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Journal of problem in computer science and information technologies

IMPLEMENTATION OF QUANTUM ARITHMETIC OPERATIONS WITH INTEGER CHARACTERS USING THE QUANTUM FOURIER TRANSFORM

Aksultan Mukhanbet, Moldir Nakibayeva, Beimbet Daribayev

Published Apr 3, 2023• 2 citations
Publisher

Abstract

It has been proven that a quantum computer is superior to an electronic com puter in solving some NP problems. Based on quantum operations, this article proposes a new quantum sum and quantum multiplication, and then the floating point quantum multiplier and quantum sum are created on the basis of fixed point number operations. These studies lay the foundation for the quantum implementation of digital filters. This article provides a new way to calculate the summator on a quantum computer. This method uses the quantum Fourier transform (QFT) and reduces the number of qubits needed to be added by eliminating the need to temporarily transfer bits. This approach also allows you to add a classical number to a quantum superposition without encoding a classical number in a quantum register. This method also allows for mass parallelization during its execution. Adding and multiplying capabilities based on QFT are improved with some changes. The proposed operations are compared with the operations of close quantum arithmetic.

DOI
10.26577/jpcsit.2023.v1.i1.09

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Since Shor's proposition of the method for factoring products of prime numbers using quantum computing, there has been a quest to implement efficient quantum arithmetic algorithms. These algorithms are capable of applying arithmetic operations simultaneously on large sets of values using quantum parallelism. Draper proposed an addition algorithm based on the quantum Fourier transform whose operands are two quantum registers, which I refer to as register-by-register addition. However, for cases where there is the need to be added a constant value to a target register, Draper's algorithm is more complex than necessary in terms of number of operations and number of qubits used. In this paper, I present a more efficient addition algorithm than Draper's for cases where there needs to be added just a constant to a target register.
This article demonstrates a quantum calculator by experimentally simulating and performing basic arithmetic operations, namely addition, subtraction, multiplication, division on real quantum devices using the IBM Quantum Experience (IBM QE) platform. Two addition algorithms are considered, the classical version, using the Toffoli gate, and using the quantum Fourier transform (QFT). In particular summation with QFT saves memory and time. The task of the work is to build a quantum circuit that performs arithmetic operations between the binary representation of two integers. We then propose a quantum algorithm for converting decimal to binary. This method also allows for massive parallelization during its execution. The addition, subtraction, multiplication, and division functions based on QFT have been improved with some changes. The proposed operations are compared with the closest quantum arithmetic operations. Therefore, we can implement a practical quantum calculator by performing operations on a real quantum computer. The circuits are defined as a kind of quantum adder using a quantum Fourier transform, implemented in Qiskit and demonstrated on one or two copies.
The quantum Fourier transform (QFT) brings efficiency in many respects, especially usage of resource, for most operations on quantum computers. In this study, the existing QFT-based and non-QFT-based quantum arithmetic operations are examined. The capabilities of QFT-based addition and multiplication are improved with some modifications. The proposed operations are compared with the nearest quantum arithmetic operations. Furthermore, novel QFT-based subtraction, division and exponentiation operations are presented. The proposed arithmetic operations can perform nonmodular operations on all signed numbers without any limitation by using less resources. In addition, novel quantum circuits of two’s complement, absolute value and comparison operations are also presented by using the proposed QFT-based addition and subtraction operations.
Quantum circuits which perform integer arithmetic could potentially outperform their classical counterparts. In this paper, a quantum circuit is considered which performs a specific computational pattern on classically represented integers to accelerate the computation. Such a hybrid circuit could be embedded in a conventional computer architecture as a quantum device or accelerator. In particular, a quantum multiply-add circuit (QMAC) using a Quantum Fourier Transform (QFT) is proposed which can perform the calculation on conventional integers faster than its conventional counterpart. Whereas classically applying a multiply-adder (MAC) nnn times to kkk bit integers would require O(nlog⁡k)O(n \log k)O(nlogk) parallel steps, the hybrid QMAC needs only O(n+k)O(n + k)O(n+k) steps for the exact result and O(n+log⁡k)O(n + \log k)O(n+logk) steps for an approximate result.
There are several possible ways to encode random variables in a quantum state. The basis encoding of bit strings has paramount importance because it allows to load the values of a random variable through the superposition of corresponding basis states, and to then exploit quantum parallelism in processing algorithms. The basis encoding offers a natural way to represent an unsigned integer random variable, and extends to signed integers, as well as to fixed-point and floating-point variables. Each quantum representation of fractional numbers, however, involves a trade-off between accuracy and depth of manipulation circuits. Here, an efficient hybrid quantum-classical representation of quantum floating points is introduced. It combines a quantum register containing the values, with a classical register storing global information about the variable, namely the range and approximation tolerances. The sum and product operations are defined, in such a way as to ensure they are performed without overflow. By taking advantage of the stored classical information, the precision degradation that occurs due to rounding after repeated data manipulations, can be significantly reduced compared to known strategies. Ad hoc examples show up to around 90%90\%90% reduction in approximation, compared to previous techniques, after repeated additions. The method finds application in many algorithms of practical relevance and constitutes a significant advance in the design of arithmetic circuits with low depth and high accuracy.