Noise-Induced Barren Plateaus in Variational Quantum Algorithms
- URL: http://arxiv.org/abs/2007.14384v6
- Date: Sat, 2 Mar 2024 02:23:40 GMT
- Title: Noise-Induced Barren Plateaus in Variational Quantum Algorithms
- Authors: Samson Wang, Enrico Fontana, M. Cerezo, Kunal Sharma, Akira Sone,
Lukasz Cincio, Patrick J. Coles
- Abstract summary: 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.
- Score: 0.3562485774739681
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on
Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is
whether noise on NISQ devices places fundamental limitations on VQA
performance. We rigorously prove a serious limitation for noisy VQAs, in that
the noise causes the training landscape to have a barren plateau (i.e.,
vanishing gradient). Specifically, for the local Pauli noise considered, we
prove that the gradient vanishes exponentially in the number of qubits $n$ if
the depth of the ansatz grows linearly with $n$. These noise-induced barren
plateaus (NIBPs) are conceptually different from noise-free barren plateaus,
which are linked to random parameter initialization. Our result is formulated
for a generic ansatz that includes as special cases the Quantum Alternating
Operator Ansatz and the Unitary Coupled Cluster Ansatz, among others. For the
former, our numerical heuristics demonstrate the NIBP phenomenon for a
realistic hardware noise model.
Related papers
- Enhancing Quantum Variational Algorithms with Zero Noise Extrapolation
via Neural Networks [0.4779196219827508]
Variational Quantum Eigensolver (VQE) is a promising algorithm for solving complex quantum problems.
The ubiquitous presence of noise in quantum devices often limits the accuracy and reliability of VQE outcomes.
This research introduces a novel approach by utilizing neural networks for zero noise extrapolation (ZNE) in VQE computations.
arXiv Detail & Related papers (2024-03-10T15:35:41Z) - Beyond unital noise in variational quantum algorithms: noise-induced barren plateaus and limit sets [0.0]
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.
arXiv Detail & Related papers (2024-02-13T19:00:05Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - 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) - 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) - Simulating strongly interacting Hubbard chains with the Variational
Hamiltonian Ansatz on a quantum computer [0.0]
Variational Quantum Eigensolver (VQE) has been implemented to study molecules and condensed matter systems on small size quantum computers.
We try to answer the question: how much of the underlying physics of a 1D Hubbard chain is described by a problem-inspired Variational Hamiltonian Ansatz (VHA) in a broad range of parameter values.
Our findings suggest that even low fidelity solutions capture energy and number of doubly occupied sites well, while spin-spin correlations are not well captured even when the solution is of high fidelity.
arXiv Detail & Related papers (2021-11-23T16:54:36Z) - 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) - 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) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z) - On the learnability of quantum neural networks [132.1981461292324]
We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme.
We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
arXiv Detail & Related papers (2020-07-24T06:34:34Z)
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.