The simulation of distributed quantum algorithms
- URL: http://arxiv.org/abs/2402.10745v3
- Date: Wed, 26 Feb 2025 16:03:53 GMT
- Title: The simulation of distributed quantum algorithms
- Authors: Sreraman Muralidharan,
- Abstract summary: Distributed quantum computing (DQC) provides a way to scale quantum computers using multiple quantum processing units (QPU) connected through quantum communication links.<n>We have built a distributed quantum computing simulator and used it to investigate quantum algorithms.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Distributed quantum computing (DQC) provides a way to scale quantum computers using multiple quantum processing units (QPU) connected through quantum communication links. In this paper, we have built a distributed quantum computing simulator and used the simulator to investigate quantum algorithms such as the quantum Fourier transform, quantum phase estimation, quantum amplitude estimation, and generation of probability distribution in DQC. The simulator can be used to easily generate and execute distributed quantum circuits, obtain and benchmark DQC parameters such as the fidelity of the algorithm and the number of entanglement generation steps, and use dynamic circuits in a distributed setting to improve results. We show the applicability of dynamic quantum circuits in DQC, where mid-circuit measurements, local operations, and classical communication are used in place of noisy inter-processor (non-local) quantum gates
Related papers
- 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) - DisQ: A Model of Distributed Quantum Processors [0.0]
We present Disq, as the first formal model of distributed quantum processors.
Disq is a distributed quantum programming language.
We develop a simulation relation to check the equivalence of a quantum algorithm and its distributed versions.
arXiv Detail & Related papers (2024-07-12T22:26:22Z) - 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) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - 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) - 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) - Quantum Volume for Photonic Quantum Processors [15.3862808585761]
Defining metrics for near-term quantum computing processors has been an integral part of the quantum hardware research and development efforts.
Most metrics such as randomized benchmarking and quantum volume were originally introduced for circuit-based quantum computers.
We present a framework to map physical noises and imperfections in MBQC processes to logical errors in equivalent quantum circuits.
arXiv Detail & Related papers (2022-08-24T18:05:16Z) - Arbitrary coherent distributions in a programmable quantum walk [9.037302699507409]
coherent superposition of position states in a quantum walk (QW) can be precisely engineered towards the desired distributions to meet the need of quantum information applications.
We experimentally demonstrate that the rich dynamics featured with arbitrary coherent distributions can be obtained by introducing different sets of the time- and position-dependent operations.
Our results contribute to the practical realization of quantum-walk-based quantum computation, quantum simulations and quantum information protocols.
arXiv Detail & Related papers (2022-02-19T15:56:45Z) - 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) - Distributed Quantum Computing with QMPI [11.71212583708166]
We introduce an extension of the Message Passing Interface (MPI) to enable high-performance implementations of distributed quantum algorithms.
In addition to a prototype implementation of quantum MPI, we present a performance model for distributed quantum computing, SENDQ.
arXiv Detail & Related papers (2021-05-03T18:30:43Z) - 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) - 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.