Error-corrected phase estimation averaged over variable grids on a trapped-ion quantum computer: hyperacuity spectra of a CO molecule adsorbed onto $χ$-Fe$_5$C$_2$

• URL: http://arxiv.org/abs/2605.29674v1
• Date: Thu, 28 May 2026 09:33:24 GMT
• Title: Error-corrected phase estimation averaged over variable grids on a trapped-ion quantum computer: hyperacuity spectra of a CO molecule adsorbed onto $χ$-Fe$_5$C$_2$
• Abstract summary: We propose a vernier-type approach that combines low-resolution QPE with multiple origin shifts and physically motivated continuous parametrization to reconstruct the spectra accurately.<n>QAVG provides a robust route to quantum simulations of correlated spectra toward the era of early-fault-tolerant quantum computers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum phase estimation (QPE) is an underlying technology for extracting the excitation spectra of many-electron systems, yet its practical use on current hardware is hindered by low grid resolution and environmental noises. Here we propose QPE averaged over variable grids (QAVG), a vernier-type approach that combines low-resolution QPE with multiple origin shifts and physically motivated continuous parametrization to reconstruct the spectra accurately. We introduce this approach into an end-to-end workflow for the {\it ab initio}-based model system for a CO molecule adsorbed onto the $χ$-Fe$_5$C$_2$ surface. We perform experiments on Quantinuum H2-2 using both physical QPE circuits and logical QPE circuits encoded in the Steane code with offline bit-flip correction. We demonstrate that QAVG accurately reconstructs the spectra with deviations much smaller than the nominal QPE resolution, even when the noisy histograms are used. The cost landscapes averaged over the shifted grids substantially suppress the local minima arising from the spectral leakage, thereby stabilizing the optimization of trial parameters. These results indicate that QAVG provides a robust route to quantum simulations of correlated spectra toward the era of early-fault-tolerant quantum computers.

Related papers

• Scalable Lindblad Noise Learning via Stochastic Tensor-Network Simulation [48.887627688666974]
  Learning dissipation rates in large-scale open quantum systems is a major obstacle for near-term quantum technologies.<n>Here, we propose a scalable noise-learning framework for Lindblad dissipation rates that combines a simulation method, the Jump Method (TJM)<n>We demonstrate the approach on two noise models in the Ising model, in which independent dissipation rates are learned for each site up to $N_mathrmsite=16$.<n>This combination of scalable numerics and rigorous theoretical guarantees positions TJM-based noise learning as a practical foundation for characterizing dissipation in large quantum devices.
  arXiv  Detail & Related papers  (2026-08-25T15:07:11Z)
• An Analytically Trained Variational Surrogate for Quantum Phase Estimation on NISQ Hardware [0.3908906925636173]
  Quantum Phase Estimation (QPE) is a foundational algorithm for molecular ground-state energy estimation, but its deep circuit requirements make direct hardware execution impractical on Noisy Intermediate-Scale Quantum (NISQ) devices.<n>We present an analytically grounded variational surrogate framework in which a shallow Variational Quantum Circuit (VQC) is trained to reproduce the QPE measurement distribution without any quantum circuit simulation.
  arXiv  Detail & Related papers  (2026-07-23T05:52:17Z)
• Split-Evolution Quantum Phase Estimation for Particle-Conserving Hamiltonians [0.0]
  We present a hardware demonstration and resource analysis of split-evolution quantum phase estimation (SE-QPE) on a Quantinuum System Model H2 quantum computer.<n>SE-QPE is a modification to canonical QPE for particle-conserving Hamiltonians in which controlled time evolution is replaced by CSWAP-based interference between a target register.
  arXiv  Detail & Related papers  (2026-04-16T12:05:34Z)
• QARIMA: A Quantum Approach To Classical Time Series Analysis [0.5039813366558307]
  We present a quantum-inspired ARIMA methodology that integrates quantum-assisted lag discovery with fixed-configurational quantum circuits (VQCs) for parameter estimation and weaklag refinement.<n>We perform rolling-origin evaluations against automated classical ARIMA, reporting out-of-sample mean squared error (MSE), mean absolute percentage error (MAPE), and Diebold-Mariano tests on MSE and MAE.
  arXiv  Detail & Related papers  (2026-04-09T14:11:39Z)
• Hybrid Fourier Neural Operator for Surrogate Modeling of Laser Processing with a Quantum-Circuit Mixer [0.0]
  HQ-LP-FNO is a hybrid quantum-classical FNO that replaces a fraction of dense spectral blocks with a compact, mode-shared variational quantum circuit mixer.<n> evaluated on three-dimensional surrogate modeling of high-energy laser processing, coupling heat transfer, melt-pool convection, free-surface deformation, and phase change.
  arXiv  Detail & Related papers  (2026-04-06T16:28:49Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Filtered Quantum Phase Estimation [2.6967769052209873]
  We develop a unified framework for filtered-state preparation that enhances the overlap of a given input state through spectral filtering.<n>We further develop a filtered variant of QPE that mitigates the unfavorable dependence on the initial overlap present in standard QPE.<n>Experiments show that FQPE reduces the total runtime by more than two orders of magnitude in the high-precision regime, with overlap amplification exceeding a factor of one hundred.
  arXiv  Detail & Related papers  (2025-10-05T17:11:30Z)
• LRQ-DiT: Log-Rotation Post-Training Quantization of Diffusion Transformers for Image and Video Generation [41.66473889057111]
  Diffusion Transformers (DiTs) have achieved impressive performance in text-to-image and text-to-video generation.<n>DiTs' high computational cost and large parameter sizes pose significant challenges for usage in resource-constrained scenarios.<n>We propose LRQ-DiT, an efficient and accurate post-training quantization framework for image and video generation.
  arXiv  Detail & Related papers  (2025-08-05T14:16:11Z)
• FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix Approximation [55.12070409045766]
  Post-training quantization (PTQ) has stood out as a cost-effective and promising model compression paradigm in recent years.<n>Current PTQ methods for Vision Transformers (ViTs) still suffer from significant accuracy degradation, especially under low-bit quantization.
  arXiv  Detail & Related papers  (2025-06-13T07:57:38Z)
• Towards Non-Abelian Quantum Signal Processing: Efficient Control of Hybrid Continuous- and Discrete-Variable Architectures [0.0]
  Quantum Signal Processing (QSP) transforms a unitary parameterized by $theta$ into one governed by a function $f(theta)$.<n>We extend QSP to a new class, non-abelian QSP, which utilizes non-commuting control parameters.
  arXiv  Detail & Related papers  (2025-04-28T17:10:27Z)
• Compressed-sensing Lindbladian quantum tomography with trapped ions [44.99833362998488]
  Characterizing the dynamics of quantum systems is a central task for the development of quantum information processors. We propose two different improvements of Lindbladian quantum tomography (LQT) that alleviate previous shortcomings.
  arXiv  Detail & Related papers  (2024-03-12T09:58:37Z)
• Noise-aware variational eigensolvers: a dissipative route for lattice gauge theories [40.772310187078475]
  We propose a novel variational ansatz for the ground-state preparation of the $mathbbZ$ lattice gauge theory (LGT) in quantum simulators. It combines dissipative and unitary operations in a completely deterministic scheme with a circuit depth that does not scale with the size of the considered lattice. We find that, with very few variational parameters, the ansatz can achieve $>!99%$ precision in energy in both the confined and deconfined phase of the $mathbbZ$ LGT.
  arXiv  Detail & Related papers  (2023-08-07T14:23:00Z)
• Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis [42.275148861039895]
  Variational quantum algorithms (VQAs) offer the most promising path to obtaining quantum advantages via noisy processors. gate noise due to imperfections and decoherence affects the gradient estimates by introducing a bias. Quantum error mitigation (QEM) techniques can reduce the estimation bias without requiring any increase in the number of qubits. QEM can reduce the number of required iterations, but only as long as the quantum noise level is sufficiently small.
  arXiv  Detail & Related papers  (2022-09-23T10:48:04Z)

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.