Tunable Tradeoff between Quantum and Classical Computation via
Nonunitary Zeno-like Dynamics
- URL: http://arxiv.org/abs/2011.10901v2
- Date: Sat, 5 Nov 2022 12:04:59 GMT
- Title: Tunable Tradeoff between Quantum and Classical Computation via
Nonunitary Zeno-like Dynamics
- Authors: P. V. Pyshkin, A. G\'abris, Da-Wei Luo, Jian-Qiang You and Lian-Ao Wu
- Abstract summary: We show that the algorithm scales similarly to the pure quantum version by deriving tight analytical lower bounds on its efficiency.
We also study the behavior of the algorithm subject to noise, and find that under certain oracle and operational errors our measurement-based algorithm outperforms the standard algorithm.
- Score: 0.5249805590164902
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose and analyze a nonunitary variant of the continuous time Grover
search algorithm based on frequent Zeno-type measurements. We show that the
algorithm scales similarly to the pure quantum version by deriving tight
analytical lower bounds on its efficiency for arbitrary database sizes and
measurement parameters. We also study the behavior of the algorithm subject to
noise, and find that under certain oracle and operational errors our
measurement-based algorithm outperforms the standard algorithm, showing
robustness against these noises. Our analysis is based on deriving a
non-hermitian effective description of the algorithm, which yields a deeper
insight into components responsible for the quantum and the classical operation
of the protocol.
Related papers
- Gravitational-wave matched filtering with variational quantum algorithms [0.0]
We explore the application of variational quantum algorithms to the problem of matched filtering in the detection of gravitational waves.
We present results of classical numerical simulations of these quantum algorithms using open science data from LIGO.
arXiv Detail & Related papers (2024-08-23T15:53:56Z) - Benchmarking Variational Quantum Algorithms for Combinatorial Optimization in Practice [0.0]
Variational quantum algorithms and, in particular, variants of the varational quantum eigensolver have been proposed to address optimization (CO) problems.
We numerically investigate what this scaling result means in practice for solving CO problems using Max-Cut as a benchmark.
arXiv Detail & Related papers (2024-08-06T09:57:34Z) - Robustness of Variational Quantum Algorithms against stochastic parameter perturbation [0.0]
Variational quantum algorithms are tailored to perform within the constraints of current quantum devices.
We consider a noise model that reflects realistic gate errors inherent to variational quantum algorithms.
We show that certain gate errors have a significantly smaller impact on the coherence of the state, allowing us to reduce the execution time without compromising performance.
arXiv Detail & Related papers (2022-12-30T20:36:29Z) - Exploring the role of parameters in variational quantum algorithms [59.20947681019466]
We introduce a quantum-control-inspired method for the characterization of variational quantum circuits using the rank of the dynamical Lie algebra.
A promising connection is found between the Lie rank, the accuracy of calculated energies, and the requisite depth to attain target states via a given circuit architecture.
arXiv Detail & Related papers (2022-09-28T20:24:53Z) - Optimal quantum control via genetic algorithms for quantum state
engineering in driven-resonator mediated networks [68.8204255655161]
We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms.
We consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator.
We observe high quantum fidelities and resilience to noise, despite the algorithm being trained in the ideal noise-free setting.
arXiv Detail & Related papers (2022-06-29T14:34:00Z) - 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) - 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) - Self-Guided Quantum State Learning for Mixed States [7.270980742378388]
The salient features of our algorithm are efficient $O left( d3 right)$ post-processing in the infidelity dimension $d$ of the state.
A higher resilience against measurement noise makes our algorithm suitable for noisy intermediate-scale quantum applications.
arXiv Detail & Related papers (2021-06-11T04:40:26Z) - Quantum Algorithms for Data Representation and Analysis [68.754953879193]
We provide quantum procedures that speed-up the solution of eigenproblems for data representation in machine learning.
The power and practical use of these subroutines is shown through new quantum algorithms, sublinear in the input matrix's size, for principal component analysis, correspondence analysis, and latent semantic analysis.
Results show that the run-time parameters that do not depend on the input's size are reasonable and that the error on the computed model is small, allowing for competitive classification performances.
arXiv Detail & Related papers (2021-04-19T00:41:43Z) - Algorithmic Primitives for Quantum-Assisted Quantum Control [1.52292571922932]
We discuss two primitive algorithms to evaluate overlaps and transition matrix time series.
They are used to construct a variety of quantum-assisted quantum control algorithms implementable on NISQ devices.
arXiv Detail & Related papers (2020-11-27T15:20:29Z) - Active Model Estimation in Markov Decision Processes [108.46146218973189]
We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision process (MDP)
We show that our Markov-based algorithm outperforms both our original algorithm and the maximum entropy algorithm in the small sample regime.
arXiv Detail & Related papers (2020-03-06T16:17:24Z)
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.