Quantum Circuit Transformation: A Monte Carlo Tree Search Framework

In Noisy Intermediate-Scale Quantum (NISQ) era, quantum processing units (QPUs) suffer from, among others, highly limited connectivity between physical qubits. To make a quantum circuit effectively executable, a circuit transformation process is necessary to transform it, with overhead cost the smaller the better, into a functionally equivalent one so that the connectivity constraints imposed by the QPU are satisfied. While several algorithms have been proposed for this goal, the overhead costs are often very high, which degenerates the fidelity of the obtained circuits sharply. One major reason for this lies in that, due to the high branching factor and vast search space, almost all these algorithms only search very shallowly and thus, very often, only (at most) locally optimal solutions can be reached. In this paper, we propose a Monte Carlo Tree Search (MCTS) framework to tackle the circuit transformation problem, which enables the search process to go much deeper. The general framework supports implementations aiming to reduce either the size or depth of the output circuit through introducing SWAP or remote CNOT gates. The algorithms, called MCTS-Size and MCTS-Depth, are polynomial in all relevant parameters. Empirical results on extensive realistic circuits and IBM Q Tokyo show that the MCTS-based algorithms can reduce the size (depth, resp.) overhead by, on average, 66% (84%, resp.) when compared with t|ket PLX − right 〉., an industrial level compiler. </jats:p

Optimizing quantum circuit layouts

Un dels problemes amb els quals s'enfronta la computació quàntica és el de l'optimització de la compilació d'un circuit quàntic. El procés de compilació inclou bàsicament dues etapes: síntesi del circuit a executar en termes de les portes quàntiques suportades pel processador, i adaptació del circuit a executar a les limitacions de connectivitat imposades pel processador. En aquest treball, he abordat el segon d'aquests problemes, conegut amb el nom de Quantum Circuit Layout (QCL). Per a la seva resolució, he intentat usar tècniques de Reinforcement Learning (RL), que requereixen modelitzar prèviament el problema en termes d'un Markov Decision Process (MDP). En concret, descric dos MDP's finits la solució dels quals proporciona una solució a una part del problema del QCL. El problema principal és dissenyar un mètode que permeti efectivament resoldre aquests MDP's, ni que sigui de manera aproximada. En el treball es discuteixen dues aproximacions al problema. La primera d'elles utilitza una variant de l'algoritme usat per AlphaZero, dissenyat amb l'objectiu d'entrenar a una màquina per tal que aprengui a jugar als jocs d'Escacs, Shogi i Go. La segona utilitza una aproximació més estàndard coneguda com a Deep Q-Learning (DQL).One of the challenges in quantum computing is the problem of optimizing quantum circuit compilation. The compilation process involves two main stages: synthesizing the circuit to be executed in terms of the quantum gates supported by the processor, and adapting the circuit to the connectivity limitations imposed by the processor. In this work, I have addressed the second of these problems, known as Quantum Circuit Layout (QCL). To tackle this problem, I have attempted to use Reiforcement Learning (RL) techniques, which require modeling the problem as a Markov Decision Process (MDP). Specifically, I describe two finite MDPs whose solution provides a solution to a part of the QCL problem. The main problem is to design a method that effectively solves these MDPs, even if it is only an approximate solution. In the thesis two approaches to the problem are discussed. The first one uses a variant of the algorithm used in AlphaZero, designed to train a machine to learn how to play Chess, Shogi, and Go. The second approach uses a more standard approximation known as Deep Q-Learning (DQL)

Machine Learning Optimization of Quantum Circuit Layouts

The quantum circuit layout problem is to map a quantum circuit to a quantum computing device, such that the constraints of the device are satisfied. The optimality of a layout method is expressed, in our case, by the depth of the resulting circuits. We introduce QXX, a novel search-based layout method, which includes a configurable Gaussian function used to: \emph{i)} estimate the depth of the generated circuits; \emph{ii)} determine the circuit region that influences most the depth. We optimize the parameters of the QXX model using an improved version of random search (weighted random search). To speed up the parameter optimization, we train and deploy QXX-MLP, an MLP neural network which can predict the depth of the circuit layouts generated by QXX. We experimentally compare the two approaches (QXX and QXX-MLP) with the baseline: exponential time exhaustive search optimization. According to our results: 1) QXX is on par with state-of-the-art layout methods, 2) the Gaussian function is a fast and accurate optimality estimator. We present empiric evidence for the feasibility of learning the layout method using approximation.Comment: comments are welcom

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum