Beyond unital noise in variational quantum algorithms: noise-induced barren plateaus and limit sets
- URL: http://arxiv.org/abs/2402.08721v5
- Date: Fri, 2 Aug 2024 19:54:49 GMT
- Title: Beyond unital noise in variational quantum algorithms: noise-induced barren plateaus and limit sets
- Authors: P. Singkanipa, D. A. Lidar,
- Abstract summary: Variational quantum algorithms (VQAs) hold much promise but face the challenge of exponentially small gradients.
Noise-induced barren plateaus (NIBPs) are a type of unavoidable BP arising from open system effects.
We identify the associated phenomenon of noise-induced limit sets (NILS) of the VQA cost function and prove its existence for both unital and HS-contractive non-unital noise maps.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Variational quantum algorithms (VQAs) hold much promise but face the challenge of exponentially small gradients. Unmitigated, this barren plateau (BP) phenomenon leads to an exponential training overhead for VQAs. Perhaps the most pernicious are noise-induced barren plateaus (NIBPs), a type of unavoidable BP arising from open system effects, which have so far been shown to exist for unital noise maps. Here, we generalize the study of NIBPs to more general completely positive, trace-preserving maps, establishing the existence of NIBPs in the unital case and a class of non-unital maps we call Hilbert-Schmidt (HS)-contractive. The latter includes amplitude damping. We identify the associated phenomenon of noise-induced limit sets (NILS) of the VQA cost function and prove its existence for both unital and HS-contractive non-unital noise maps. Along the way, we extend the parameter shift rule of VQAs to the noisy setting. We provide rigorous bounds in terms of the relevant variables that give rise to NIBPs and NILSs, along with numerical simulations of the depolarizing and amplitude-damping maps that illustrate our analytical results.
Related papers
- Mixed-State Topological Order under Coherent Noises [2.8391355909797644]
We find remarkable stability of mixed-state topological order under random rotation noise with axes near the $Y$-axis of qubits.
The upper bounds for the intrinsic error threshold are determined by these phase boundaries, beyond which quantum error correction becomes impossible.
arXiv Detail & Related papers (2024-11-05T19:00:06Z) - Emergence of noise-induced barren plateaus in arbitrary layered noise models [44.99833362998488]
In variational quantum algorithms the parameters of a parameterized quantum circuit are optimized in order to minimize a cost function that encodes the solution of the problem.
We discuss how, and in which sense, the phenomenon of noise-induced barren plateaus emerges in parameterized quantum circuits with a layered noise model.
arXiv Detail & Related papers (2023-10-12T15:18:27Z) - Noise correlations behind superdiffusive quantum walks [0.0]
We study how discrete-time quantum walks behave under short-range correlated noise.
For spatial inhomogeneities, we show noise correlations driving quantum walks from the well-known exponentially localized condition to superdiffusive spreading.
arXiv Detail & Related papers (2022-07-26T18:54:28Z) - Stabilizing and improving qubit coherence by engineering noise spectrum
of two-level systems [52.77024349608834]
Superconducting circuits are a leading platform for quantum computing.
Charge fluctuators inside amorphous oxide layers contribute to both low-frequency $1/f$ charge noise and high-frequency dielectric loss.
We propose to mitigate those harmful effects by engineering the relevant TLS noise spectral densities.
arXiv Detail & Related papers (2022-06-21T18:37:38Z) - High-Order Qubit Dephasing at Sweet Spots by Non-Gaussian Fluctuators:
Symmetry Breaking and Floquet Protection [55.41644538483948]
We study the qubit dephasing caused by the non-Gaussian fluctuators.
We predict a symmetry-breaking effect that is unique to the non-Gaussian noise.
arXiv Detail & Related papers (2022-06-06T18:02:38Z) - Clipped Stochastic Methods for Variational Inequalities with
Heavy-Tailed Noise [64.85879194013407]
We prove the first high-probability results with logarithmic dependence on the confidence level for methods for solving monotone and structured non-monotone VIPs.
Our results match the best-known ones in the light-tails case and are novel for structured non-monotone problems.
In addition, we numerically validate that the gradient noise of many practical formulations is heavy-tailed and show that clipping improves the performance of SEG/SGDA.
arXiv Detail & Related papers (2022-06-02T15:21:55Z) - 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) - Noise-Induced Barren Plateaus in Variational Quantum Algorithms [0.3562485774739681]
Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) computers.
We rigorously prove a serious limitation for noisy VQAs, in that the noise causes the training landscape to have a barren plateau.
arXiv Detail & Related papers (2020-07-28T17:52:21Z) - 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.