Quantum computing overview: discrete vs. continuous variable models

• URL: http://arxiv.org/abs/2206.07246v1
• Date: Wed, 15 Jun 2022 02:20:14 GMT
• Title: Quantum computing overview: discrete vs. continuous variable models
• Authors: Sophie Choe
• Abstract summary: In this Near Intermediate-Scale Quantum era, there are two types of near-term quantum devices available on cloud. In implementing quantum algorithms, the CV model offers more quantum gates that are not available in the discrete variable model. CV-based photonic quantum computers provide additional flexibility of controlling the length of the output vectors of quantum circuits.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this Near Intermediate-Scale Quantum era, there are two types of near-term quantum devices available on cloud: superconducting quantum processing units (QPUs) based on the discrete variable model and linear optics (photonics) QPUs based on the continuous variable (CV) model. Quantum computation in the discrete variable model is performed in a finite dimensional quantum state space and the CV model in an infinite dimensional space. In implementing quantum algorithms, the CV model offers more quantum gates that are not available in the discrete variable model. CV-based photonic quantum computers provide additional flexibility of controlling the length of the output vectors of quantum circuits, using different methods of measurement and the notion of cutoff dimension.

Related papers

• Digital Quantum Simulation of the Holstein-Primakoff Transformation on Noisy Qubits [40.8066152850216]
  We study the digital quantum simulation of bosonic modes on a cloud-based superconducting quantum processor.<n>We examine the interplay between algorithmic and hardware-induced errors to identify optimal simulation parameters.
  arXiv  Detail & Related papers  (2026-02-19T20:14:04Z)
• Quantum Latent Diffusion Models [65.16624577812436]
  We propose a potential version of a quantum diffusion model that leverages the established idea of classical latent diffusion models. This involves using a traditional autoencoder to reduce images, followed by operations with variational circuits in the latent space. The results demonstrate an advantage in using a quantum version, as evidenced by obtaining better metrics for the images generated by the quantum version.
  arXiv  Detail & Related papers  (2025-01-19T21:24:02Z)
• Enhancing Quantum Diffusion Models with Pairwise Bell State Entanglement [35.436358464279785]
  This paper introduces a novel quantum diffusion model designed for Noisy Intermediate-Scale Quantum (NISQ) devices. By leveraging quantum entanglement and superposition, this approach advances quantum generative learning.
  arXiv  Detail & Related papers  (2024-11-24T20:14:57Z)
• Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
  We present a model for parallelizing simulation of quantum circuit executions. The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
  arXiv  Detail & Related papers  (2024-06-05T17:16:07Z)
• Continuous-variable quantum kernel method on a programmable photonic quantum processor [0.0]
  We experimentally prove that the CV quantum kernel method successfully classifies several datasets robustly even under the experimental imperfections. This demonstration sheds light on the utility of CV quantum systems for QML and should stimulate further study in other CV QML algorithms.
  arXiv  Detail & Related papers  (2024-05-02T08:33:31Z)
• Photonic Quantum Computing [0.0]
  Photonic quantum computation uses photons as the physical system for doing the quantum computation.<n>The field is largely divided between discrete-variable (DV) and continuous-variable (CV) photonic quantum computation.<n>It is possible to combine both DV and CV in a hybrid CV-DV fashion to overcome the limitations of either approach.
  arXiv  Detail & Related papers  (2024-04-04T11:09:04Z)
• Quantum Computation of Phase Transition in Interacting Scalar Quantum Field Theory [0.0]
  It has been demonstrated that the critical point of the phase transition in scalar quantum field theory can be approximated via a Gaussian Effective Potential (GEP) We perform quantum computations with various lattice sizes and obtain evidence of a transition from a symmetric to a symmetry-broken phase. We implement the ten-site case on IBM quantum hardware using the Variational Quantum Eigensolver (VQE) algorithm to minimize the GEP.
  arXiv  Detail & Related papers  (2023-03-04T14:11:37Z)
• TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework [59.07246314484875]
  TeD-Q is an open-source software framework for quantum machine learning. It seamlessly integrates classical machine learning libraries with quantum simulators. It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
  arXiv  Detail & Related papers  (2023-01-13T09:35:05Z)
• 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)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• On Quantum Circuits for Discrete Graphical Models [1.0965065178451106]
  We provide the first method that allows one to provably generate unbiased and independent samples from general discrete factor models. Our method is compatible with multi-body interactions and its success probability does not depend on the number of variables. Experiments with quantum simulation as well as actual quantum hardware show that our method can carry out sampling and parameter learning on quantum computers.
  arXiv  Detail & Related papers  (2022-06-01T11:03:51Z)
• 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)
• Quantum Phases of Matter on a 256-Atom Programmable Quantum Simulator [41.74498230885008]
  We demonstrate a programmable quantum simulator based on deterministically prepared two-dimensional arrays of neutral atoms. We benchmark the system by creating and characterizing high-fidelity antiferromagnetically ordered states. We then create and study several new quantum phases that arise from the interplay between interactions and coherent laser excitation.
  arXiv  Detail & Related papers  (2020-12-22T19:00:04Z)
• Deterministic multi-mode gates on a scalable photonic quantum computing platform [0.0]
  We show a small quantum circuit consisting of 10 single-mode gates and 2 two-mode gates on a three-mode input state. On this platform, fault-tolerant universal quantum computing is possible if the cluster state entanglement is improved.
  arXiv  Detail & Related papers  (2020-10-27T16:37:59Z)
• Quantum Statistical Complexity Measure as a Signalling of Correlation Transitions [55.41644538483948]
  We introduce a quantum version for the statistical complexity measure, in the context of quantum information theory, and use it as a signalling function of quantum order-disorder transitions. We apply our measure to two exactly solvable Hamiltonian models, namely: the $1D$-Quantum Ising Model and the Heisenberg XXZ spin-$1/2$ chain. We also compute this measure for one-qubit and two-qubit reduced states for the considered models, and analyse its behaviour across its quantum phase transitions for finite system sizes as well as in the thermodynamic limit by using Bethe ansatz.
  arXiv  Detail & Related papers  (2020-02-05T00:45:21Z)

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.