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Real-time adaptation of quantum noise channel estimates

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arXiv 2025年

作者： Daguerre, Lucas Sarovar, Mohan Department of Physics and Astronomy University of California DavisCA95616 United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories LivermoreCA94550 United States

Estimates of noise channels for quantum gates are required for most error mitigation techniques and are desirable for informing quantum error correction decoders. These estimates can be obtained by resource-intensive off-line characterization techniques, but can become stale due to device drift and fluctuations. We propose a method to address this issue by performing real-time adaptation of noise channel estimates during the execution of a quantum algorithmic circuit using extended flag gadgets, mid-circuit measurements and Bayesian inference. We carry out analytical calculations and numerical simulations employing a Dirichlet prior distribution for the error rates in a Pauli channel to demonstrate and evaluate the technique, which can be seen as a protocol for real-time calibration of high-level gate error information. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum noise

Lyapunov-control-inspired strategies for quantum combinatorial optimization

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Physical Review A 2022年第6期106卷 062414-062414页

作者： Alicia B. Magann Kenneth M. Rudinger Matthew D. Grace Mohan Sarovar Quantum Algorithms and Applications Collaboratory Sandia National Laboratories Livermore California 94550 USA Quantum Algorithms and Applications Collaboratory Sandia National Laboratories Albuquerque New Mexico 87185 USA Department of Chemical & Biological Engineering Princeton University Princeton New Jersey 08544 USA

The prospect of using quantum computers to solve combinatorial optimization problems via the quantum approximate optimization algorithm (QAOA) has attracted considerable interest in recent years. However, a key limitation associated with QAOA is the need to classically optimize over a set of quantum circuit parameters. This classical optimization can have significant associated costs and challenges. Here we provide an expanded description of Lyapunov control-inspired strategies for quantum optimization, as presented in [Magann et al., Phys. Rev. Lett. 129, 250502 (2022)], that do not require any classical optimization effort. Instead, these strategies utilize feedback from qubit measurements to assign values to the quantum circuit parameters in a deterministic manner, such that the combinatorial optimization problem solution improves monotonically with the quantum circuit depth. Numerical analyses are presented that investigate the utility of these strategies towards MaxCut on weighted and unweighted 3-regular graphs, both in ideal implementations and in the presence of measurement noise. We also discuss how how these strategies compare with QAOA and how they may be used to seed QAOA optimizations in order to improve performance for near-term applications, and we explore connections to quantum annealing.

关键词： Quantum algorithms Quantum control

Feedback-Based Quantum Optimization

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Physical Review Letters 2022年第25期129卷 250502-250502页

作者： Alicia B. Magann Kenneth M. Rudinger Matthew D. Grace Mohan Sarovar Quantum Algorithms and Applications Collaboratory Sandia National Laboratories Livermore California 94550 USA Quantum Algorithms and Applications Collaboratory Sandia National Laboratories Albuquerque New Mexico 87185 USA Department of Chemical and Biological Engineering Princeton University Princeton New Jersey 08544 USA

It is hoped that quantum computers will offer advantages over classical computers for combinatorial optimization. Here, we introduce a feedback-based strategy for quantum optimization, where the results of qubit measurements are used to constructively assign values to quantum circuit parameters. We show that this procedure results in an estimate of the combinatorial optimization problem solution that improves monotonically with the depth of the quantum circuit. Importantly, the measurement-based feedback enables approximate solutions to the combinatorial optimization problem without the need for any classical optimization effort, as would be required for the quantum approximate optimization algorithm. We demonstrate this feedback-based protocol on a superconducting quantum processor for the graph-partitioning problem MaxCut, and present a series of numerical analyses that further investigate the protocol’s performance.

关键词： Quantum algorithms Quantum control Variational approach

Benchmarking Photonic Quantum Machine Learning Simulators 17

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Benchmarking Photonic Quantum Machine Learning Simulators

17th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Varga, Henrik Kiss, Attila Kolarovszki, Zoltan Elte Eötvös Loránd University Department of Algorithms and Their Applications Budapest Hungary Elte Eötvös Loránd University Department of Information Systems Budapest Hungary J. Selye University Department of Informatics Komárno Slovakia Department of Programming Languages and Compilers Elte Eotvos Lorand University Budapest Hungary Wigner Research Centre for Physics Department of Computational Sciences Budapest Hungary

ISBN: (纸本)9798350321104

In the past few years, quantum computing has gotten more attention, and the need for efficient simulations is getting increasingly important as well. A significant branch of quantum computing is photonic quantum computing. For simulating photonic quantum circuits, Strawberry Fields is the most popular framework. In this paper, we compared it with another framework currently under development called Piquasso regarding gradient calculation time, which is an essential part of continuous-variable quantum neural networks. We present the apparent scalability of Piquasso over Strawberry Fields by storing fewer data, but leading to possible accuracy differences as a trade-off, which could motivate future work. © 2023 IEEE.

关键词： Quantum optics

Feedback-based quantum algorithms for ground state preparation

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arXiv 2023年

作者： Larsen, James B. Grace, Matthew D. Baczewski, Andrew D. Magann, Alicia B. Department of Mathematics University of Michigan Ann ArborMI48109 United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories AlbuquerqueNM87185 United States Department of Mathematics Brigham Young University ProvoUT84604 United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories LivermoreCA94550 United States

The ground state properties of quantum many-body systems are a subject of interest across chemistry, materials science, and physics. Thus, algorithms for finding ground states can have broad impacts. Variational quantum algorithms are one class of ground state algorithms that has received significant attention in recent years. These algorithms utilize a hybrid quantum-classical computing framework to prepare ground states on quantum computers. However, this requires solving a classical optimization problem that can become prohibitively expensive in high dimensions. Here, we develop formulations of feedback-based quantum algorithms for ground state preparation that can be used to address this challenge for two broad classes of Hamiltonians: Fermi-Hubbard Hamiltonians, and molecular Hamiltonians represented in second quantization. Feedback-based quantum algorithms are optimization-free;in place of classical optimization, quantum circuit parameters are set according to a deterministic feedback law derived from quantum Lyapunov control principles. This feedback law guarantees a monotonic improvement in solution quality with respect to the depth of the quantum circuit. A variety of numerical illustrations are provided that analyze the convergence and robustness of feedback-based quantum algorithms for these problem classes. Copyright © 2023, The Authors. All rights reserved.

关键词： Ground state

Constrained local Hamiltonians: quantum generalizations of Vertex Cover

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arXiv 2024年

作者： Parekh, Ojas Rayudu, Chaithanya Thompson, Kevin Quantum Algorithms and Applications Collaboratory Sandia National Laboratories AlbuquerqueNM United States Department of Physics and Astronomy Center for Quantum Information and Control University of New Mexico AlbuquerqueNM United States

Recent successes in producing rigorous approximation algorithms for local Hamiltonian problems such as Quantum Max Cut have exploited connections to unconstrained classical discrete optimization problems. We initiate the study of approximation algorithms for constrained local Hamiltonian problems, using the well-studied classical Vertex Cover problem as inspiration. We consider natural quantum generalizations of Vertex Cover, and one of them, called Transverse Vertex Cover (TVC), is equivalent to the PXP model with additional 1-local Pauli-Z terms. We show TVC is StoqMA-hard and develop an approximation algorithm for it based on a quantum generalization of the classical local ratio method. This results in a simple linear-time classical approximation algorithm that does not depend on solving a convex relaxation. We also demonstrate our quantum local ratio method on a traditional unconstrained quantum local Hamiltonian version of Vertex Cover which is equivalent to the anti-ferromagnetic transverse field Ising model. © 2024, CC BY.

关键词： Ising model

NC-ALG: Graph-Based Active Learning under Noisy Crowd 40

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NC-ALG: Graph-Based Active Learning under Noisy Crowd

40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Li, Yang Cao, Gang Yang, Zhi Cui, Bin Center for Machine Learning Research Peking University China Key Lab of High Confidence Software Technologies Peking University China Institute of Advanced Algorithms Research Shanghai China Institute of Computational Social Science Peking University Qingdao China National Engineering Labratory for Big Data Analytics and Applications China TEG Tencent Inc. Department of Data Platform China Beijing Academy of Artificial Intelligence China

ISBN: (纸本)9798350317152

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed. © 2024 IEEE.

关键词： Crowdsourcing

Quantum dynamical emulation of imaginary time evolution

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arXiv 2024年

作者： Leamer, Jacob M. Magann, Alicia B. Bondar, Denys I. McCaul, Gerard Tulane University New OrleansLA70118 United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories AlbuquerqueNM87185 United States Department of Physics Loughborough University Loughborough United Kingdom

We introduce a constructive method for mapping non-unitary dynamics to a weighted set of unitary operations. We utilize this construction to derive a new correspondence between real and imaginary time, which we term Imaginary Time Quantum Dynamical Emulation (ITQDE). This correspondence enables an imaginary time evolution to be constructed from the overlaps of states evolved in opposite directions. We develop ITQDE as a tool for estimating the ground and thermal state properties associated with a given Hamiltonian. We additionally provide a prescription for leveraging ITQDE to estimate the complete Hamiltonian spectrum. We go on to develop a quantum algorithm for computing Hamiltonian spectra based on ITQDE, which we validate through numerical simulations and quantum hardware implementations. We conclude with a discussion of how ITQDE can be utilized more broadly to derive novel thermodynamic results, including a generalisation of the Hubbard-Stratonovich transformation. © 2024, CC BY.

关键词： Hamiltonians

Self-healing of Trotter error in digital adiabatic state preparation

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arXiv 2022年

作者： Kovalsky, Lucas K. Calderon-Vargas, Fernando A. Grace, Matthew D. Magann, Alicia B. Larsen, James B. Baczewski, Andrew D. Sarovar, Mohan Quantum Algorithms and Applications Collaboratory Sandia National Laboratories LivermoreCA94550 United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories AlbuquerqueNM87185 United States Department of Mathematics Brigham Young University ProvoUT84602 United States

Adiabatic time evolution can be used to prepare a complicated quantum many-body state from one that is easier to synthesize and Trotterization can be used to implement such an evolution digitally. The complex interplay between non-adiabaticity and digitization influences the infidelity of this process. We prove that the first-order Trotterization of a complete adiabatic evolution has a cumulative infidelity that scales as O(T−2δt2) instead of O(T2δt2) expected from general Trotter error bounds, where δt is the time step and T is the total time. This result suggests a self-healing mechanism and explains why, despite increasing T, infidelities for fixed-δt digitized evolutions still decrease for a wide variety of Hamiltonians. It also establishes a correspondence between the Quantum Approximate Optimization Algorithm (QAOA) and digitized quantum annealing. Copyright © 2022, The Authors. All rights reserved.

关键词： Self-healing materials