Simple Hamiltonian dynamics is a powerful quantum processing resource
- URL: http://arxiv.org/abs/2405.14245v1
- Date: Thu, 23 May 2024 07:24:50 GMT
- Title: Simple Hamiltonian dynamics is a powerful quantum processing resource
- Authors: Akitada Sakurai, Aoi Hayashi, William John Munro, Kae Nemoto,
- Abstract summary: A quadrillion dimensional Hilbert space hosted by a quantum processor with over 50 physical qubits has been expected to be powerful enough to perform computational tasks.
We show how the interplay between complexity and integrability/symmetries of the quantum system dictates the performance as quantum neural network.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A quadrillion dimensional Hilbert space hosted by a quantum processor with over 50 physical qubits has been expected to be powerful enough to perform computational tasks ranging from simulations of many-body physics to complex financial modeling. Despite few examples and demonstrations, it is still not clear how we can utilize such a large Hilbert space as a computational resource; in particular, how a simple and small quantum system could solve non-trivial computational tasks. In this paper, we show a simple Ising model capable of performing such non-trivial computational tasks in a quantum neural network model. An Ising spin chain as small as ten qubits can solve a practical image classification task with high accuracy. To evaluate the mechanism of its computation, we examine how the symmetries of the Hamiltonian would affect its computational power. We show how the interplay between complexity and integrability/symmetries of the quantum system dictates the performance as quantum neural network.
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) - Expanding Hardware-Efficiently Manipulable Hilbert Space via Hamiltonian
Embedding [9.219297088819634]
Many promising quantum applications depend on the efficient quantum simulation of an exponentially large sparse Hamiltonian.
In this paper, we propose a technique named Hamiltonian embedding.
This technique simulates a desired sparse Hamiltonian by embedding it into the evolution of a larger and more structured quantum system.
arXiv Detail & Related papers (2024-01-16T18:19:29Z) - 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) - Towards Neural Variational Monte Carlo That Scales Linearly with System
Size [67.09349921751341]
Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors.
The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems.
We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
arXiv Detail & Related papers (2022-12-21T19:00:04Z) - Noisy Quantum Kernel Machines [58.09028887465797]
An emerging class of quantum learning machines is that based on the paradigm of quantum kernels.
We study how dissipation and decoherence affect their performance.
We show that decoherence and dissipation can be seen as an implicit regularization for the quantum kernel machines.
arXiv Detail & Related papers (2022-04-26T09:52:02Z) - Recompilation-enhanced simulation of electron-phonon dynamics on IBM
Quantum computers [62.997667081978825]
We consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems.
We perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling.
Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation.
arXiv Detail & Related papers (2022-02-16T19:00:00Z) - Variational Quantum Simulation of Chemical Dynamics with Quantum
Computers [23.13347792805101]
We present variational simulations of real-space quantum dynamics suitable for implementation in Noisy Intermediate-Scale Quantum (NISQ) devices.
Motivated by the insights that most chemical dynamics occur in the low energy subspace, we propose a subspace expansion method.
arXiv Detail & Related papers (2021-10-12T16:28:52Z) - Quantum reservoir computation utilising scale-free networks [0.0]
We introduce a new reservoir computational model for pattern recognition showing a quantum advantage utilizing scale-free networks.
The simplicity in our approach illustrates the computational power in quantum complexity as well as provide new applications for such processors.
arXiv Detail & Related papers (2021-08-27T06:28:08Z) - Holographic dynamics simulations with a trapped ion quantum computer [0.0]
We demonstrate and benchmark a new scalable quantum simulation paradigm.
Using a Honeywell trapped ion quantum processor, we simulate the non-integrable dynamics of the self-dual kicked Ising model.
Results suggest that quantum tensor network methods, together with state-of-the-art quantum processor capabilities, enable a viable path to practical quantum advantage in the near term.
arXiv Detail & Related papers (2021-05-19T18:00:02Z) - Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum
dynamics [0.0]
We present a scalable algorithm based on neural networks for Hamiltonian tomography in out-of-equilibrium quantum systems.
Specifically, we show that our algorithm is able to reconstruct the Hamiltonian of an arbitrary size quasi-1D bosonic system.
arXiv Detail & Related papers (2021-03-01T19:00:15Z) - Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor.
We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
arXiv Detail & Related papers (2021-01-21T22:18:49Z)
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.