Sparse Non-Markovian Noise Modeling of Transmon-Based Multi-Qubit Operations

• URL: http://arxiv.org/abs/2412.16092v1
• Date: Fri, 20 Dec 2024 17:37:26 GMT
• Title: Sparse Non-Markovian Noise Modeling of Transmon-Based Multi-Qubit Operations
• Authors: Yasuo Oda, Kevin Schultz, Leigh Norris, Omar Shehab, Gregory Quiroz,
• Abstract summary: The influence of noise on quantum dynamics is one of the main factors preventing current quantum processors from performing accurate quantum computations. We present an approach for effective noise modeling of multi-qubit operations on transmon-based devices. We show that the model can capture and predict a wide range of single- and two-qubit behaviors, including non-temporally correlated noise sources.
• Score: 0.0
• License:
• Abstract: The influence of noise on quantum dynamics is one of the main factors preventing current quantum processors from performing accurate quantum computations. Sufficient noise characterization and modeling can provide key insights into the effect of noise on quantum algorithms and inform the design of targeted error protection protocols. However, constructing effective noise models that are sparse in model parameters, yet predictive can be challenging. In this work, we present an approach for effective noise modeling of multi-qubit operations on transmon-based devices. Through a comprehensive characterization of seven devices offered by the IBM Quantum Platform, we show that the model can capture and predict a wide range of single- and two-qubit behaviors, including non-Markovian effects resulting from spatio-temporally correlated noise sources. The model's predictive power is further highlighted through multi-qubit dynamical decoupling demonstrations and an implementation of the variational quantum eigensolver. As a training proxy for the hardware, we show that the model can predict expectation values within a relative error of 0.5%; this is a 7$\times$ improvement over default hardware noise models. Through these demonstrations, we highlight key error sources in superconducting qubits and illustrate the utility of reduced noise models for predicting hardware dynamics.

Related papers

• 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)
• Error mitigation with stabilized noise in superconducting quantum processors [2.2752198833969315]
  We experimentally demonstrate that tuning of the qubit-TLS interactions helps reduce noise instabilities and enables more reliable error-mitigation performance. We anticipate that the capabilities introduced here will be crucial for the exploration of quantum applications on solid-state processors at non-trivial scales.
  arXiv  Detail & Related papers  (2024-07-02T17:47:07Z)
• Flexible Error Mitigation of Quantum Processes with Data Augmentation Empowered Neural Model [9.857921247636451]
  We propose a data augmentation empowered neural model for error mitigation (DAEM) Our model does not require any prior knowledge about the specific noise type and measurement settings. It can estimate noise-free statistics solely from the noisy measurement results of the target quantum process.
  arXiv  Detail & Related papers  (2023-11-03T05:52:14Z)
• RobustMQ: Benchmarking Robustness of Quantized Models [54.15661421492865]
  Quantization is an essential technique for deploying deep neural networks (DNNs) on devices with limited resources. We thoroughly evaluated the robustness of quantized models against various noises (adrial attacks, natural corruptions, and systematic noises) on ImageNet. Our research contributes to advancing the robust quantization of models and their deployment in real-world scenarios.
  arXiv  Detail & Related papers  (2023-08-04T14:37:12Z)
• Volumetric Benchmarking of Quantum Computing Noise Models [3.0098885383612104]
  We present a systematic approach to benchmark noise models for quantum computing applications. It compares the results of hardware experiments to predictions of noise models for a representative set of quantum circuits. We also construct a noise model and optimize its parameters with a series of training circuits.
  arXiv  Detail & Related papers  (2023-06-14T10:49:01Z)
• Impact of quantum noise on the training of quantum Generative Adversarial Networks [0.0]
  We conduct a first study of the performance of quantum Generative Adversarial Networks (qGANs) in the presence of different types of quantum noise. In particular, we explore the effects of readout and two-qubit gate errors on the qGAN training process.
  arXiv  Detail & Related papers  (2022-03-02T10:35:34Z)
• Learning Noise via Dynamical Decoupling of Entangled Qubits [49.38020717064383]
  Noise in entangled quantum systems is difficult to characterize due to many-body effects involving multiple degrees of freedom. We develop and apply multi-qubit dynamical decoupling sequences that characterize noise that occurs during two-qubit gates.
  arXiv  Detail & Related papers  (2022-01-26T20:22:38Z)
• 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)
• Pulse-level noisy quantum circuits with QuTiP [53.356579534933765]
  We introduce new tools in qutip-qip, QuTiP's quantum information processing package. These tools simulate quantum circuits at the pulse level, leveraging QuTiP's quantum dynamics solvers and control optimization features. We show how quantum circuits can be compiled on simulated processors, with control pulses acting on a target Hamiltonian.
  arXiv  Detail & Related papers  (2021-05-20T17:06:52Z)
• Modelling and Simulating the Noisy Behaviour of Near-term Quantum Computers [0.0]
  Noise dominates every aspect of near-term quantum computers. We study the modelling of noise in Noisy Intermediate-Scale Quantum (NISQ) computers.
  arXiv  Detail & Related papers  (2021-01-06T16:04:13Z)
• A deep learning model for noise prediction on near-term quantum devices [137.6408511310322]
  We train a convolutional neural network on experimental data from a quantum device to learn a hardware-specific noise model. A compiler then uses the trained network as a noise predictor and inserts sequences of gates in circuits so as to minimize expected noise.
  arXiv  Detail & Related papers  (2020-05-21T17:47:29Z)

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.