In this work, a fuzzy approach for Quantum processors using Qutrits has been proposed. Today Quantum computing has gained importance, as we need to have more computing power for transmitting large amount of data. Use of bits to transfer data, limits the amount of data and power as bits are capable of transmitting data in only two states namely 0 and 1. Qubits and Qutrits facilitate transmitting data in multiple states. This approach creates more states compared to those provided by bits. However, it would be ideal if more number of states are available for large amount of data processing. The fuzzy qubit approach attempts to enhance the number of states by using fuzzy membership functions. It takes into account the photon spin motion in generating the number of states. The Fuzzy Qutrit approach has been on Python platform using Qiskit package and it was observed that the computation time has reduced by 33%, compared to the conventional Fuzzy Qubit approach.
In this work, a fuzzy approach for Quantum processors using Qutrits has been proposed. Today Quantum computing has gained importance, as we need to have more computing power for transmitting large amount of data. Use of bits to transfer data, limits the amount of data and power as bits are capable of transmitting data in only two states namely 0 and 1. Qubits and Qutrits facilitate transmitting data in multiple states. This approach creates more states compared to those provided by bits. However, it would be ideal if more number of states are available for large amount of data processing. The fuzzy qubit approach attempts to enhance the number of states by using fuzzy membership functions. It takes into account the photon spin motion in generating the number of states. The Fuzzy Qutrit approach has been on Python platform using Qiskit package and it was observed that the computation time has reduced by 33%, compared to the conventional Fuzzy Qubit approach.
An approach for emulating quantum circuits using conventional analog hardware is presented based on the intuitive similarity between fuzzy logic and quantum superposition, as well as some geometrical analogies. This approach has the advantage of being easy to implement on dedicated hardware for parallel processing of membership functions and fuzzy inference, compared to conventional quantum computing, which requires quantum mechanical systems which are extremely sensitive to noise and difficult to extend to large scale systems. Using geometrical analogies and a suitable transformation, qubits are modeled as pairs of fuzzy membership functions evolving on the unit square and basic one qubit gates are modeled as transformations on this unit square. A fuzzy implementation of the one bit and two bit Deutsch-Jozsa algorithm is proposed. Physical implementation and advantages, such as the possibility of implementing nonlinear or non-unitary gates, as well as drawbacks of the proposed model compared to conventional quantum computing are shown.
In this paper, the formulation of Quantum Mechanics in terms of fuzzy logic and fuzzy sets is explored. A result by Pykacz, which establishes a correspondence between (quantum) logics (lattices with certain properties) and certain families of fuzzy sets, is applied to the Birkhoff–von Neumann logic, the lattice of projectors of a Hilbert space. Three cases are considered: the qubit, two qubits entangled, and a qutrit ‘nested’ inside the two entangled qubits. The membership functions of the fuzzy sets are explicitly computed and all the connectives of the fuzzy sets are interpreted as operations with these particular membership functions. In this way, a complete picture of the standard quantum logic in terms of fuzzy sets is obtained for the systems considered.
The increasing need to work in environments characterized by a large number of variables and data is driving the development of ad hoc fuzzy reasoners. Recently, the first quantum fuzzy inference engine (QFIE) has been proposed to evaluate fuzzy rules in parallel on quantum computers, whose design hardware is based on different technologies such as superconductors, photonics, trap-ion, cold atoms, and so on. Although the theoretical potential of this approach is very promising, its practical usage is still limited by the current quantum hardware, in term of dimension and noise. This paper attempts to bridge this theoretical/practical gap and pave the way for the execution of fuzzy rule-based systems on real quantum hardware by presenting a comparative study between two of the most promising quantum computing platforms, superconducting and trap-ion devices. The comparison is carried out by using QFIE for the control of a simulated particle accelerator system inspired by a real facility at the European Organization for Nuclear Research (CERN). The result of this original study allow to establish the ideal running backend for the practical use case of quantum fuzzy systems.
One of the greatest technological advancements in the last century lies in digital computer science. The idea of storing information and performing complicated calculations with the help of bits, i.e., 0 and 1. But due to a sudden surge in data, the classical computer system has been becoming weak in data processing. Quantum computers offer promising substantial speedup over classical computers for many applications. Quantum chip fabrication has made remarkable gains in recent years, with the number of qubits and fidelity growing. In general computing, a binary digit is the smallest unit of information or a bit. “In quantum computing, the term Qubit (Quantum Bit) serves the exact function of the term bit.” IBM Research released the IBMQ Experience in 2018, the first quantum computer that anyone can use and make accessible to a huge audience of different countries through cloud access. IBM also introduced the tool QISKIT (Quantum information software kit), which enables teachers, researchers, and developers to write coding and run their coding on quantum machines. It also includes different packages of quantum computing. In this paper, the author has discussed different steps to install qiskit. Mainly this paper focused on the “programming and application side of quantum computing.” Qiskit tools used in the python programming language. The “quantum circuits” are fabricated with the use of quantum gates and favorable algorithms with less execution time
Quantum computation has attracted numerous attentions and develops rapidly in the recent decades. To against the decoherence and the control errors upon the qubits, quantum error corrections are adopted. Such approaches require lots of redundant qubits, accurate measurement and timely feedback. Here we investigate a new framework of quantum computation that is associated with fuzzy processing. It will benefit significantly from three aspects: the fuzzy recognition of qubit states reduce the required gate fidelity; the fuzzy encoding encodes the information of the qubits into a distribution of probability, suppressing the fluctuations in the output of long quantum circuits; the fuzzy feedback offers a more efficient way to control the qubits when precision information of quantum states are absent. Furthermore, the fuzzy processing can be integrated into quantum error correction, eliminating the need for immediate correction operations. The proposed scheme will be fairly suitable for the solution of decision problems, which has significant applications in the optimization problems and control problems.
AbstractThe quantum computing has been proven to provide the solutions to most complex computations in the world today. Quantum computers can solve the problems with polynomial resources. Quantum information can be encoded in a number of ways, such as spin components of basic particles like photons, electrons, and polarization of photons. The quantum computer uses quantum bits which are superposition of states called as qubits, which is the classical fundamental unit of information. A quantum computer is a regular classical computer with additional ability to control the quantum bits. It is a regular computer connected to a special peripherals and a set of quantum bits simulated to build controller architecture. Two simulators are used for the quantum programming, one is Microsoft Q# and other is IBM Qiskit. Classical language Q # is used to program the quantum controller and its operations such that initializing a qubit, applying a gate on a qubit and measuring a qubit. IBM Qiskit programs simulates the functionality of quantum circuit and creates it’s drawing using a graphical interface with qubits, gates, and measurements. Qiskit provides simulators that run quantum circuits using visualization tools that help to understand the effect of our quantum circuits on the quantum states. The teleportation technique allows the quantum information to be communicated at a distance. An unbreakable encryption protocol is built for polarizing photons through the quantum channel. The combination of quantum computing and AI has the potential to drive significant advances across many sectors.