Quantum Amplitude Amplification Operators
- URL: http://arxiv.org/abs/2105.09559v3
- Date: Tue, 30 Nov 2021 11:40:36 GMT
- Title: Quantum Amplitude Amplification Operators
- Authors: Hyeokjea Kwon, Joonwoo Bae
- Abstract summary: We show the characterization of quantum iterations that would generally construct quantum amplitude amplification algorithms with a quadratic speedup.
We show that an optimal and exact quantum amplitude amplification algorithm corresponds to the Grover algorithm together with a single iteration of QAAO.
We then realize 3-qubit QAAOs with the current quantum technologies via cloud-based quantum computing services, IBMQ and IonQ.
- Score: 3.8073142980733
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: In this work, we show the characterization of quantum iterations that would
generally construct quantum amplitude amplification algorithms with a quadratic
speedup, namely, quantum amplitude amplification operators (QAAOs). Exact
quantum search algorithms that find a target with certainty and with a
quadratic speedup can be composed of sequential applications of QAAO: existing
quantum amplitude amplification algorithms thus turn out to be sequences of
QAAOs. We show that an optimal and exact quantum amplitude amplification
algorithm corresponds to the Grover algorithm together with a single iteration
of QAAO. We then realize 3-qubit QAAOs with the current quantum technologies
via cloud-based quantum computing services, IBMQ and IonQ. Finally, our results
find that fixed-point quantum search algorithms known so far are not a sequence
of QAAOs, e.g. the amplitude of a target state may decrease during quantum
iterations.
Related papers
- Benchmarking Variational Quantum Eigensolvers for Entanglement Detection in Many-Body Hamiltonian Ground States [37.69303106863453]
Variational quantum algorithms (VQAs) have emerged in recent years as a promise to obtain quantum advantage.
We use a specific class of VQA named variational quantum eigensolvers (VQEs) to benchmark them at entanglement witnessing and entangled ground state detection.
Quantum circuits whose structure is inspired by the Hamiltonian interactions presented better results on cost function estimation than problem-agnostic circuits.
arXiv Detail & Related papers (2024-07-05T12:06:40Z) - Quantum quench dynamics as a shortcut to adiabaticity [31.114245664719455]
We develop and test a quantum algorithm in which the incorporation of a quench step serves as a remedy to the diverging adiabatic timescale.
Our experiments show that this approach significantly outperforms the adiabatic algorithm.
arXiv Detail & Related papers (2024-05-31T17:07:43Z) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - Assisted quantum simulation of open quantum systems [0.0]
We introduce the quantum-assisted quantum algorithm, which reduces the circuit depth of UQA via NISQ technology.
We present two quantum-assisted quantum algorithms for simulating open quantum systems.
arXiv Detail & Related papers (2023-02-26T11:41:02Z) - Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - Improved maximum-likelihood quantum amplitude estimation [0.0]
Quantum estimation is a key subroutine in a number of powerful quantum algorithms, including quantum-enhanced Monte Carlo simulation and quantum machine learning.
In this article, we deepen the analysis of Maximum-likelihood quantum amplitude estimation (MLQAE) to put the algorithm in a more prescriptive form, including scenarios where quantum circuit depth is limited.
We then propose and numerically validate a modification to the algorithm to overcome this problem, bringing the algorithm even closer to being useful as a practical subroutine on near- and mid-term quantum hardware.
arXiv Detail & Related papers (2022-09-07T17:30:37Z) - Entanglement and coherence in Bernstein-Vazirani algorithm [58.720142291102135]
Bernstein-Vazirani algorithm allows one to determine a bit string encoded into an oracle.
We analyze in detail the quantum resources in the Bernstein-Vazirani algorithm.
We show that in the absence of entanglement, the performance of the algorithm is directly related to the amount of quantum coherence in the initial state.
arXiv Detail & Related papers (2022-05-26T20:32:36Z) - A Grand Unification of Quantum Algorithms [0.0]
A number of quantum algorithms were recently tied together by a technique known as the quantum singular value transformation.
This paper provides a tutorial through these developments, first illustrating how quantum signal processing may be generalized to the quantum eigenvalue transform.
We then employ QSVT to construct intuitive quantum algorithms for search, phase estimation, and Hamiltonian simulation.
arXiv Detail & Related papers (2021-05-06T17:46:33Z) - Variational quantum compiling with double Q-learning [0.37798600249187286]
We propose a variational quantum compiling (VQC) algorithm based on reinforcement learning (RL)
An agent is trained to sequentially select quantum gates from the native gate alphabet and the qubits they act on by double Q-learning.
It can reduce the errors of quantum algorithms due to decoherence process and gate noise in NISQ devices.
arXiv Detail & Related papers (2021-03-22T06:46:35Z) - Quantum Amplitude Arithmetic [20.84884678978409]
We propose the notion of quantum amplitude arithmetic (QAA) that intent to evolve the quantum state by performing arithmetic operations on amplitude.
QAA is expected to find applications in a variety of quantum algorithms.
arXiv Detail & Related papers (2020-12-21T00:17:18Z)
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.