Calculating spin correlations with a quantum computer
- URL: http://arxiv.org/abs/2206.14584v1
- Date: Sun, 26 Jun 2022 14:03:58 GMT
- Title: Calculating spin correlations with a quantum computer
- Authors: Jed Brody and Gavin Guzman
- Abstract summary: This exercise is ideal for remote learning and generates data with real quantum mechanical systems.
Students learn a wide variety of skills, including calculation of multipartite spin correlation functions.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We calculate spin correlation functions using IBM quantum processors,
accessed online. We demonstrate the rotational invariance of the singlet state,
interesting properties of the triplet states, and surprising features of a
state of three entangled qubits. This exercise is ideal for remote learning and
generates data with real quantum mechanical systems that are impractical to
investigate in the local laboratory. Students learn a wide variety of skills,
including calculation of multipartite spin correlation functions, design and
analysis of quantum circuits, and remote measurement with real quantum
processors.
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) - 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 correlation generation capability of experimental processes [5.552315676636436]
EPR steering and Bell nonlocality illustrate two different kinds of correlations predicted by quantum mechanics.
We show that the capability of an experimental process to create quantum correlations can be quantified.
We demonstrate this utility by examining the experimental capability of creating quantum correlations with the controlled-phase operations on the IBM Quantum Experience and Amazon Braket Rigetti superconducting quantum computers.
arXiv Detail & Related papers (2023-04-30T02:22:56Z) - Complete characterization of quantum correlations by randomized
measurements [0.832184180529969]
We provide a method to measure any locally invariant property of quantum states using locally randomized measurements.
We implement these methods experimentally using pairs of entangled photons, characterizing their usefulness for quantum teleportation.
Our results can be applied to various quantum computing platforms, allowing simple analysis of correlations between arbitrary distant qubits.
arXiv Detail & Related papers (2022-12-15T15:22:28Z) - Quantum circuits for the preparation of spin eigenfunctions on quantum
computers [63.52264764099532]
Hamiltonian symmetries are an important instrument to classify relevant many-particle wavefunctions.
This work presents quantum circuits for the exact and approximate preparation of total spin eigenfunctions on quantum computers.
arXiv Detail & Related papers (2022-02-19T00:21:46Z) - 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) - On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
We devise three effective QAE-based learning protocols to address three classically computational hard learning problems.
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - 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) - Correlations for computation and computation for correlations [0.0]
We connect quantum correlations with computation using 4-photon Greenberger-Horne-Zeilinger (GHZ) states.
We show how the generated states can be used to specifically compute Boolean functions.
The connection between quantum correlation and computability shown here has applications in quantum technologies.
arXiv Detail & Related papers (2020-05-04T18:33:13Z)
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.