We want in this article to show the usefulness of Quantum Turing Machine (QTM) in a high-level didactic context as well as in theoretical studies. We use QTM to show its equivalence with quantum circuit model for Deutsch and Deutsch-Jozsa algorithms. Further we introduce a strategy of translation from Quantum Circuit to Quantum Turing models by these examples. Moreover we illustrate some features of Quantum Computing such as superposition from a QTM point of view and starting with few simple examples very known in Quantum Circuit form.
We want in this article to show the usefulness of Quantum Turing Machine (QTM) in a high-level didactic context as well as in theoretical studies. We use QTM to show its equivalence with quantum circuit model for Deutsch and Deutsch-Jozsa algorithms. Further we introduce a strategy of translation from Quantum Circuit to Quantum Turing models by these examples. Moreover we illustrate some features of Quantum Computing such as superposition from a QTM point of view and starting with few simple examples very known in Quantum Circuit form.
Contrary to the classical case, the relation between quantum programming languages and quantum Turing Machines (QTM) has not been fully investigated. In particular, there are features of QTMs that have not been exploited, a notable example being the intrinsic infinite nature of any quantum computation. In this paper, we propose a definition of QTM, which extends and unifies the notions of Deutsch and Bernstein & Vazirani. In particular, we allow both arbitrary quantum input, and meaningful superpositions of computations, where some of them are “terminated” with an “output”, while others are not. For some infinite computations an “output” is obtained as a limit of finite portions of the computation. We propose a natural and robust observation protocol for our QTMs, which does not modify the probability of the possible outcomes of the machines. Finally, we use QTMs to define a class of quantum computable functions—any such function is a mapping from a general quantum state to a probability distribution of natural numbers. We expect that our class of functions, when restricted to classical input-output, will not be different from the set of the recursive functions.
In this article, we underline and compare different quantum and deterministic models of computation by means of a probabilistic point of view. The main models used for this are deterministic, probabilistic and quantum Turing machines (DTM, PTM and QTM, respectively). Then we suggest that extending PTM with only part of quantum features is not useful to implement real quantum supremacy if we don’t incorporate them fully. This is done reviewing some notorious quantum physical experiments and quantum algorithms, showing that generally PTM does not fit for them, also if in some particular cases it fits.Here we compare probabilistic and quantum models of computation to underline some common features and differences.
The quantum Turing machine (QTM) has been introduced by Deutsch as an abstract model of quantum computation. The transition function of a QTM is linear, and has to be unitary to be a well-formed QTM. This well-formedness condition ensures that the evolution of the machine does not violate the postulates of quantum mechanics. However, we claim in this paper that the well-formedness condition is too strong and we introduce a weaker condition, leading to a larger class of Turing machines called Observable Quantum Turing Machines (OQTMs). We prove that the evolution of such OQTM does not violate the postulates of quantum mechanics while offering a more general abstract model for quantum computing. This novel abstract model unifies classical and quantum computations, since every well-formed QTM and every deterministic TM are OQTMs, whereas a deterministic TM has to be reversible to be a well-formed QTM. In this paper we introduce the fundamentals of OQTM like a well-observed lemma and a completion lemma. The introduction of such an abstract machine allowing classical and quantum computations is motivated by the emergence of models of quantum computation like the one-way model. More generally, the OQTM aims to be an abstract framework for the pragmatic paradigm of quantum computing: quantum data, classical control'. Furthermore, this model allows a formal and rigorous treatment of problems requiring classical interactions, like the halting of QTM. Finally, it opens new perspectives for the construction of a universal QTM.
In this article, we present an introduction to quantum computing (QC) tailored for computing professionals such as programmers, machine learning engineers, and data scientists. Our approach abstracts away the physics underlying QC, which can be challenging to grasp, and frames it as a model of computation similar to, for instance, Turing machines. This helps readers grasp the fundamental principles of QC from a purely logical perspective. We begin by defining quantum states and qubits, establishing their mathematical representation and role in computation. We introduce fundamental concepts such as basis states, quantum gates, and tensor products, illustrating how these form the building blocks of quantum computation. Then we present the Deutsch-Josza algorithm, one of the simplest quantum algorithms that demonstrate how quantum computers can outperform classical computers. Finally, we provide guidance for further study, recommending resources for those interested in exploring quantum algorithms, implementations, and industry applications.
This chapter provides an introduction to the principles of quantum information processing, establishing the theoretical foundation for quantum machine learning. It covers the representation of quantum states using qubits, the mathematical framework of quantum mechanics, and the role of superposition, entanglement, and measurement. Key models such as quantum circuits and quantum gates are introduced, along with illustrative algorithms including Deutsch–Jozsa and Grover’s search. These examples demonstrate how quantum mechanics enables computational speedups and highlights the potential advantages of quantum computing in learning and optimization tasks.