Amplitude Amplification and Estimation using a Floquet system
- URL: http://arxiv.org/abs/2406.13211v2
- Date: Thu, 29 Aug 2024 05:14:56 GMT
- Title: Amplitude Amplification and Estimation using a Floquet system
- Authors: Keshav V, M. S. Santhanam,
- Abstract summary: The quantum kicked rotor (QKR) is a fundamental model of time-dependent quantum chaos.
It is used to implement a quantum algorithm to perform unstructured search.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The quantum kicked rotor (QKR) is a fundamental model of time-dependent quantum chaos and the physics of Anderson localization. It is one of the most well-studied Floquet systems. In this work, it is shown that QKR can be used to implement a quantum algorithm to perform unstructured search; namely Amplitude Amplification, a generalization of Grover's search algorithm. Further, the QKR is employed for amplitude estimation when the amplitude of the marked states is unknown. It is also shown that the characteristic property of dynamical localization of the QKR can be exploited to enhance the performance of the amplitude amplification algorithm by reducing its average runtime. The sensitivity of the success probability of unstructured search to detuning from resonance and the effects of noisy kick strengths are analyzed and the robustness of the QKR based algorithm is demonstrated. The experimental feasibility of every component of the algorithm is discussed.
Related papers
- Quantum Algorithm for Signal Denoising [32.77959665599749]
The proposed algorithm is able to process textitboth classical and quantum signals.
Numerical results show that it is efficient at removing noise of both classical and quantum origin.
arXiv Detail & Related papers (2023-12-24T05:16:04Z) - Iterative Qubit Coupled Cluster using only Clifford circuits [36.136619420474766]
An ideal state preparation protocol can be characterized by being easily generated classically.
We propose a method that meets these requirements by introducing a variant of the iterative qubit coupled cluster (iQCC)
We demonstrate the algorithm's correctness in ground-state simulations and extend our study to complex systems like the titanium-based compound Ti(C5H5)(CH3)3 with a (20, 20) active space.
arXiv Detail & Related papers (2022-11-18T20:31:10Z) - 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) - Real Quantum Amplitude Estimation [0.0]
We introduce the Real Quantum Amplitude Estimation (RQAE) algorithm, an extension of Quantum Amplitude Estimation (QAE)
RQAE is an iterative algorithm which offers explicit control over the amplification policy through an adjustable parameter.
arXiv Detail & Related papers (2022-04-28T17:02:20Z) - Grover search revisited; application to image pattern matching [0.8367938108534343]
We propose a quantum algorithm that approximately executes the entire Grover database search or pattern matching algorithm.
The key idea is to use the recently proposed approximate amplitude encoding method on a shallow quantum circuit.
arXiv Detail & Related papers (2021-08-24T17:30:41Z) - 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) - On the Cryptographic Hardness of Learning Single Periodic Neurons [42.86685497609574]
We show a simple reduction which demonstrates the cryptographic hardness of learning a single neuron over isotropic Gaussian distributions in the presence of noise.
Our proposed algorithm is not a gradient-based or an adversarial SQ-time algorithm, but is rather based on the celebrated Lenstra-LenstraLov'asz (LLL) lattice basis reduction algorithm.
arXiv Detail & Related papers (2021-06-20T20:03:52Z) - Tunable Tradeoff between Quantum and Classical Computation via
Nonunitary Zeno-like Dynamics [0.5249805590164902]
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.
arXiv Detail & Related papers (2020-11-22T00:57:17Z) - Plug-And-Play Learned Gaussian-mixture Approximate Message Passing [71.74028918819046]
We propose a plug-and-play compressed sensing (CS) recovery algorithm suitable for any i.i.d. source prior.
Our algorithm builds upon Borgerding's learned AMP (LAMP), yet significantly improves it by adopting a universal denoising function within the algorithm.
Numerical evaluation shows that the L-GM-AMP algorithm achieves state-of-the-art performance without any knowledge of the source prior.
arXiv Detail & Related papers (2020-11-18T16:40:45Z) - ACSS-q: Algorithmic complexity for short strings via quantum accelerated
approach [1.4873907857806357]
We present a quantum circuit for estimating algorithmic complexity using the coding theorem method.
As a use-case, an application framework for protein-protein interaction based on algorithmic complexity is proposed.
arXiv Detail & Related papers (2020-09-18T14:41:41Z) - Efficient classical simulation and benchmarking of quantum processes in
the Weyl basis [0.0]
We develop a randomized benchmarking algorithm which uses Weyl unitaries to efficiently identify and learn a mixture of error models.
We apply our methods to ansatz circuits that appear in the Variational Quantum Eigensolver.
arXiv Detail & Related papers (2020-08-27T16:46:12Z)
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.