IT-Porto

Towards an Applied Quantum Machine Learning

in Intelligent Transportation Systems

Networked Systems

Modeling and predicting (citywide) traffic congestion can have high

computational cost due to its complexity. Quantum machine learning (QML)

can aid speedup traffic dynamics by performing quantum operations. We aim

to implement a traffic flow application that would not be efficiently solved in a

classical computer. The main contributions are summarized as follows: (a)

data fusion method to infer microscopic fundamental diagrams which

allows us to determine the traffic state in individual road segments, (b)

methodology to identify traffic congestion causalities making use of Bayesian

networks to learn probabilistic dependencies from traffic data and

confounding factors, and (c) assess the performance of the proposed

methodology in a case study scenario in the city of Porto.

Can we implement Markov

computations faster in a

quantum simulator?

We explore Pauli X gate (NOT

gate) and Hadamard gate to

map the qubit-basis states |0⟩and |1⟩ to superposition states.

● Poster presented at the 3rd International Conference for Young Quantum Information Scientists 3-6 October 2017, Max Planck Institute

for the Science of Light, Friedrich-Alexander Universitat Erlangen-Nurnberg.

● Journal article at ITNOW (Oxford Academic)

● Extended abstract presented at Quantum Machine Learning Workshop at KDD 2018 - London, United Kingdom. 19 - 23 August 2018.

arXiv:1808.08429

● Conference article at 1st Workshop on Data-driven Intelligent Transportation (DIT 2018), Held in conjunction with IEEE ICDM 2018,

November 17-20, 2018 in Singapore, November 2018

Can we use quantum tabu search efficiently to find coupling maps

for quantum circuits?

We uncover optimal solutions and accelerate the optimisation process

using entangled states in a quantum optimisation.

UID/EEA/50008/2013

Six spatial windows for fusing data. Speed measurements for the virtual spatial windows. Probe Penetration Ratio (PPR): 2.92%.

Estimated microscopic fundamental diagrams for selected urban roads in the city of Porto.

Tree Augmented Naive

Bayes: congested (left),

uncongested (right) states

for the different locations.

Background and challenges

Simulations in Google Quantum Computing Playground and IBM Q Experience

Achievements

Traffic Congestion Using Fundamental Diagrams and Probabilistic Graphical Models

Description and main innovation