Quantum Algorithms and Simulation for Parallel and Distributed Quantum Computing

Parekh, Rhea; Ricciardi, Andrea; Darwish, Ahmed; DiAdamo, Stephen

doi:10.1109/qcs54837.2021.00005

Cited by 17 publications

(4 citation statements)

References 41 publications

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“…The work [27] discusses parallel quantum computing architectures and control strategies for distributed quantum machines, noting that multiple few-qubit devices may be more technologically feasible than single many-qubit devices. In this work, we consider parallel quantum computers to be multiple simultaneous identical quantum devices governed by a classical device in order to perform parallel computation.…”

Section: A Literature Reviewmentioning

confidence: 99%

Parallel Quantum Rapidly-Exploring Random Trees

Lathrop,

Boardman,

Martínez

2024

IEEE Access

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In this paper, we present the Parallel Quantum Rapidly-Exploring Random Tree (Pq-RRT) algorithm, a parallel version of the Quantum Rapidly-Exploring Random Trees (q-RRT) algorithm [1]. Parallel Quantum RRT is a parallel quantum algorithm formulation of a sampling-based motion planner that uses Quantum Amplitude Amplification to search databases of reachable states for addition to a tree. In this work we investigate how parallel quantum devices can more efficiently search a database, as the quantum measurement process involves the collapse of the superposition to a base state, erasing probability information and therefore the ability to efficiently find multiple solutions. Pq-RRT uses a manager/parallelquantum-workers formulation, inspired by traditional parallel motion planning, to perform simultaneous quantum searches of a feasible state database. We present symbolic runtime comparisons between parallel architectures, then results regarding likelihoods of multiple parallel units finding any and all solutions contained with a shared database, with and without reachability errors, allowing efficiency predictions to be made. We offer simulations in dense obstacle environments showing efficiency, density/heatmap, and speed comparisons for Pq-RRT against q-RRT, classical RRT, and classical parallel RRT. We then present Quantum Database Annealing, a database construction strategy that uses a temperature construct to define database creation over time for balancing exploration and exploitation.

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Section: A Literature Reviewmentioning

confidence: 99%

Parallel Quantum Rapidly-Exploring Random Trees

Lathrop,

Boardman,

Martínez

2024

IEEE Access

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“…6.1. The work [173] discusses parallel quantum computing architectures and control strategies for distributed quantum machines, noting that multiple few-qubit devices may be more technologically feasible than single many-qubit devices. In this chapter, we consider parallel quantum computers to be multiple simultaneous identical quantum devices governed by a classical device in order to perform parallel computation.…”

Section: Literature Reviewmentioning

confidence: 99%

Motion Planning Algorithms for Safety and Quantum Computing Efficiency

Lathrop

2023

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I am grateful for the members, past and present, of the UCSD Multi-Agent Robotics Lab and other colleagues of the Contextual Robotics Institute for lunchtime discussions, coffee walks, and office company, especially those who put up with me both in lab and in sport. I would like to acknowledge the E-3 group at LANL for support over the summers, and in London, and I acknowledge Los Alamos National Laboratory for the financial support during my degree and the opportunity to take part in three lab internships.Lastly, I would like to express my deepest appreciation to my family: Dr. Lathrop, Dr. Lathrop, Dr. Lathrop, and John for their unwavering support and technical assistance during my Ph.D, to my friends: the UCSD and UMD triathlon teams, TE, and ZAPA for ensuring that I stay active in the great outdoors, and to my very good friend and colleague Dr. Alves and my chief of staff Calypso (feline) for their encouragement and strength.

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“…In simulation, the approach improved performance, but when there is only one quantum computer, this makes no difference. The advantage from parallel execution is achieved with multiple quantum computers working together to compute distance estimations as described in [23].…”

Section: Running Clustering On the Quantum Cloudmentioning

confidence: 99%

Practical Quantum K-Means Clustering: Performance Analysis and Applications in Energy Grid Classification

DiAdamo¹,

O'Meara²,

Cortiana³

et al. 2022

IEEE Trans. Quantum Eng.

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In this work, we aim to solve a practical use-case of unsupervised clustering which has applications in predictive maintenance in the energy operations sector using quantum computers. Using only cloud access to quantum computers, we complete a thorough performance analysis of what some current quantum computing systems are capable of for practical applications involving non-trivial midto-high dimensional datasets. We first benchmark how well distance estimation can be performed using two different metrics based on the swap-test, using angle and amplitude data embedding. Next, for the clustering performance analysis, we generate sets of synthetic data with varying cluster variance and compare simulation to physical hardware results using the two metrics. From the results of this performance analysis, we propose a general, competitive, and parallelized version of quantum k-means clustering to avoid some pitfalls discovered due to noisy hardware and apply the approach to a real energy grid clustering scenario. Using real-world German electricity grid data, we show that the new approach improves the balanced accuracy of the standard quantum k-means clustering by 67.8% with respect to the labeling of the classical algorithm.INDEX TERMS Quantum clustering, quantum distance estimation, quantum computing, cloud quantum computing.

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Quantum Algorithms and Simulation for Parallel and Distributed Quantum Computing

Cited by 17 publications

References 41 publications

Parallel Quantum Rapidly-Exploring Random Trees

Parallel Quantum Rapidly-Exploring Random Trees

Motion Planning Algorithms for Safety and Quantum Computing Efficiency

Practical Quantum K-Means Clustering: Performance Analysis and Applications in Energy Grid Classification

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