Abstract
Quantum algorithms to solve practical problems in quantum chemistry, materials science, and matrix inversion often involve a significant amount of arithmetic operations which act on a superposition of inputs. These have to be compiled to a set of fault-tolerant low-level operations and throughout this translation process, the compiler aims to come close to the Pareto-optimal front between the number of required qubits and the depth of the resulting circuit. In this paper, we provide quantum circuits for floating-point addition and multiplication which we find using two vastly different approaches. The first approach is to automatically generate circuits from classical Verilog implementations using synthesis tools and the second is to generate and optimize these circuits by hand. We compare our two approaches and provide evidence that floating-point arithmetic is a viable candidate for use in quantum computing, at least for typical scientific applications, where addition operations usually do not dominate the computation. All our circuits were constructed and tested using the software tools LIQ\(Ui|\rangle {}\) and RevKit.
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Haener, T., Soeken, M., Roetteler, M., Svore, K.M. (2018). Quantum Circuits for Floating-Point Arithmetic. In: Kari, J., Ulidowski, I. (eds) Reversible Computation. RC 2018. Lecture Notes in Computer Science(), vol 11106. Springer, Cham. https://doi.org/10.1007/978-3-319-99498-7_11
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DOI: https://doi.org/10.1007/978-3-319-99498-7_11
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