Quantum optical neural networks with programmable nonlinearities
- URL: http://arxiv.org/abs/2410.07868v1
- Date: Thu, 10 Oct 2024 12:37:05 GMT
- Title: Quantum optical neural networks with programmable nonlinearities
- Authors: E. A. Chernykh, M. Yu. Saygin, G. I. Struchalin, S. P. Kulik, S. S. Straupe,
- Abstract summary: We show that using programmable nonlinearities, rather than linear optics, offers a more efficient method for constructing quantum optical circuits.
This approach significantly reduces the number of adjustable parameters needed to achieve high-fidelity operation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Parametrized quantum circuits are essential components of variational quantum algorithms. Until now, optical implementations of these circuits have relied solely on adjustable linear optical units. In this study, we demonstrate that using programmable nonlinearities, rather than linear optics, offers a more efficient method for constructing quantum optical circuits -- especially quantum neural networks. This approach significantly reduces the number of adjustable parameters needed to achieve high-fidelity operation. Specifically, we explored a quantum optical neural network (QONN) architecture composed of meshes of two-mode interferometers programmable by adjustable Kerr-like nonlinearities. We assessed the capabilities of our quantum optical neural network architecture and compared its performance to previously studied architectures that use multimode linear optics units. Additionally, we suggest future research directions for improving programmable quantum optical circuits.
Related papers
- 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) - Characterization of multi-mode linear optical networks [0.0]
We formulate efficient procedures for the characterization of optical circuits in the presence of imperfections.
We show the viability of this approach in an experimentally relevant scenario, defined by a tunable integrated photonic circuit.
Our findings can find application in a wide range of optical setups, based both on bulk and integrated configurations.
arXiv Detail & Related papers (2023-04-13T13:09:14Z) - Realization of a quantum neural network using repeat-until-success
circuits in a superconducting quantum processor [0.0]
In this paper, we use repeat-until-success circuits enabled by real-time control-flow feedback to realize quantum neurons with non-linear activation functions.
As an example, we construct a minimal feedforward quantum neural network capable of learning all 2-to-1-bit Boolean functions.
This model is shown to perform non-linear classification and effectively learns from multiple copies of a single training state consisting of the maximal superposition of all inputs.
arXiv Detail & Related papers (2022-12-21T03:26:32Z) - Quantum Neural Architecture Search with Quantum Circuits Metric and
Bayesian Optimization [2.20200533591633]
We propose a new quantum gates distance that characterizes the gates' action over every quantum state.
Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems.
arXiv Detail & Related papers (2022-06-28T16:23:24Z) - Tunable photon-mediated interactions between spin-1 systems [68.8204255655161]
We show how to harness multi-level emitters with several optical transitions to engineer photon-mediated interactions between effective spin-1 systems.
Our results expand the quantum simulation toolbox available in cavity QED and quantum nanophotonic setups.
arXiv Detail & Related papers (2022-06-03T14:52:34Z) - Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most.
We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
arXiv Detail & Related papers (2022-04-12T19:39:31Z) - Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions.
Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
arXiv Detail & Related papers (2021-06-24T20:16:02Z) - Optimized Low-Depth Quantum Circuits for Molecular Electronic Structure
using a Separable Pair Approximation [0.0]
We present a classically solvable model that leads to optimized low-depth quantum circuits leveraging separable pair approximations.
The obtained circuits are well suited as a baseline circuit for emerging quantum hardware and can, in the long term, provide significantly improved initial states for quantum algorithms.
arXiv Detail & Related papers (2021-05-09T05:10:59Z) - Experimental Quantum Generative Adversarial Networks for Image
Generation [93.06926114985761]
We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor.
Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
arXiv Detail & Related papers (2020-10-13T06:57:17Z) - Rapid characterisation of linear-optical networks via PhaseLift [51.03305009278831]
Integrated photonics offers great phase-stability and can rely on the large scale manufacturability provided by the semiconductor industry.
New devices, based on such optical circuits, hold the promise of faster and energy-efficient computations in machine learning applications.
We present a novel technique to reconstruct the transfer matrix of linear optical networks.
arXiv Detail & Related papers (2020-10-01T16:04:22Z)
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.