Searcharxiv⌕ Search

arXiv · 2610.08667

Fault-tolerant resource estimation for ground-state preparation via Lindblad simulation

Abstract

Recent advances in algorithms for simulating Lindblad dynamics have clarified their theoretical potential for state preparation, but their practicality in the early fault-tolerant regime and beyond remains uncertain. In this work, we address this by investigating the cost of preparing ground states of the fermionic Hubbard model, following the single-ancilla approach of [Phys. Rev. Research 6, 033147 (2024)]. We derive rigorous error bounds including constant prefactors, and compare to empirical error behavior and practical convergence parameters obtained from circuit-level simulations. We find that empirically chosen parameters can reduce the required resources by orders of magnitude, resulting in an estimated $7.7 \times 10^8$ T gates to perform one unit of time evolution targeting the low-energy subspace of a 36-site fermionic Hubbard model. We identify the accurate filtering of energy transitions as the main source of this cost.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Marius Bothe, Patrick Schoepf, Nick S. Blunt. 2026-10-06. Fault-tolerant resource estimation for ground-state preparation via Lindblad simulation. https://arxiv.org/abs/2610.08667

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

The Concept of Entropic Time: A Preliminary Discussion

The deep connection between entropy and information is discussed in terms of both classical and quantum physics. The mechanism of information transfer between systems via entanglement is explored in the context of decoherence theory. The concept of entropic time is then introduced on the basis of information acquisition, which is argued to be effectively irreversible and consistent with both the Second Law of Thermodynamics and our psychological perception of time. This is distinguished from the notion of parametric time, which serves as the temporal parameter for the unitary evolution of a physical state in non-relativistic quantum mechanics. The interpretation of these ideas in terms of both subjective and objective collapse models is also discussed. It is shown that energy is conserved under subjective collapse schemes whereas, in general, under objective collapse it is not. This is consistent with the fact that the latter is inherently non-unitary and that energy conservation arises out of time symmetry in the first place.

quant-ph↗

Tunable spectral correlations of highly multimode visible light via broadband quantum frequency conversion

Multimode squeezed states of light are a resource for achieving quantum advantage in computing and sensing, where spatial or temporal modes have been the experimental norm. In our experiments, we generated highly frequency-multimode infrared quantum light, and show how adiabatic frequency conversion can be used to convert the quantum state to visible wavelengths, while concurrently manipulating the joint spectrum by realizing a configurable many-port frequency-domain-beamsplitter unitary transformation. We report near-unity-efficiency quantum frequency conversion over a bandwidth >45 THz, which allowed us to measure the state with an electron-multiplying CCD (EMCCD) camera-based spectrometer, at non-cryogenic temperatures. The parametric amplification and conversion of >400 frequency modes yielded an overall mean of approximately 700 visible photons per shot, and photon statistics consistent with squeezing. Our work shows how many-mode quantum states of light can be generated, manipulated, and measured with efficient use of hardware resources, motivating the use of frequency encoding in quantum optics.

quant-ph↗

Quantum convolutional neural networks for jet images classification

Recently, interest in quantum computing has significantly increased, driven by its potential advantages over classical techniques. Quantum machine learning (QML) exemplifies one of the important quantum computing applications that are expected to surpass classical machine learning in a wide range of instances. This paper addresses the performance of QML in the context of high-energy physics (HEP). As an example, we focus on the top-quark tagging, for which classical convolutional neural networks (CNNs) have been effective but fall short in accuracy when dealing with highly energetic jet images. In this paper, we use a quantum convolutional neural network (QCNN) for this task and compare its performance with CNN using a classical noiseless simulator. We compare various setups for the QCNN, varying the convolutional circuit, type of encoding, loss function, and batch sizes. For every quantum setup, we design a similar setup to the corresponding classical model for a fair comparison. Our results indicate that, using a classical simulator, QCNN with proper setups tend to perform better than their CNN counterparts, especially when the convolution block has a lower number of parameters. For the higher parameter regime, the QCNN circuit was adjusted according to the dimensional expressivity analysis (DEA) to lower the parameter count while preserving its optimal structure. The DEA circuit demonstrated improved results over the comparable classical CNN model.

quant-ph↗