Noise-Aware Quantum Amplitude Estimation
- URL: http://arxiv.org/abs/2109.04840v3
- Date: Wed, 15 May 2024 11:44:20 GMT
- Title: Noise-Aware Quantum Amplitude Estimation
- Authors: Steven Herbert, Ifan Williams, Roland Guichard, Darren Ng,
- Abstract summary: We provide results from quantum amplitude estimation run on various IBM superconducting quantum computers.
We show that the proposed model is a good fit for real-world experimental data.
- Score: 0.18641315013048293
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In this paper we derive from simple and reasonable assumptions a Gaussian noise model for quantum amplitude estimation. We provide results from quantum amplitude estimation run on various IBM superconducting quantum computers and Quantinuum's H1 trapped-ion quantum computer to show that the proposed model is a good fit for real-world experimental data. We also show that the proposed Gaussian noise model can be easily composed with other noise models in order to capture effects that are not well described by Gaussian noise. We then give a generalised procedure for how to embed this noise model into any quantum-phase-estimation-free QAE algorithm, such that the amplitude estimation is ``noise aware''.
Related papers
- Power Characterization of Noisy Quantum Kernels [52.47151453259434]
We show that noise may make quantum kernel methods to only have poor prediction capability, even when the generalization error is small.
We provide a crucial warning to employ noisy quantum kernel methods for quantum computation.
arXiv Detail & Related papers (2024-01-31T01:02:16Z) - General noise-resilient quantum amplitude estimation [0.0]
We present a novel algorithm that enhances the estimation of amplitude and observable under noise.
Remarkably, our algorithm exhibits robustness against noise that varies across different depths of the quantum circuits.
arXiv Detail & Related papers (2023-12-02T09:27:40Z) - 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) - Quantum Conformal Prediction for Reliable Uncertainty Quantification in
Quantum Machine Learning [47.991114317813555]
Quantum models implement implicit probabilistic predictors that produce multiple random decisions for each input through measurement shots.
This paper proposes to leverage such randomness to define prediction sets for both classification and regression that provably capture the uncertainty of the model.
arXiv Detail & Related papers (2023-04-06T22:05:21Z) - Leveraging hardware-control imperfections for error mitigation via
generalized quantum subspace [0.8399688944263843]
In the era of quantum computing without full fault-tolerance, it is essential to suppress noise effects via the quantum error mitigation techniques to enhance the computational power of the quantum devices.
One of the most effective noise-agnostic error mitigation schemes is the generalized quantum subspace expansion (GSE) method.
We propose the fault-subspace method, which constructs an error-mitigated quantum state with copies of quantum states with different noise levels.
arXiv Detail & Related papers (2023-03-14T07:01:30Z) - 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) - Error mitigation and quantum-assisted simulation in the error corrected
regime [77.34726150561087]
A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations.
We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
arXiv Detail & Related papers (2021-03-12T20:58:41Z) - Simulating noisy variational quantum eigensolver with local noise models [4.581041382009666]
Variational quantum eigensolver (VQE) is promising to show quantum advantage on near-term noisy-intermediate-scale quantum computers.
One central problem of VQE is the effect of noise, especially the physical noise on realistic quantum computers.
We study systematically the effect of noise for the VQE algorithm, by performing numerical simulations with various local noise models.
arXiv Detail & Related papers (2020-10-28T08:51:59Z) - Amplitude estimation via maximum likelihood on noisy quantum computer [3.5462326830737805]
We give an experimental demonstration on a superconducting IBM Quantum device.
We show that the proposed maximum likelihood estimator achieves quantum speedup in the number of queries.
arXiv Detail & Related papers (2020-06-29T17:44:04Z) - State preparation and measurement in a quantum simulation of the O(3)
sigma model [65.01359242860215]
We show that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site.
We apply Trotter methods to obtain results for the complexity of adiabatic ground state preparation in both the weak-coupling and quantum-critical regimes.
We present and analyze a quantum algorithm based on non-unitary randomized simulation methods.
arXiv Detail & Related papers (2020-06-28T23:44:12Z) - Shape Matters: Understanding the Implicit Bias of the Noise Covariance [76.54300276636982]
Noise in gradient descent provides a crucial implicit regularization effect for training over parameterized models.
We show that parameter-dependent noise -- induced by mini-batches or label perturbation -- is far more effective than Gaussian noise.
Our analysis reveals that parameter-dependent noise introduces a bias towards local minima with smaller noise variance, whereas spherical Gaussian noise does not.
arXiv Detail & Related papers (2020-06-15T18:31:02Z)
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.