Quantum Speedup for Index Modulation

<div>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. </div><div>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.<br></div><div><br></div><div>(c) IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.<br><br>Naoki Ishikawa, "Quantum speedup for index modulation," vol. 9, pp. 111114-111124, Aug. 2021.</div><div>DOI: 10.1109/ACCESS.2021.3103207<br></div>

Scanner data in inflation measurement: From raw data to price indices

Scanner data offer new opportunities for CPI or HICP calculation. They can be obtained from a wide variety of retailers (supermarkets, home electronics, Internet shops, etc.) and provide information at the level of the barcode. One of advantages of using scanner data is the fact that they contain complete transaction information, i.e. prices and quantities for every sold item. After clearing data and unifying product names, products should be carefully classified (e.g. into COICOP 5 or below), matched, filtered and aggregated. One of new challenges connected with scanner data is the appropriate choice of the index formula. In this article we present a proposal for the implementation of individual stages of handling scanner data. We also point out potential problems during scanner data processing and their solutions. We compare a large number of price index methods based on real scanner data sets and we verify their sensitivity on adopted data filtering and aggregating methods. One of the aims is also to compare calculations of multilateral indices in terms of how time-consuming they are. Finally, the paper investigates distances between these indices and the theoretical, expected value of the price share when prices are log-normally distributed. It is a new approach to providing an additional criterion in the price index selection.

A mapreduce relational-database index-selection tool

A mapreduce relational-database index-selection tool

A mapreduce relational-database index-selection tool

A mapreduce relational-database index-selection tool

Quantum Speedup for Index Modulation

This letter 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 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. It is observed that the Grover adaptive search can provide the quantum speedup for the index selection problem. This promising result implies that a future fault-tolerant quantum computer may be useful for solving optimization problems encountered in wireless communications.

Quantum Speedup for Index Modulation

This letter 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 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. It is observed that the Grover adaptive search can provide the quantum speedup for the index selection problem. This promising result implies that a future fault-tolerant quantum computer may be useful for solving optimization problems encountered in wireless communications.