Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor

• URL: http://arxiv.org/abs/2605.21276v1
• Date: Wed, 20 May 2026 15:08:08 GMT
• Title: Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor
• Abstract summary: We show that the kernel estimated from the logical implementation performs better than its physical counterpart on relevant metrics.<n>We apply the computed quantum kernel to the task of solving differential equations, confirming how the superior performance of a logical quantum kernel is retained at an end-to-end applicative level.
• Score: 1.053514925037787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We report on a performance comparison between physical and logical computations on a prototypical machine-learning application: solving differential equations using quantum kernel methods. The algorithm is implemented on an atom-based logical quantum processor, both at the physical and logical levels. We show that the kernel estimated from the logical implementation performs better than its physical counterpart on relevant metrics. We observe how such performance improvement can be traced back to specific noise-induced errors detected by the chosen encoding. We apply the computed quantum kernel to the task of solving differential equations, confirming how the superior performance of a logical quantum kernel is retained also at an end-to-end applicative level. Our findings show that experimental validation of end-to-end protocols can already highlight the positive impact of fault-tolerant implementations despite their higher quantum resource count, and guide application-informed architectural choices.

Related papers

• Logical accreditation: a framework for efficient certification of fault-tolerant computations [1.7181078670359513]
  We introduce logical accreditation, a framework for efficiently certifying quantum computations performed on logical qubits.<n>Our protocol is robust against general noise models, far beyond those typically considered in performance analyses of quantum error-correcting codes.
  arXiv  Detail & Related papers  (2025-08-07T15:53:05Z)
• Characterizing physical and logical errors in a transversal CNOT via cycle error reconstruction [0.24075310817964646]
  advancing towards fault-tolerant quantum computing will require an understanding of the underlying error mechanisms in logical primitives.<n>We demonstrate the novel capability to characterize the physical error properties relevant to fault-tolerant operations via cycle error reconstruction.
  arXiv  Detail & Related papers  (2025-04-16T11:22:32Z)
• An Efficient Quantum Classifier Based on Hamiltonian Representations [50.467930253994155]
  Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks.<n>We propose an efficient approach that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings.<n>We evaluate our approach on text and image classification tasks, against well-established classical and quantum models.
  arXiv  Detail & Related papers  (2025-04-13T11:49:53Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Automatic and effective discovery of quantum kernels [41.61572387137452]
  Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data.<n>We present an approach to this problem, which employs optimization techniques, similar to those used in neural architecture search and AutoML.<n>The results obtained by testing our approach on a high-energy physics problem demonstrate that, in the best-case scenario, we can either match or improve testing accuracy with respect to the manual design approach.
  arXiv  Detail & Related papers  (2022-09-22T16:42:14Z)
• Noisy Quantum Kernel Machines [58.09028887465797]
  An emerging class of quantum learning machines is that based on the paradigm of quantum kernels. We study how dissipation and decoherence affect their performance. We show that decoherence and dissipation can be seen as an implicit regularization for the quantum kernel machines.
  arXiv  Detail & Related papers  (2022-04-26T09:52:02Z)
• Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
  We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
  arXiv  Detail & Related papers  (2022-04-12T19:39:31Z)
• Quantum circuit architecture search on a superconducting processor [56.04169357427682]
  Variational quantum algorithms (VQAs) have shown strong evidences to gain provable computational advantages for diverse fields such as finance, machine learning, and chemistry. However, the ansatz exploited in modern VQAs is incapable of balancing the tradeoff between expressivity and trainability. We demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique to enhance VQAs on an 8-qubit superconducting quantum processor.
  arXiv  Detail & Related papers  (2022-01-04T01:53:42Z)
• Performance Evaluations of Signed and Unsigned Noisy Approximate Quantum Fourier Arithmetic [3.256297688080386]
  We implement QFT-based integer addition and multiplications on quantum computers.<n>These operations are fundamental to various quantum applications.<n>We evaluate these implementations based on IBM's superconducting qubit architecture.
  arXiv  Detail & Related papers  (2021-12-17T06:51:18Z)
• Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference [56.24109486973292]
  We study the interplay between pruning and quantization during the training of neural networks for ultra low latency applications. We find that quantization-aware pruning yields more computationally efficient models than either pruning or quantization alone for our task.
  arXiv  Detail & Related papers  (2021-02-22T19:00:05Z)
• Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack [0.0]
  Quantum computing systems need to be benchmarked in terms of practical tasks they would be expected to do. We propose 3 "application-motivated" circuit classes for benchmarking: deep, shallow, and square. We quantify the performance of quantum computing system in running circuits from these classes using several figures of merit.
  arXiv  Detail & Related papers  (2020-06-01T21:21:33Z)

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.