Evaluating the noise resilience of variational quantum algorithms

• URL: http://arxiv.org/abs/2011.01125v3
• Date: Mon, 23 Nov 2020 18:19:07 GMT
• Title: Evaluating the noise resilience of variational quantum algorithms
• Authors: Enrico Fontana, Nathan Fitzpatrick, David Mu\~noz Ramo, Ross Duncan, Ivan Rungger
• Abstract summary: We simulate the effects of different types of noise in state preparation circuits of variational quantum algorithms. We find that the inclusion of redundant parameterised gates makes the quantum circuits more resilient to noise.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We simulate the effects of different types of noise in state preparation circuits of variational quantum algorithms. We first use a variational quantum eigensolver to find the ground state of a Hamiltonian in presence of noise, and adopt two quality measures in addition to the energy, namely fidelity and concurrence. We then extend the task to the one of constructing, with a layered quantum circuit ansatz, a set of general random target states. We determine the optimal circuit depth for different types and levels of noise, and observe that the variational algorithms mitigate the effects of noise by adapting the optimised parameters. We find that the inclusion of redundant parameterised gates makes the quantum circuits more resilient to noise. For such overparameterised circuits different sets of parameters can result in the same final state in the noiseless case, which we denote as parameter degeneracy. Numerically, we show that this degeneracy can be lifted in the presence of noise, with some states being significantly more resilient to noise than others. We also show that the average deviation from the target state is linear in the noise level, as long as this is small compared to a circuit-dependent threshold. In this region the deviation is well described by a stochastic model. Above the threshold, the optimisation can converge to states with largely different physical properties from the true target state, so that for practical applications it is critical to ensure that noise levels are below this threshold.

Related papers

• Optimized Noise Suppression for Quantum Circuits [0.40964539027092917]
  Crosstalk noise is a severe error source in, e.g., cross-resonance based superconducting quantum processors. Intrepid programming algorithm extends previous work on optimized qubit routing by swap insertion. We evaluate the proposed method by characterizing crosstalk noise for two chips with up to 127 qubits.
  arXiv  Detail & Related papers  (2024-01-12T07:34:59Z)
• 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)
• Accurate and Honest Approximation of Correlated Qubit Noise [39.58317527488534]
  We propose an efficient systematic construction of approximate noise channels, where their accuracy can be enhanced by incorporating noise components with higher qubit-qubit correlation degree. We find that, for realistic noise strength typical for fixed-frequency superconducting qubits, correlated noise beyond two-qubit correlation can significantly affect the code simulation accuracy.
  arXiv  Detail & Related papers  (2023-11-15T19:00:34Z)
• 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)
• Error-aware Quantization through Noise Tempering [43.049102196902844]
  Quantization-aware training (QAT) optimize model parameters with respect to the end task while simulating quantization error. In this work, we incorporate exponentially decaying quantization-error-aware noise together with a learnable scale of task loss gradient to approximate the effect of a quantization operator. Our method obtains state-of-the-art top-1 classification accuracy for uniform (non mixed-precision) quantization, out-performing previous methods by 0.5-1.2% absolute.
  arXiv  Detail & Related papers  (2022-12-11T20:37:50Z)
• Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis [42.275148861039895]
  Variational quantum algorithms (VQAs) offer the most promising path to obtaining quantum advantages via noisy processors. gate noise due to imperfections and decoherence affects the gradient estimates by introducing a bias. Quantum error mitigation (QEM) techniques can reduce the estimation bias without requiring any increase in the number of qubits. QEM can reduce the number of required iterations, but only as long as the quantum noise level is sufficiently small.
  arXiv  Detail & Related papers  (2022-09-23T10:48:04Z)
• Numerical Simulations of Noisy Quantum Circuits for Computational Chemistry [51.827942608832025]
  Near-term quantum computers can calculate the ground-state properties of small molecules. We show how the structure of the computational ansatz as well as the errors induced by device noise affect the calculation.
  arXiv  Detail & Related papers  (2021-12-31T16:33:10Z)
• Using gradient-based algorithms to determine ground state energies on a quantum computer [0.0]
  Variational algorithms are promising candidates to be implemented on near-term quantum computers. We study how different methods for obtaining the gradient, specifically the finite-difference and the parameter-shift rule, are affected by shot noise and noise of the quantum computer.
  arXiv  Detail & Related papers  (2021-09-17T09:12:43Z)
• Modeling and mitigation of cross-talk effects in readout noise with applications to the Quantum Approximate Optimization Algorithm [0.0]
  Noise mitigation can be performed up to some error for which we derive upper bounds. Experiments on 15 (23) qubits using IBM's devices to test both the noise model and the error-mitigation scheme. We show that similar effects are expected for Haar-random quantum states and states generated by shallow-depth random circuits.
  arXiv  Detail & Related papers  (2021-01-07T02:19:58Z)
• Efficient and robust certification of genuine multipartite entanglement in noisy quantum error correction circuits [58.720142291102135]
  We introduce a conditional witnessing technique to certify genuine multipartite entanglement (GME) We prove that the detection of entanglement in a linear number of bipartitions by a number of measurements scales linearly, suffices to certify GME. We apply our method to the noisy readout of stabilizer operators of the distance-three topological color code and its flag-based fault-tolerant version.
  arXiv  Detail & Related papers  (2020-10-06T18:00:07Z)

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.