Noisy Quantum Simulation: Performance and Resource Considerations for the Tavis-Cummings and Heisenberg Models

• URL: http://arxiv.org/abs/2402.16692v2
• Date: Fri, 10 Oct 2025 11:46:51 GMT
• Title: Noisy Quantum Simulation: Performance and Resource Considerations for the Tavis-Cummings and Heisenberg Models
• Authors: Alisa Haukisalmi, Daniel Paz Ramos, Matti Raasakka, Andrea Marchesin, Lauri Ylinen, Ilkka Tittonen,
• Abstract summary: Fault-tolerant quantum computers promise the simulation of complex quantum systems beyond the reach of classical computation.<n>Two techniques addressing these challenges are zero-noise extrapolation (ZNE) and incremental structural learning (ISL)<n>ZNE and ISL are benchmarked for simulating the Trotterized time evolution of two models: the Tavis-Cummings model (TCM) and the Heisenberg spin chain (HSC)<n>Results indicate that ISL performs more favorably in HSC systems, consistently surpassing ZNE in expectation value accuracy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fault-tolerant quantum computers promise the simulation of complex quantum systems beyond the reach of classical computation. In contrast, current noisy intermediate-scale quantum (NISQ) devices are constrained by hardware noise. Consequently, quantum simulation methods remain limited in their near-term applicability. Two prominent techniques addressing these challenges are zero-noise extrapolation (ZNE) and incremental structural learning (ISL). In this work, ZNE and ISL are benchmarked for simulating the Trotterized time evolution of two models: the Tavis-Cummings model (TCM) and the Heisenberg spin chain (HSC), using a classically simulated noisy hardware backend. The methods are evaluated on the basis of the accuracy of expectation values relative to noiseless simulations and their resource demands such as circuit depths and shot counts. The impact of noise on optimization routines in ISL, previously underexplored, is also investigated. Results indicate that ISL performs more favorably in HSC systems, consistently surpassing ZNE in expectation value accuracy. Conversely, for the TCM, ISL generally yields lower accuracies despite reduced Trotter circuit depths, with weak interactions often leading to pronounced phase lags or flat expectation curves. Notably, when performing ISL optimization under noiseless conditions, the protocol is generally able to reduce dephasing errors, but average accuracies still vary on the simulated Hamiltonian. Our findings highlight the sensitivity of quantum simulation protocols to the structure of the Hamiltonian encoding system dynamics. Trends across systems suggest that ISL optimization benefits from Trotter circuits with stronger interactions, and that ansatz construction favors isotropic couplings. Moreover, although ISL introduces approximation errors, it demonstrates greater robustness than ZNE in systems with deeper Trotter circuits.

Related papers

• Enhancing classical simulation with noisy quantum devices [11.834807045442057]
  We show that noisy quantum devices can be leveraged as computational resources to enhance the classical simulation of quantum circuits.<n>We introduce the Noisy-device-enhanced Classical Simulation (NDE-CS) protocol, which improves stabilizer-based Monte Carlo simulation methods.<n>NDE-CS achieves orders-of-magnitude reductions in sampling cost compared to the underlying purely classical Monte Carlo approaches.
  arXiv  Detail & Related papers  (2026-01-13T18:01:46Z)
• 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)
• Exploiting biased noise in variational quantum models [0.0]
  Variational quantum algorithms (VQAs) are promising tools for demonstrating quantum utility on near-term quantum hardware.<n>We study the effect of quantum noise on the classical optimisation process.<n>We find that twirling, which is commonly used in standard error-mitigation strategies to symmetrise noise, actually degrades performance in the variational setting.
  arXiv  Detail & Related papers  (2025-10-28T04:14:20Z)
• Error mitigation of shot-to-shot fluctuations in analog quantum simulators [46.54051337735883]
  We introduce an error mitigation technique that addresses shot-to-shot fluctuations in the parameters for the Hamiltonian governing the system dynamics.<n>We rigorously prove that amplifying this shot-to-shot noise and extrapolating to the zero-noise limit recovers noiseless results for realistic noise distributions.<n> Numerically, we predict a significant enhancement in the effective many-body coherence time for Rydberg atom arrays under realistic conditions.
  arXiv  Detail & Related papers  (2025-06-19T18:00:00Z)
• Dynamic Estimation Loss Control in Variational Quantum Sensing via Online Conformal Inference [39.72602887300498]
  Current variational quantum sensing methods lack rigorous performance guarantees.<n>This paper proposes an online control framework for VQS that dynamically updates the variational parameters while providing deterministic error bars on the estimates.<n> Experiments on a quantum magnetometry task confirm that the proposed dynamic VQS approach maintains the required reliability over time, while still yielding precise estimates.
  arXiv  Detail & Related papers  (2025-05-29T12:19:07Z)
• Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
  Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
  arXiv  Detail & Related papers  (2025-05-24T02:51:34Z)
• Prospects of Quantum Error Mitigation for Quantum Signal Processing [0.0]
  This work explores the performance of zero-noise-extrapolation (ZNE) on a Hamiltonian simulation algorithm designed within quantum signal processing (QSP)<n>We quantify for which noise and depth regimes our ZNE protocol can recover an approximation of the noiseless expectation value.<n>We briefly discuss and present a numerical study on the region where ZNE is unusable, even given an unlimited sample budget.
  arXiv  Detail & Related papers  (2025-05-08T19:49:54Z)
• Approximation Methods for Simulation and Equivalence Checking of Noisy Quantum Circuits [3.2559508547981917]
  In the current NISQ era, simulating and verifying noisy quantum circuits is crucial. This paper introduces an approximation algorithm for simulating and assessing the equivalence of noisy quantum circuits.
  arXiv  Detail & Related papers  (2025-03-13T13:19:30Z)
• Bayesian Quantum Amplitude Estimation [49.1574468325115]
  We introduce BAE, a noise-aware Bayesian algorithm for quantum amplitude estimation. We show that BAE achieves Heisenberg-limited estimation and benchmark it against other approaches.
  arXiv  Detail & Related papers  (2024-12-05T18:09:41Z)
• Subspace-Based Local Compilation of Variational Quantum Circuits for Large-Scale Quantum Many-Body Simulation [0.0]
  This paper proposes a hybrid quantum-classical algorithm for compiling the time-evolution operator. It achieves a 95% reduction in circuit depth compared to Trotterization while maintaining accuracy. We estimate the gate count needed to execute the quantum simulations using the LSVQC on near-term quantum computing architectures.
  arXiv  Detail & Related papers  (2024-07-19T09:50:01Z)
• Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
  We develop a reinforcement learning-based quantum compiler for a superconducting processor. We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths. Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
  arXiv  Detail & Related papers  (2024-06-18T01:49:48Z)
• Characterizing randomness in parameterized quantum circuits through expressibility and average entanglement [39.58317527488534]
  Quantum Circuits (PQCs) are still not fully understood outside the scope of their principal application. We analyse the generation of random states in PQCs under restrictions on the qubits connectivities. We place a connection between how steep is the increase on the uniformity of the distribution of the generated states and the generation of entanglement.
  arXiv  Detail & Related papers  (2024-05-03T17:32:55Z)
• 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)
• Towards Efficient Quantum Computing for Quantum Chemistry: Reducing Circuit Complexity with Transcorrelated and Adaptive Ansatz Techniques [0.0]
  This work demonstrates how to reduce circuit depth by combining the transcorrelated (TC) approach with adaptive quantum ans"atze. Our study demonstrates that combining the TC method with adaptive ans"atze yields compact, noise-resilient, and easy-to-optimize quantum circuits.
  arXiv  Detail & Related papers  (2024-02-26T15:31:56Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Classical simulations of noisy variational quantum circuits [0.0]
  Noisely affects quantum computations so that they not only become less accurate but also easier to simulate classically as systems scale up. We construct a classical simulation algorithm, LOWESA, for estimating expectation values of noisy parameterised quantum circuits.
  arXiv  Detail & Related papers  (2023-06-08T17:52:30Z)
• Mitigating crosstalk errors by randomized compiling: Simulation of the BCS model on a superconducting quantum computer [41.94295877935867]
  Crosstalk errors, stemming from CNOT two-qubit gates, are a crucial source of errors on numerous quantum computing platforms. We develop and apply an extension of the randomized compiling protocol that includes a special treatment of neighboring qubits. Our twirling of neighboring qubits is shown to dramatically improve the noise estimation protocol without the need to add new qubits or circuits.
  arXiv  Detail & Related papers  (2023-05-03T18:00:02Z)
• Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding [44.89303833148191]
  We develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs) We propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs. In an image classification task, extensive simulations corroborate the effectiveness of eSQFL.
  arXiv  Detail & Related papers  (2022-12-04T03:18:03Z)
• 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)
• Robust resource-efficient quantum variational ansatz through evolutionary algorithm [0.46180371154032895]
  Vari quantum algorithms (VQAsational) are promising methods to demonstrate quantum advantage on near-term devices. We show that a fixed VQA circuit design, such as the widely-used hardware efficient ansatz, is not necessarily robust against imperfections. We propose a genome-length-adjustable evolutionary algorithm to design a robust VQA circuit that is optimized over variations of both circuit ansatz and gate parameters.
  arXiv  Detail & Related papers  (2022-02-28T12:14:11Z)
• Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition [67.95996816744251]
  State-of-the-art language models (LMs) represented by long-short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming increasingly complex and expensive for practical applications. Current quantization methods are based on uniform precision and fail to account for the varying performance sensitivity at different parts of LMs to quantization errors. Novel mixed precision neural network LM quantization methods are proposed in this paper.
  arXiv  Detail & Related papers  (2021-11-29T12:24:02Z)

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.