Efficient Floating Point Arithmetic for Quantum Computers

• Published in: IEEE access Vol. 10; pp. 72400 - 72415
• Main Authors: Seidel, Raphael, Tcholtchev, Nikolay, Bock, Sebastian, Becker, Colin Kai-Uwe, Hauswirth, Manfred
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adders; Algorithms; Arithmetic; Boolean; Boolean algebra; Coders; Computers; Data points; Data processing; Floating point arithmetic; Gates (circuits); Integers; Logic gates; Mathematical analysis; Multiplication; Polynomials; Quantum arithmetic; Quantum computers; Quantum computing; Qubit; Qubits (quantum computing); Registers
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3188251

One of the major promises of quantum computing is the realization of SIMD (single instruction - multiple data) operations using the phenomenon of superposition. Since the dimension of the state space grows exponentially with the number of qubits, we can easily reach situations where we pay less than a single quantum gate per data point for data-processing instructions, which would be rather expensive in classical computing. Formulating such instructions in terms of quantum gates, however, still remains a challenging task. Laying out the foundational functions for more advanced data-processing is therefore a subject of paramount importance for advancing the realm of quantum computing. In this paper, we introduce the formalism of encoding so called-semi-boolean polynomials. As it turns out, arithmetic <inline-formula> <tex-math notation="LaTeX">\mathbb {Z}/2^{n}\mathbb {Z} </tex-math></inline-formula> ring operations can be formulated as semi-boolean polynomial evaluations, which allows convenient generation of unsigned integer arithmetic quantum circuits. For arithmetic evaluations, the resulting algorithm has been known as Fourier-arithmetic. We extend this type of algorithm with additional features, such as ancilla-free in-place multiplication and integer coefficient polynomial evaluation. Furthermore, we introduce a tailor-made method for encoding signed integers succeeded by an encoding for arbitrary floating-point numbers. This representation of floating-point numbers and their processing can be applied to any quantum algorithm that performs unsigned modular integer arithmetic. We discuss some further performance enhancements of the semi boolean polynomial encoder and finally supply a complexity estimation. The application of our methods to a 32-bit unsigned integer multiplication demonstrated a 90% circuit depth reduction compared to carry-ripple approaches.