# Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches

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## Abstract

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## 1. Introduction

- Random forest and AdaBoost classifiers that use mobile device sensor data to classify the environment [2];
- Support vector machine (SVM) and deep learning (DL) techniques used in combination with a hybrid semi-supervised learning system to identify the indoor–outdoor environment using large and real collected 3rd Generation Partnership Project (3GPP) signal measurements [3];
- Deep learning, based on radio signals, time-related features and mobility indicators for a more complex environment classification, with multiple environments in [4].
- Ensemble learning schemes [5];
- Semi-supervised learning algorithm [6];
- Ensemble model based on stacking and filtering the detection results with a hidden Markov model [7].

- Improvements in run time: obtaining faster results;
- Learning capacity improvements: increase in the capacity of associative or content-addressable memories;
- Learning efficiency improvements: less training information or simpler models needed to produce the same results or more complex relations can be learned from the same data.

- They assume preloaded databases in quantum states, for example, using quantum RAM (QRAM);
- They assume data to be ‘relatively uniform’, meaning no big differences in value;
- They produce a quantum state as output, meaning this has to be translated efficiently to a meaningful result.

## 2. Quantum Computing

#### 2.1. Gate-Based Quantum Computing

#### 2.2. Annealing-Based Quantum Computing

## 3. Generating Data

- ${P}_{R}$: Received power level,
- ${P}_{T}$: Transmitted power level,
- $L\left(d\right)$: Path loss/Attenuation,
- $\beta $: Slow fading/Shadowing,
- $\Gamma $: Fast fading/Multipath effects.

## 4. Quantum Machine Learning Approaches

#### 4.1. Quantum Variational Classifier

#### 4.2. Quantum Distance-Based Classifier

#### 4.3. Quantum Annealing-Based SVM

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 2.**Amplitude encoding: two-qubit circuit for data encoding of two normalised two-dimensional data points.

**Figure 3.**Original generated data points. Red dots are the outdoor measurements, blue dots are the indoor measurements.

**Figure 4.**Prepared artificial data points. Red dots are the outdoor measurements, blue dots are the indoor measurements.

**Figure 5.**Padded and normalised data. Red dots are the outdoor measurements, blue dots are the indoor measurements.

**Figure 11.**Resulting assignments of the distance-based classifier with 256 training data points and 44 validation points.

N | Model | Cost Function | F1 | Acc | Pr | Rc |
---|---|---|---|---|---|---|

225 | 2-qubit | 0.4167 | 1 | 1 | 1 | 1 |

4-qubit | 0.3329 | 1 | 1 | 1 | 1 |

**Table 2.**Overview of performance indicators for the validation data set for the distance-based classification trained on 256 data points.

N | F1 | Acc | Pr | Rc |
---|---|---|---|---|

256 | 1 | 1 | 1 | 1 |

N | Solver | Energy | F1 | Acc | Pr | Rc |
---|---|---|---|---|---|---|

25 | SA | −4.70 | 1 | 1 | 1 | 1 |

HQPU | −4.83 | 1 | 1 | 1 | 1 | |

QPU | 14.79 | 1 | 1 | 1 | 1 | |

LocalSolver—QUBO | −3.26 | |||||

LocalSolver—original | −6.09 | |||||

70 | SA | −5.59 | 0.95 | 0.96 | 1 | 0.9 |

HQPU | −6.28 | 1 | 1 | 1 | 1 | |

QPU | 270.04 | 0.95 | 0.96 | 1 | 0.9 | |

LocalSolver—QUBO | −5.34 | |||||

LocalSolver—original | −9.14 | |||||

225 | SA | −6.65 | 1 | 1 | 1 | 1 |

HQPU | −7.53 | 1 | 1 | 1 | 1 | |

LocalSolver—QUBO | −9.38 | |||||

LocalSolver—original | −14.68 |

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**MDPI and ACS Style**

Phillipson, F.; Wezeman, R.S.; Chiscop, I.
Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches. *Computers* **2021**, *10*, 71.
https://doi.org/10.3390/computers10060071

**AMA Style**

Phillipson F, Wezeman RS, Chiscop I.
Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches. *Computers*. 2021; 10(6):71.
https://doi.org/10.3390/computers10060071

**Chicago/Turabian Style**

Phillipson, Frank, Robert S. Wezeman, and Irina Chiscop.
2021. "Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches" *Computers* 10, no. 6: 71.
https://doi.org/10.3390/computers10060071