Frustration-Induced Expressibility Limitations in Variational Quantum Algorithms
- Abstract summary: We show that geometric frustration leads to strongly inhomogeneous correlations that are difficult to capture using standard Hamiltonian-inspired anstze.<n>By introducing bond-resolved variational parameters, we recover accurate results at reduced circuit depth.<n>Our results provide a clear physical explanation for the limitations of variational quantum algorithms in frustrated systems and suggest improved ansatz design strategies for quantum simulation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Geometric frustration, arising from competing interactions that prevent simultaneous energy minimization, presents a fundamental challenge for variational quantum algorithms applied to quantum many-body systems. We investigate the transverse-field Ising model on a square lattice with frustrated diagonal coupling and show that geometric frustration leads to strongly inhomogeneous correlations that are difficult to capture using standard Hamiltonian-inspired ansätze with global parameters. As a result, the required circuit depth increases significantly in the intermediate-field regime. We demonstrate that this limitation is not caused by optimization difficulties such as barren plateaus, but instead arises from insufficient expressibility of the ansatz. By introducing bond-resolved variational parameters, we recover accurate results at reduced circuit depth. We also study low-energy excitations and find that near-degenerate spectra in the frustrated regime further challenge variational methods. Our results provide a clear physical explanation for the limitations of variational quantum algorithms in frustrated systems and suggest improved ansatz design strategies for quantum simulation.
Related papers
- Wavelength-Uniform Quantum Algorithms for Quantum Dynamics [39.740772144144366]
We develop a quantum algorithm for it all ranges of wavelengths. Specifically, it has a it complexity in spatial dimension, and discretization and bounds it without negative powers of possibly small wavelength, thus enabling it to capture the correct physical observables even if the spatial grid does not resolve the frequency, hence defying the Nyquist-Shannon sampling theorem.
arXiv Detail & Related papers (2026-09-07T12:00:04Z) - Projected Inverse Iteration: An Eigenvalue Approach to Ground-State Computation with Neural Quantum States [17.26784290982439]
Deep learning offers a powerful approach to quantum many-body problems via neural network wavefunctions.<n>Existing optimization methods, including natural descent and gradient reconfiguration, suffer from spectral gap-dependent convergence.<n>Here, we introduce Projected Inverse Iteration (PII) by re-framing the ground-state search as an eigenvalue problem.
arXiv Detail & Related papers (2026-06-05T20:23:38Z) - Quantum remeshing and efficient encoding for fracture mechanics [0.5541644538483946]
We present a variational quantum algorithm for structural mechanical problems, specifically addressing crack opening simulations.<n>Our approach provides an alternative solution for a relevant 2D case by implementing a parametrized quantum circuit.<n>Our method has been experimentally validated on Quandela's photonic quantum processor Ascella.
arXiv Detail & Related papers (2025-10-16T14:50:59Z) - Looking elsewhere: improving variational Monte Carlo gradients by importance sampling [41.94295877935867]
Neural-network quantum states (NQS) offer a powerful and expressive ansatz for representing quantum many-body wave functions.<n>It is well known that some scenarios - such as sharply peaked wave functions emerging in quantum chemistry - lead to high-variance gradient estimators hindering the effectiveness of variational optimizations.<n>In this work we investigate a systematic strategy to tackle those sampling issues by means of adaptively tuned importance sampling.<n>Our approach can reduce the computational cost of vanilla VMC considerably, up to a factor of 100x when targeting highly peaked quantum chemistry wavefunctions.
arXiv Detail & Related papers (2025-07-07T18:00:03Z) - 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) - Trotter error mitigation by error profiling with shallow quantum circuit [5.281011822909396]
We propose a resource-efficient scheme to reduce the algorithmic Trotter error with relatively shallow circuit depth.<n>Our approach offers an efficient way of quantum simulation on near-term quantum processors with shallow circuits.
arXiv Detail & Related papers (2025-03-12T18:02:44Z) - Variational Quantum Subspace Construction via Symmetry-Preserving Cost Functions [36.94429692322632]
We propose a variational strategy based on symmetry-preserving cost functions to iteratively construct a reduced subspace for extraction of low-lying energy states.<n>As a proof of concept, we test the proposed algorithms on H4 chain and ring, targeting both the ground-state energy and the charge gap.
arXiv Detail & Related papers (2024-11-25T20:33:47Z) - Bias-field digitized counterdiabatic quantum optimization [39.58317527488534]
We call this protocol bias-field digitizeddiabatic quantum optimization (BF-DCQO)
Our purely quantum approach eliminates the dependency on classical variational quantum algorithms.
It achieves scaling improvements in ground state success probabilities, increasing by up to two orders of magnitude.
arXiv Detail & Related papers (2024-05-22T18:11:42Z) - Machine-learning-inspired quantum control in many-body dynamics [6.817811305553492]
We introduce a promising and versatile control neural network tailored to optimize control fields.
We address the problem of suppressing defect density and enhancing cat-state fidelity during the passage across the critical point in the quantum Ising model.
In comparison to gradient-based power-law quench methods, our approach demonstrates significant advantages for both small system sizes and long-term evolutions.
arXiv Detail & Related papers (2024-04-09T01:47:55Z) - Contextual Subspace Variational Quantum Eigensolver Calculation of the Dissociation Curve of Molecular Nitrogen on a Superconducting Quantum Computer [0.06990493129893112]
We present an experimental demonstration of the Contextual Subspace Variational Quantum Eigensolver on superconducting quantum hardware.
In particular, we compute the potential energy curve for molecular nitrogen, where a dominance of static correlation in the dissociation limit proves challenging for many conventional quantum chemistry techniques.
Our quantum simulations retain good agreement with the full configuration interaction energy in the chosen STO-3G basis, outperforming all benchmarked single-reference wavefunction techniques in capturing the bond-breaking appropriately.
arXiv Detail & Related papers (2023-12-07T16:05:52Z) - Equilibrium Quantum Impurity Problems via Matrix Product State Encoding
of the Retarded Action [0.0]
In this Article, we explore the computational power of representing the retarded action as matrix product state (RAMPS)
We demonstrate that the RAMPS approach reliably reaches the Kondo regime for a range of interaction strengths $U$, with a numerical error scaling as a weak power law with inverse temperature.
Our results show that the RAMPS approach offers promise as an alternative tool for studying quantum impurity problems in regimes that challenge established methods.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Simulating the Mott transition on a noisy digital quantum computer via
Cartan-based fast-forwarding circuits [62.73367618671969]
Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model.
Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models.
This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
arXiv Detail & Related papers (2021-12-10T17:32:15Z) - Quantum algorithms for quantum dynamics: A performance study on the
spin-boson model [68.8204255655161]
Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator.
variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware.
We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
arXiv Detail & Related papers (2021-08-09T18:00:05Z) - Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions.
Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
arXiv Detail & Related papers (2021-06-24T20:16:02Z)
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.