13 research outputs found
QPCF: higher order languages and quantum circuits
qPCF is a paradigmatic quantum programming language that ex- tends PCF with
quantum circuits and a quantum co-processor. Quantum circuits are treated as
classical data that can be duplicated and manipulated in flexible ways by means
of a dependent type system. The co-processor is essentially a standard QRAM
device, albeit we avoid to store permanently quantum states in between two
co-processor's calls. Despite its quantum features, qPCF retains the classic
programming approach of PCF. We introduce qPCF syntax, typing rules, and its
operational semantics. We prove fundamental properties of the system, such as
Preservation and Progress Theorems. Moreover, we provide some higher-order
examples of circuit encoding
Rule switching mechanisms in the Game of Life with synchronous and asynchronous updating policy
The emergence of complex structures in the systems governed by a simple set
of rules is among the most fascinating aspects of Nature. The particularly
powerful and versatile model suitable for investigating this phenomenon is
provided by cellular automata, with the Game of Life being one of the most
prominent examples. However, this simplified model can be too limiting in
providing a tool for modelling real systems. To address this, we introduce and
study an extended version of the Game of Life, with the dynamical process
governing the rule selection at each step. We show that the introduced
modification significantly alters the behaviour of the game. We also
demonstrate that the choice of the synchronization policy can be used to
control the trade-off between the stability and the growth in the system.Comment: 14 pages, 5+1 figures, code available at
https://doi.org/10.5281/zenodo.809960
Symbolic quantum programming for supporting applications of quantum computing technologies
The goal of this paper is to deliver the overview of the current state of the
art, to provide experience report on developing quantum software tools, and to
outline the perspective for developing quantum programming tools supporting
symbolic programming for the needs of quantum computing technologies. The main
focus of this paper is on quantum computing technologies, as they can in the
most direct way benefit from developing tools enabling the symbolic
manipulation of quantum circuits and providing software tools for creating,
optimizing, and testing quantum programs. We deliver a short survey of the most
popular approaches in the field of quantum software development and we aim at
pointing their strengths and weaknesses. This helps to formulate a list of
desirable characteristics which should be included in quantum computing
frameworks. Next, we describe a software architecture and its preliminary
implementation supporting the development of quantum programs using symbolic
approach, encouraging the functional programming paradigm, and, at the same,
time enabling the integration with high-performance and cloud computing. The
described software consists of several packages developed to address different
needs, but nevertheless sharing common design concepts. We also outline how the
presented approach could be used in tasks in quantum software engineering,
namely quantum software testing and quantum circuit construction.Comment: 14 pages, contribution to QP2023 Workshop, Programming'23, Tokyo, JP,
March 13-17, 202
A quantum-inspired version of the nearest mean classifier
We introduce a framework suitable for describing standard classification problems using the mathematical language of quantum states. In particular, we provide a one-to-one correspondence between real objects and pure density operators. This correspondence enables us: (1) to represent the nearest mean classifier (NMC) in terms of quantum objects, (2) to introduce a quantum-inspired version of the NMC called quantum classifier (QC). By comparing the QC with the NMC on different datasets, we show how the first classifier is able to provide additional information that can be beneficial on a classical computer with respect to the second classifier
Quantum Genetic Algorithms for Computer Scientists
Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data) has led to a new class of GAs known as âQuantum Genetic Algorithmsâ (QGAs). In this review, we present a discussion, future potential, pros and cons of this new class of GAs. The review will be oriented towards computer scientists interested in QGAs âavoidingâ the possible difficulties of quantum-mechanical phenomena