Abstract
This paper presents a quantum-assisted index modulation for next-generation IoT wireless networks. The NP-hard index selection problem is first formulated by a quadratic unconstrained binary optimization (QUBO) problem consisting of constraints of feasible solutions. To minimize the number of qubits required for a quantum circuit, this formulation is then simplified by a dictionary-based approach that partially exploits a classical computer. For both formulations, the numbers of required qubits and non-zero elements in QUBO matrices are analyzed algebraically, and found to be in close agreement with the actual measurement. It is observed that the Grover adaptive search can provide the quantum speedup for the index selection problem. This promising result implies that the on-off structure of index modulation is suitable for quantum computation, and future fault-tolerant quantum computers may be useful for obtaining high-performance index activation patterns.
Highlights
I NDEX modulation (IM) represents information by switching elements on and off
It has been demonstrated that significant gains can be achieved at low transmission rates, and it is expected to play an important role in internet of things (IoT) wireless networks [2]
The quantum circuit for Grover adaptive search (GAS) requires qubits to encode an objective function, in addition to qubits for binary variables. It is shown in [22] that portfolio optimization, which is a quadratic unconstrained binary optimization (QUBO) problem, can be solved on a real quantum computer
Summary
I NDEX modulation (IM) represents information by switching elements on and off. Since Mesleh et al proposed IM for multiple-input multiple-output (MIMO) scenarios [1], a tremendous number of papers have been published trying to break the trade-off between performance and computational complexity, especially in the field of wireless communications. In [8], the index selection problem is formulated as an integer linear programming problem, which can activate each element with an equal probability It cannot be solved on a classical computer depending on the size of the search space. The quantum circuit for GAS requires qubits to encode an objective function, in addition to qubits for binary variables It is shown in [22] that portfolio optimization, which is a quadratic unconstrained binary optimization (QUBO) problem, can be solved on a real quantum computer. For more information on quantum optimization in wireless communications, a comprehensive survey can be found in [36] Against this background, the NP-hard index selection problem is newly solved by GAS that can provide the quan-.
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