Quantum Simulator Based on the Paraxial Wave Equation

• URL: http://arxiv.org/abs/2308.07388v1
• Date: Mon, 14 Aug 2023 18:15:04 GMT
• Title: Quantum Simulator Based on the Paraxial Wave Equation
• Authors: Micheline B. Soley and Deniz D. Yavuz
• Abstract summary: We propose a paraxial quantum simulator that requires only widely available optical fibers or metamaterials. We show theoretically that the method accurately simulates quantum dynamics and quantum effects for an example system.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a paraxial quantum simulator that requires only widely available optical fibers or metamaterials. Such a simulator would facilitate cost-effective quantum simulation without specialized techniques. We show theoretically that the method accurately simulates quantum dynamics and quantum effects for an example system, which invites extension of the method to many-body systems using nonlinear optical elements and implementation of the paraxial quantum simulator to extend access to quantum computation and prototype quantum parity-time reversal ($\mathcal{PT}$) symmetric technologies.

Related papers

• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
  Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Distributed Quantum Simulation [13.11934294941432]
  We propose communication-efficient distributed quantum simulation protocols. Our protocols are shown to be optimal by deriving a lower bound on the quantum communication complexity. Our work paves the way for achieving a practical quantum advantage by scalable quantum simulation.
  arXiv  Detail & Related papers  (2024-11-05T07:48:40Z)
• 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)
• 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)
• Quantum Tunneling: From Theory to Error-Mitigated Quantum Simulation [49.1574468325115]
  This study presents the theoretical background and the hardware aware circuit implementation of a quantum tunneling simulation. We use error mitigation techniques (ZNE and REM) and multiprogramming of the quantum chip for solving the hardware under-utilization problem.
  arXiv  Detail & Related papers  (2024-04-10T14:27:07Z)
• Quantum-classical simulation of quantum field theory by quantum circuit learning [0.0]
  We employ quantum circuit learning to simulate quantum field theories (QFTs) We find that our predictions closely align with the results of rigorous classical calculations. This hybrid quantum-classical approach illustrates the feasibility of efficiently simulating large-scale QFTs on cutting-edge quantum devices.
  arXiv  Detail & Related papers  (2023-11-27T20:18:39Z)
• 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)
• An Introduction to Quantum Machine Learning for Engineers [36.18344598412261]
  Quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers. This book provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra.
  arXiv  Detail & Related papers  (2022-05-11T12:10:52Z)
• 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)
• Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
  We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state. In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
  arXiv  Detail & Related papers  (2021-12-27T21:15:35Z)
• Variational Quantum Anomaly Detection: Unsupervised mapping of phase diagrams on a physical quantum computer [0.0]
  We propose variational quantum anomaly detection, an unsupervised quantum machine learning algorithm to analyze quantum data from quantum simulation. The algorithm is used to extract the phase diagram of a system with no prior physical knowledge. We show that it can be used with readily accessible devices nowadays and perform the algorithm on a real quantum computer.
  arXiv  Detail & Related papers  (2021-06-15T06:54:47Z)
• Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale [57.84751206630535]
  We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework. The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators. By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
  arXiv  Detail & Related papers  (2021-04-21T13:26:42Z)
• Optimal quantum simulation of open quantum systems [1.9551668880584971]
  Digital quantum simulation on quantum systems require algorithms that can be implemented using finite quantum resources. Recent studies have demonstrated digital quantum simulation of open quantum systems on Noisy Intermediate-Scale Quantum (NISQ) devices. We develop quantum circuits for optimal simulation of Markovian and Non-Markovian open quantum systems.
  arXiv  Detail & Related papers  (2020-12-14T14:00:36Z)
• Digital quantum simulation framework for energy transport in an open quantum system [1.9551668880584971]
  Digital quantum simulations offer greater universality and flexibility over analog simulations. We give a theoretical framework for digital quantum simulation of ENAQT by introducing new quantum evolution operators. We simulate the FMO complex in the digital setting, reproducing theoretical and experimental evidence of the dynamics.
  arXiv  Detail & Related papers  (2020-06-25T02:12:11Z)

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.