Modeling Noisy Quantum Circuits Using Experimental Characterization
- URL: http://arxiv.org/abs/2001.08653v2
- Date: Thu, 6 Jan 2022 22:52:23 GMT
- Title: Modeling Noisy Quantum Circuits Using Experimental Characterization
- Authors: Megan L. Dahlhauser, Travis S. Humble
- Abstract summary: Noisy intermediate-scale quantum (NISQ) devices offer unique platforms to test and evaluate the behavior of non-fault-tolerant quantum computing.
We present a test-driven approach to characterizing NISQ programs that manages the complexity of noisy circuit modeling.
- Score: 0.40611352512781856
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Noisy intermediate-scale quantum (NISQ) devices offer unique platforms to
test and evaluate the behavior of non-fault-tolerant quantum computing.
However, validating programs on NISQ devices is difficult due to fluctuations
in the underlying noise sources and other non-reproducible behaviors that
generate computational errors. Efficient and effective methods for modeling
NISQ behaviors are necessary to debug these devices and develop programming
techniques that mitigate against errors. We present a test-driven approach to
characterizing NISQ programs that manages the complexity of noisy circuit
modeling by decomposing an application-specific circuit into a series of
bootstrapped experiments. By characterizing individual subcircuits, we generate
a composite model for the original noisy quantum circuit as well as other
related programs. We demonstrate this approach using a family of
superconducting transmon devices running applications of GHZ-state preparation
and the Bernstein-Vazirani algorithm. We measure the model accuracy using the
total variation distance between predicted and experimental results, and we
find that the composite model works well across multiple circuit instances. In
addition, these characterizations are computationally efficient and offer a
trade-off in model complexity that can be tailored to the desired predictive
accuracy.
Related papers
- Learning Density Functionals from Noisy Quantum Data [0.0]
noisy intermediate-scale quantum (NISQ) devices are used to generate training data for machine learning (ML) models.
We show that a neural-network ML model can successfully generalize from small datasets subject to noise typical of NISQ algorithms.
Our findings suggest a promising pathway for leveraging NISQ devices in practical quantum simulations.
arXiv Detail & Related papers (2024-09-04T17:59:55Z) - 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) - 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) - Score-based Diffusion Models in Function Space [140.792362459734]
Diffusion models have recently emerged as a powerful framework for generative modeling.
We introduce a mathematically rigorous framework called Denoising Diffusion Operators (DDOs) for training diffusion models in function space.
We show that the corresponding discretized algorithm generates accurate samples at a fixed cost independent of the data resolution.
arXiv Detail & Related papers (2023-02-14T23:50:53Z) - Importance sampling for stochastic quantum simulations [68.8204255655161]
We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients.
We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage.
Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
arXiv Detail & Related papers (2022-12-12T15:06:32Z) - Generalization Metrics for Practical Quantum Advantage in Generative
Models [68.8204255655161]
Generative modeling is a widely accepted natural use case for quantum computers.
We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance.
Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
arXiv Detail & Related papers (2022-01-21T16:35:35Z) - Measuring NISQ Gate-Based Qubit Stability Using a 1+1 Field Theory and
Cycle Benchmarking [50.8020641352841]
We study coherent errors on a quantum hardware platform using a transverse field Ising model Hamiltonian as a sample user application.
We identify inter-day and intra-day qubit calibration drift and the impacts of quantum circuit placement on groups of qubits in different physical locations on the processor.
This paper also discusses how these measurements can provide a better understanding of these types of errors and how they may improve efforts to validate the accuracy of quantum computations.
arXiv Detail & Related papers (2022-01-08T23:12:55Z) - Improving readout in quantum simulations with repetition codes [0.0]
We use repetition codes as scalable schemes with the potential to provide more accurate solutions to problems of interest in quantum chemistry and physics.
We showcase our approach in multiple IBM Quantum devices and validate our results using a simplified theoretical noise model.
arXiv Detail & Related papers (2021-05-27T18:01:05Z) - Randomizing multi-product formulas for Hamiltonian simulation [2.2049183478692584]
We introduce a scheme for quantum simulation that unites the advantages of randomized compiling on the one hand and higher-order multi-product formulas on the other.
Our framework reduces the circuit depth by circumventing the need for oblivious amplitude amplification.
Our algorithms achieve a simulation error that shrinks exponentially with the circuit depth.
arXiv Detail & Related papers (2021-01-19T19:00:23Z) - Sinkhorn Natural Gradient for Generative Models [125.89871274202439]
We propose a novel Sinkhorn Natural Gradient (SiNG) algorithm which acts as a steepest descent method on the probability space endowed with the Sinkhorn divergence.
We show that the Sinkhorn information matrix (SIM), a key component of SiNG, has an explicit expression and can be evaluated accurately in complexity that scales logarithmically.
In our experiments, we quantitatively compare SiNG with state-of-the-art SGD-type solvers on generative tasks to demonstrate its efficiency and efficacy of our method.
arXiv Detail & Related papers (2020-11-09T02:51:17Z) - Efficient classical simulation and benchmarking of quantum processes in
the Weyl basis [0.0]
We develop a randomized benchmarking algorithm which uses Weyl unitaries to efficiently identify and learn a mixture of error models.
We apply our methods to ansatz circuits that appear in the Variational Quantum Eigensolver.
arXiv Detail & Related papers (2020-08-27T16:46:12Z)
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.