The simulation of distributed quantum algorithms

Ming‐Xing Luo

Adriano Maron, Anderson Boettge Pinheiro, Renata Reiser +1 more

This work describes a proposal for a platform focused on modeling and simulation (sequential or distributed) of quantum algorithms. More specifically, the integration between the environments VPE-qGM and VirD-GM is considered, providing a solution to the high computational cost associated to the simulation of quantum algorithms on classical computers. In this context, the algorithms are visually programmed and the details of distribution, communication and synchronization, required for the execution in distributed systems, are transparently managed by the execution environment. The validation and performance analysis consist in simulating a quantum search algorithm, obtaining results which demonstrate the performance gain of the platform compared to the sequential simulation.

Tyson Jones, Bálint Koczor, Simon C. Benjamin

Classical simulation of quantum computers is an irreplaceable step in the design of quantum algorithms. Exponential simulation costs demand the use of high-performance computing techniques, and in particular distribution, whereby the quantum state description is partitioned between a network of cooperating computers - necessary for the exact simulation of more than approximately 30 qubits. Distributed computing is notoriously difficult, requiring bespoke algorithms dissimilar to their serial counterparts with different resource considerations, and which appear to restrict the utilities of a quantum simulator. This manuscript presents a plethora of novel algorithms for distributed full-state simulation of gates, operators, noise channels and other calculations in digital quantum computers. We show how a simple, common but seemingly restrictive distribution model actually permits a rich set of advanced facilities including Pauli gadgets, many-controlled many-target general unitaries, density matrices, general decoherence channels, and partial traces. These algorithms include asymptotically, polynomially improved simulations of exotic gates, and thorough motivations for high-performance computing techniques which will be useful for even non-distributed simulators. Our results are derived in language familiar to a quantum information theory audience, and our algorithms formalised for the scientific simulation community. We have implemented all algorithms herein presented into an isolated, minimalist C++ project, hosted open-source on Github with a permissive MIT license, and extensive testing. This manuscript aims both to significantly improve the high-performance quantum simulation tools available, and offer a thorough introduction to, and derivation of, full-state simulation techniques.

Tzvetan S. Metodi, Arvin I. Faruque, Frederic T. Chong

Jincheng Zhang

This paper introduces a novel quantum-enhanced probability distribution optimization algorithm, termed Quantum Adaptive Probability Distribution Optimization Algorithm (QADPOA). Traditional methods often require extensive manual parameter adjustment, limiting the adaptability of models to complex data and patterns. QADPOA leverages quantum computing simulations to automatically adjust probability distribution parameters, thereby improving model robustness and predictive capabilities. The algorithm's core mechanism involves quantum simulation of probability distributions, followed by parameter optimization using quantum annealing techniques. This approach offers a paradigm shift in probabilistic modeling, potentially surpassing the limitations of classical algorithms. We demonstrate the algorithm's effectiveness through a series of illustrative examples, showcasing its ability to accurately model and predict complex data distributions.

Tzvetan S. Metodi, Frederic T. Chong