Algorithmic Strategies for seizing Quantum Computing
- URL: http://arxiv.org/abs/2112.15175v1
- Date: Thu, 30 Dec 2021 18:57:27 GMT
- Title: Algorithmic Strategies for seizing Quantum Computing
- Authors: Adri\'an P\'erez-Salinas
- Abstract summary: Two strategies are explored to take advantage of inherently quantum properties.
The re-uploading strategy is a variational algorithm related to machine learning.
The unary strategy aims to reduce the density of information stored in a quantum circuit to increase its resilience against noise.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Quantum computing is a nascent technology with prospects to have a huge
impact in the world. Its current status, however, only counts on small and
noisy quantum computers whose performance is limited. In this thesis, two
different strategies are explored to take advantage of inherently quantum
properties and propose recipes to seize quantum computing since its advent.
First, the re-uploading strategy is a variational algorithm related to machine
learning. It consists in introducing data several times along a computation
accompanied by tunable parameters. This process permits the circuit to learn
and mimic any behavior. This capability emerges naturally from the quantum
properties of the circuit. Second, the unary strategy aims to reduce the
density of information stored in a quantum circuit to increase its resilience
against noise. This trade-off between performance and robustness brings an
advantage for noisy devices, where small but meaningful quantum speed-ups can
be found.
Related papers
- The curse of random quantum data [62.24825255497622]
We quantify the performances of quantum machine learning in the landscape of quantum data.
We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits.
Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
arXiv Detail & Related papers (2024-08-19T12:18:07Z) - A Quantum Algorithm Based Heuristic to Hide Sensitive Itemsets [1.8419202109872088]
We present a quantum approach to solve a well-studied problem in the context of data sharing.
We present results on experiments involving small datasets to illustrate how the problem could be solved using quantum algorithms.
arXiv Detail & Related papers (2024-02-12T20:44:46Z) - 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) - Quantum Financial Modeling on Noisy Intermediate-Scale Quantum Hardware:
Random Walks using Approximate Quantum Counting [0.054390204258189995]
We introduce quantum approximate counting circuits that use far fewer 2-qubit entangling gates than traditional quantum counting.
We compare the results to price change distributions from stock indices, and compare the behavior of quantum circuits with and without mid-measurement to trends in the housing market.
arXiv Detail & Related papers (2023-10-17T16:54:31Z) - Stabilization and Dissipative Information Transfer of a Superconducting
Kerr-Cat Qubit [0.0]
We study the dissipative information transfer to a qubit model called Cat-Qubit.
This model is especially important for the dissipative-based version of the binary quantum classification.
Cat-Qubit architecture has the potential to easily implement activation-like functions in artificial neural networks.
arXiv Detail & Related papers (2023-07-23T11:28:52Z) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - Iterative Qubits Management for Quantum Index Searching in a Hybrid
System [56.39703478198019]
IQuCS aims at index searching and counting in a quantum-classical hybrid system.
We implement IQuCS with Qiskit and conduct intensive experiments.
Results demonstrate that it reduces qubits consumption by up to 66.2%.
arXiv Detail & Related papers (2022-09-22T21:54:28Z) - Quantum computational intelligence for traveltime seismic inversion [0.0]
We implement an approach for traveltime seismic inversion through a near-term quantum algorithm based on gradient-free quantum circuit learning.
We demonstrate that a quantum computer with thousands of qubits, even if noisy, can solve geophysical problems.
arXiv Detail & Related papers (2022-08-11T12:36:58Z) - Data compression for quantum machine learning [2.119778346188635]
We address the problem of efficiently compressing and loading classical data for use on a quantum computer.
Our proposed methods allow both the required number of qubits and depth of the quantum circuit to be tuned.
arXiv Detail & Related papers (2022-04-24T03:03:14Z) - An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem.
The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
arXiv Detail & Related papers (2020-05-06T14:18:44Z) - Quantum noise protects quantum classifiers against adversaries [120.08771960032033]
Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies.
We show that by taking advantage of depolarisation noise in quantum circuits for classification, a robustness bound against adversaries can be derived.
This is the first quantum protocol that can be used against the most general adversaries.
arXiv Detail & Related papers (2020-03-20T17:56:14Z)
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.