On nonlinear transformations in quantum computation
- URL: http://arxiv.org/abs/2112.12307v2
- Date: Wed, 15 Feb 2023 10:49:24 GMT
- Title: On nonlinear transformations in quantum computation
- Authors: Zo\"e Holmes, Nolan Coble, Andrew T. Sornborger, Yi\u{g}it
Suba\c{s}{\i}
- Abstract summary: We develop a series of basic subroutines for implementing nonlinear transformations of input quantum states.
Our algorithms are framed around the concept of a weighted state.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While quantum computers are naturally well-suited to implementing linear
operations, it is less clear how to implement nonlinear operations on quantum
computers. However, nonlinear subroutines may prove key to a range of
applications of quantum computing from solving nonlinear equations to data
processing and quantum machine learning. Here we develop a series of basic
subroutines for implementing nonlinear transformations of input quantum states.
Our algorithms are framed around the concept of a weighted state, a
mathematical entity describing the output of an operational procedure involving
both quantum circuits and classical post-processing.
Related papers
- Nonlinear quantum computing by amplified encodings [0.0]
This paper presents a novel framework for high-dimensional nonlinear quantum computation.
It exploits tensor products of amplified vector and matrix encodings.
The framework offers a new path to nonlinear quantum algorithms.
arXiv Detail & Related papers (2024-11-25T14:37:57Z) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Information-driven Nonlinear Quantum Neuron [0.0]
In this study, a hardware-efficient quantum neural network operating as an open quantum system is proposed.
We show that this dissipative model based on repeated interactions, which allows for easy parametrization of input quantum information, exhibits differentiable, non-linear activation functions.
arXiv Detail & Related papers (2023-07-18T07:12:08Z) - 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) - An Amplitude-Based Implementation of the Unit Step Function on a Quantum
Computer [0.0]
We introduce an amplitude-based implementation for approximating non-linearity in the form of the unit step function on a quantum computer.
We describe two distinct circuit types which receive their input either directly from a classical computer, or as a quantum state when embedded in a more advanced quantum algorithm.
arXiv Detail & Related papers (2022-06-07T07:14:12Z) - Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state.
In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
arXiv Detail & Related papers (2021-12-27T21:15:35Z) - Driven Gaussian quantum walks [0.0]
Quantum walks function as essential means to implement quantum simulators.
We introduce the notion of a driven Gaussian quantum walk.
We study the generation and boost of highly multimode entanglement, squeezing, and other quantum effects.
arXiv Detail & Related papers (2021-12-16T15:53:42Z) - 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) - Nonlinear transformation of complex amplitudes via quantum singular value transformation [0.8009842832476994]
This paper defines a task called nonlinear transformation of complex amplitudes on a quantum computer.
We construct a block-encoding of complex amplitudes from a state preparation unitary.
We discuss its possible applications to quantum machine learning, where complex amplitudes encoding classical or quantum data are processed.
arXiv Detail & Related papers (2021-07-22T15:47:50Z) - Designing Kerr Interactions for Quantum Information Processing via
Counterrotating Terms of Asymmetric Josephson-Junction Loops [68.8204255655161]
static cavity nonlinearities typically limit the performance of bosonic quantum error-correcting codes.
Treating the nonlinearity as a perturbation, we derive effective Hamiltonians using the Schrieffer-Wolff transformation.
Results show that a cubic interaction allows to increase the effective rates of both linear and nonlinear operations.
arXiv Detail & Related papers (2021-07-14T15:11:05Z) - 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)
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.