Learning Quantum Systems
- URL: http://arxiv.org/abs/2207.00298v2
- Date: Wed, 6 Jul 2022 09:10:46 GMT
- Title: Learning Quantum Systems
- Authors: Valentin Gebhart, Raffaele Santagati, Antonio Andrea Gentile, Erik
Gauger, David Craig, Natalia Ares, Leonardo Banchi, Florian Marquardt, Luca
Pezze', and Cristian Bonato
- Abstract summary: Quantum technologies hold the promise to revolutionise our society with ground-breaking applications in secure communication, high-performance computing and ultra-precise sensing.
One of the main features in scaling up quantum technologies is that the complexity of quantum systems scales exponentially with their size.
This poses severe challenges in the efficient calibration, benchmarking and validation of quantum states and their dynamical control.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum technologies hold the promise to revolutionise our society with
ground-breaking applications in secure communication, high-performance
computing and ultra-precise sensing. One of the main features in scaling up
quantum technologies is that the complexity of quantum systems scales
exponentially with their size. This poses severe challenges in the efficient
calibration, benchmarking and validation of quantum states and their dynamical
control. While the complete simulation of large-scale quantum systems may only
be possible with a quantum computer, classical characterisation and
optimisation methods (supported by cutting edge numerical techniques) can still
play an important role.
Here, we review classical approaches to learning quantum systems, their
correlation properties, their dynamics and their interaction with the
environment. We discuss theoretical proposals and successful implementations in
different physical platforms such as spin qubits, trapped ions, photonic and
atomic systems, and superconducting circuits. This review provides a brief
background for key concepts recurring across many of these approaches, such as
the Bayesian formalism or Neural Networks, and outlines open questions.
Related papers
- Quantum Equilibrium Propagation for efficient training of quantum systems based on Onsager reciprocity [0.0]
Equilibrium propagation (EP) is a procedure that has been introduced and applied to classical energy-based models which relax to an equilibrium.
Here, we show a direct connection between EP and Onsager reciprocity and exploit this to derive a quantum version of EP.
This can be used to optimize loss functions that depend on the expectation values of observables of an arbitrary quantum system.
arXiv Detail & Related papers (2024-06-10T17:22:09Z) - Quantum consistent neural/tensor networks for photonic circuits with strongly/weakly entangled states [0.0]
We propose an approach to approximate the exact unitary evolution of closed entangled systems in a precise, efficient and quantum consistent manner.
By training the networks with a reasonably small number of examples of quantum dynamics, we enable efficient parameter estimation in larger Hilbert spaces.
arXiv Detail & Related papers (2024-06-03T09:51:25Z) - Practical limitations on robustness and scalability of quantum Internet [0.7499722271664144]
We study the limitations on the scaling and robustness of quantum Internet.
We present practical bottlenecks for secure communication, delegated computing, and resource distribution among end nodes.
For some examples of quantum networks, we present algorithms to perform different quantum network tasks of interest.
arXiv Detail & Related papers (2023-08-24T12:32:48Z) - Entanglement-Assisted Quantum Networks: Mechanics, Enabling
Technologies, Challenges, and Research Directions [66.27337498864556]
This paper presents a comprehensive survey of entanglement-assisted quantum networks.
It provides a detailed overview of the network structure, working principles, and development stages.
It also emphasizes open research directions, including architecture design, entanglement-based network issues, and standardization.
arXiv Detail & Related papers (2023-07-24T02:48:22Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - 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) - Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
Quantum generative learning models (QGLMs) may surpass their classical counterparts.
We review the current progress of QGLMs from the perspective of machine learning.
We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
arXiv Detail & Related papers (2022-06-07T07:32:57Z) - Quantum Federated Learning with Quantum Data [87.49715898878858]
Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems.
This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
arXiv Detail & Related papers (2021-05-30T12:19:27Z) - Entanglement transfer, accumulation and retrieval via quantum-walk-based
qubit-qudit dynamics [50.591267188664666]
Generation and control of quantum correlations in high-dimensional systems is a major challenge in the present landscape of quantum technologies.
We propose a protocol that is able to attain entangled states of $d$-dimensional systems through a quantum-walk-based it transfer & accumulate mechanism.
In particular, we illustrate a possible photonic implementation where the information is encoded in the orbital angular momentum and polarization degrees of freedom of single photons.
arXiv Detail & Related papers (2020-10-14T14:33:34Z) - Quantum computing with neutral atoms [0.0]
We review the main characteristics of neutral atom quantum processors from atoms / qubits to application interfaces.
We show how applications ranging from optimization challenges to simulation of quantum systems can be explored.
We give evidence of the intrinsic scalability of neutral atom quantum processors in the 100-1,000 qubits range.
arXiv Detail & Related papers (2020-06-22T15:09:01Z)
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.