Physically motivated extrapolation for quantum error mitigation
- URL: http://arxiv.org/abs/2505.07977v1
- Date: Mon, 12 May 2025 18:20:58 GMT
- Title: Physically motivated extrapolation for quantum error mitigation
- Authors: Pablo Díez-Valle, Gaurav Saxena, Jack S. Baker, Jun-Ho Lee, Thi Ha Kyaw,
- Abstract summary: We introduce the Physics-Inspired Extrapolation (PIE) method, built upon the EMRE framework, to achieve enhanced accuracy and robustness.<n>We demonstrate the efficacy of this method on IBMQ hardware and apply it to simulate 84-qubit quantum dynamics efficiently.
- Score: 7.598741686881365
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum error mitigation techniques are essential for the current NISQ and emerging Megaquop-era machines, which, despite their noise, are capable of performing utility-scale quantum computations. However, most QEM methods incur exponential sampling overhead to achieve unbiased estimates, limiting their practical applicability. Recently, it was shown that by using error mitigation by restricted evolution (EMRE), expectation values of a physical observable can be obtained in constant sampling overhead at the cost of a non-zero bias, which grows as the circuit size or hardware noise increases. To overcome this problem, we introduce the Physics-Inspired Extrapolation (PIE) method, built upon the EMRE framework, to achieve enhanced accuracy and robustness. Unlike traditional zero-noise extrapolation, our method assigns operational interpretation to the parameters in the extrapolation function used in PIE. We demonstrate the efficacy of this method on IBMQ hardware and apply it to simulate 84-qubit quantum dynamics efficiently. Our technique yields accurate results with significantly smaller variance, establishing PIE as a practical and scalable error mitigation strategy for near-term and early fault-tolerant quantum computing.
Related papers
- VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
arXiv Detail & Related papers (2025-06-12T01:38:15Z) - Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC)
This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions.
Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
arXiv Detail & Related papers (2024-11-13T12:03:39Z) - Machine Learning for Practical Quantum Error Mitigation [0.0]
We show that machine learning for quantum error mitigation drastically reduces the cost of mitigation.
We propose a path toward scalable mitigation by using ML-QEM to mimic traditional mitigation methods with superior runtime efficiency.
arXiv Detail & Related papers (2023-09-29T16:17:12Z) - Potential and limitations of quantum extreme learning machines [55.41644538483948]
We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements.
Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
arXiv Detail & Related papers (2022-10-03T09:32:28Z) - Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the
Race to Practical Quantum Advantage [43.3054117987806]
We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits.
We show this method significantly improves the trainability and performance of PQCs on a variety of problems.
By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
arXiv Detail & Related papers (2022-08-29T15:24:03Z) - Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state.
In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
arXiv Detail & Related papers (2021-12-27T21:15:35Z) - Error mitigation in variational quantum eigensolvers using tailored
probabilistic machine learning [5.630204194930539]
We present a novel method that employs parametric Gaussian process regression (GPR) within an active learning framework to mitigate noise in quantum computations.
We demonstrate the effectiveness of our method on a 2-site Anderson impurity model and a 8-site Heisenberg model, using the IBM open-source quantum computing framework, Qiskit.
arXiv Detail & Related papers (2021-11-16T22:29:43Z) - 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) - Enhancing quantum models of stochastic processes with error mitigation [0.0]
We bridge the gap between theoretical quantum models and practical use with the inclusion of error mitigation methods.
It is observed that error mitigation is successful in improving the resultant expectation values.
While our results indicate that error mitigation work, we show that its methodology is ultimately constrained by hardware limitations in these quantum computers.
arXiv Detail & Related papers (2021-05-13T17:45:34Z) - 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) - Algorithmic Error Mitigation Scheme for Current Quantum Processors [0.0]
We present a hardware agnostic error mitigation algorithm for near term quantum processors inspired by the classical Lanczos method.
We demonstrate through numerical simulations and experiments on IBM Quantum hardware that the proposed scheme significantly increases the accuracy of cost functions evaluations.
arXiv Detail & Related papers (2020-08-25T09:48:20Z) - Multi-exponential Error Extrapolation and Combining Error Mitigation
Techniques for NISQ Applications [0.0]
Noise in quantum hardware remains the biggest roadblock for the implementation of quantum computers.
Error extrapolation is an error mitigation technique that has been successfully implemented experimentally.
We extend this to multi-exponential error extrapolation and provide more rigorous proof for its effectiveness under Pauli noise.
arXiv Detail & Related papers (2020-07-02T17:18:47Z) - Minimizing estimation runtime on noisy quantum computers [0.0]
"engineered likelihood function" (ELF) is used for carrying out Bayesian inference.
We show how the ELF formalism enhances the rate of information gain in sampling as the physical hardware transitions from the regime of noisy quantum computers.
This technique speeds up a central component of many quantum algorithms, with applications including chemistry, materials, finance, and beyond.
arXiv Detail & Related papers (2020-06-16T17:46:18Z)
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.