Quantum Compiling

Maronese, Marco; Moro, Lorenzo; Rocutto, Lorenzo; Prati, Enrico

doi:10.48550/arxiv.2112.00187

Cited by 3 publications

(2 citation statements)

References 64 publications

(87 reference statements)

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“…The number of runs was doubled each time that N increased by a unit. We found no significant differences in the slope between D-Wave Advantage and the D-Wave 2000Q, but D-Wave Advantage proved slightly faster, probably because its greater connectivity allows for shorter physical qubit chains [63].…”

Section: ) Semiprime Factorization Problem (Fp)mentioning

confidence: 54%

Assessing the Effectiveness of Non-Turing Computing Paradigms

Rocutto,

Maronese,

Traversa

et al. 2023

IEEE Access

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In recent years the technological limits inherently present in the classical Turing paradigm of computation have sparked the development of innovative solutions based on quantum devices or analog-digital mixed approaches often based on the time evolution of differential equations. Such promising machinery require accurate analysis to understand if and how they will be able to perform better than classical approaches in solving hard optimization problems. Here we challenge two machines representative of the quantum annealing and differential equations approaches, namely D-Wave and Memcomputing by devising a benchmark of three well known hard optimization problems from the realms of number theory, optimal transport and optimal scheduling. We introduce the Mean First Solution Time, a novel metric for accurately comparing performances, and take as baseline the classical Gurobi solver. We show that performances of both solvers are heavily dependent on the selected set of internal parameters. Results shed lights on the advantages and current limits of each paradigm and give a perspective on possible future developments.

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Section: ) Semiprime Factorization Problem (Fp)mentioning

confidence: 54%

Assessing the Effectiveness of Non-Turing Computing Paradigms

Rocutto,

Maronese,

Traversa

et al. 2023

IEEE Access

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“…Here, we consider the compilation problem [9], which is generally tough to solve, even on single processor, where an NP-hardness proof is available [10]. An ever growing literature is available, with a variety of proposals for local computing [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24] and for distributed computing [25], [26], [27], [9], [28], [29], [30], [31], [32], [33], [34].…”

Section: Introductionmentioning

confidence: 99%

Optimized compiler for Distributed Quantum Computing

Cuomo¹,

Caleffi²,

Krsulich³

et al. 2021

Preprint

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Practical distributed quantum computing requires the development of efficient compilers, able to make quantum circuits compatible with some given hardware constraints. This problem is known to be tough, even for local computing. Here, we address it on distributed architectures. As generally assumed in this scenario, telegates represent the fundamental remote (interprocessor) operations. Each telegate consists of several tasks: i) entanglement generation and distribution, ii) local operations, and iii) classical communications. Entanglement generations and distribution is an expensive resource, as it is time-consuming and fault-prone. To mitigate its impact, we model an optimization problem that combines running-time minimization with the usage of that resource. Specifically, we provide a parametric ILP formulation, where the parameter denotes a time horizon (or time availability); the objective function count the number of used resources. To minimize the time, a binary search solves the subject ILP by iterating over the parameter. Ultimately, to enhance the solution space, we extend the formulation, by introducing a predicate that manipulates the circuit given in input and parallelizes telegates' tasks.

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Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning

Preti,

Schilling,

Jerbi

et al. 2024

Quantum

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Shortening quantum circuits is crucial to reducing the destructive effect of environmental decoherence and enabling useful algorithms. Here, we demonstrate an improvement in such compilation tasks via a combination of using hybrid discrete-continuous optimization across a continuous gate set, and architecture-tailored implementation. The continuous parameters are discovered with a gradient-based optimization algorithm, while in tandem the optimal gate orderings are learned via a deep reinforcement learning algorithm, based on projective simulation. To test this approach, we introduce a framework to simulate collective gates in trapped-ion systems efficiently on a classical device. The algorithm proves able to significantly reduce the size of relevant quantum circuits for trapped-ion computing. Furthermore, we show that our framework can also be applied to an experimental setup whose goal is to reproduce an unknown unitary process.

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Quantum Compiling

Cited by 3 publications

References 64 publications

Assessing the Effectiveness of Non-Turing Computing Paradigms

Assessing the Effectiveness of Non-Turing Computing Paradigms

Optimized compiler for Distributed Quantum Computing

Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning

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