Estimating truncation effects of quantum bosonic systems using sampling algorithms
- URL: http://arxiv.org/abs/2212.08546v3
- Date: Mon, 1 Apr 2024 23:15:00 GMT
- Title: Estimating truncation effects of quantum bosonic systems using sampling algorithms
- Authors: Masanori Hanada, Junyu Liu, Enrico Rinaldi, Masaki Tezuka,
- Abstract summary: To simulate bosons on a qubit- or qudit-based quantum computer, one has to regularize the theory by truncating infinite-dimensional local Hilbert spaces to finite dimensions.
In this paper, we show that traditional sampling methods on classical devices, specifically Markov Chain Monte Carlo, can address this issue.
- Score: 5.1640944784887415
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To simulate bosons on a qubit- or qudit-based quantum computer, one has to regularize the theory by truncating infinite-dimensional local Hilbert spaces to finite dimensions. In the search for practical quantum applications, it is important to know how big the truncation errors can be. In general, it is not easy to estimate errors unless we have a good quantum computer. In this paper, we show that traditional sampling methods on classical devices, specifically Markov Chain Monte Carlo, can address this issue for a rather generic class of bosonic systems with a reasonable amount of computational resources available today. As a demonstration, we apply this idea to the scalar field theory on a two-dimensional lattice, with a size that goes beyond what is achievable using exact diagonalization methods. This method can be used to estimate the resources needed for realistic quantum simulations of bosonic theories, and also, to check the validity of the results of the corresponding quantum simulations.
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) - Power of quantum measurement in simulating unphysical operations [10.8525801756287]
We show that using quantum measurement in place of classical sampling leads to lower simulation costs for general Hermitian-preserving maps.
We demonstrate our method in two applications closely related to error mitigation and quantum machine learning.
arXiv Detail & Related papers (2023-09-18T17:39:38Z) - Learning the tensor network model of a quantum state using a few
single-qubit measurements [0.0]
The constantly increasing dimensionality of artificial quantum systems demands highly efficient methods for their characterization and benchmarking.
Here we present a constructive and numerically efficient protocol which learns a tensor network model of an unknown quantum system.
arXiv Detail & Related papers (2023-09-01T11:11:52Z) - Simulating Gaussian boson sampling quantum computers [68.8204255655161]
We briefly review recent theoretical methods to simulate experimental Gaussian boson sampling networks.
We focus mostly on methods that use phase-space representations of quantum mechanics.
A brief overview of the theory of GBS, recent experiments and other types of methods are also presented.
arXiv Detail & Related papers (2023-08-02T02:03:31Z) - Robust Dequantization of the Quantum Singular value Transformation and
Quantum Machine Learning Algorithms [0.0]
We show how many techniques from randomized linear algebra can be adapted to work under this weaker assumption.
We also apply these results to obtain a robust dequantization of many quantum machine learning algorithms.
arXiv Detail & Related papers (2023-04-11T02:09:13Z) - Sample-size-reduction of quantum states for the noisy linear problem [0.0]
We show that it is possible to reduce a quantum sample size in a quantum random access memory (QRAM) to the linearithmic order.
We achieve a shorter run-time for the noisy linear problem.
arXiv Detail & Related papers (2023-01-08T05:53:17Z) - Overcoming exponential volume scaling in quantum simulations of lattice
gauge theories [1.5675763601034223]
We present a formulation of a compact U(1) gauge theory in 2+1 dimensions free of gauge redundancies.
A naive implementation onto a quantum circuit has a gate count that scales exponentially with the volume.
We discuss how to break this exponential scaling by performing an operator redefinition that reduces the non-locality of the Hamiltonian.
arXiv Detail & Related papers (2022-12-09T01:18:46Z) - Validation tests of GBS quantum computers give evidence for quantum
advantage with a decoherent target [62.997667081978825]
We use positive-P phase-space simulations of grouped count probabilities as a fingerprint for verifying multi-mode data.
We show how one can disprove faked data, and apply this to a classical count algorithm.
arXiv Detail & Related papers (2022-11-07T12:00:45Z) - Efficient criteria of quantumness for a large system of qubits [58.720142291102135]
We discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems.
Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution.
arXiv Detail & Related papers (2021-08-30T23:50:05Z) - Bosonic field digitization for quantum computers [62.997667081978825]
We address the representation of lattice bosonic fields in a discretized field amplitude basis.
We develop methods to predict error scaling and present efficient qubit implementation strategies.
arXiv Detail & Related papers (2021-08-24T15:30:04Z) - Error mitigation and quantum-assisted simulation in the error corrected
regime [77.34726150561087]
A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations.
We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
arXiv Detail & Related papers (2021-03-12T20:58:41Z)
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.