Quantum Shuttle: traffic navigation with Quantum computing

Yarkoni, Sheir; Neukart, Florian; Tagle, Eliane Moreno Gomez; Magiera, Nicole; Mehta, Bharat; Hire, Kunal; Narkhede, Swapnil; Hofmann, Martin

doi:10.1145/3412451.3428500

Cited by 30 publications

(11 citation statements)

References 16 publications

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“…Quantum computer has a great potential to solve a particular type of computing task that used to be considered infeasible for classical computers. As one type of quantum computing, the power of adiabatic quantum computation was validated in a 6G smart transportation pilot project for assigning optimal bus routes in the city of Lisbon, Portugal [282]. Such quantum computing powered AI-decision making will be exponentially faster in 6G ages due to more input data every second.…”

Section: Limiations and Challenges Of Xai For 6gmentioning

confidence: 99%

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

Wang¹,

Qureshi²,

Miralles-Pechuaán³

et al. 2021

Preprint

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When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and, more importantly, an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made every second. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making beyond designers and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This survey paper highlights the need for XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems. This research aligns with goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals (UN-SDG) 1 , promoting innovation and building infrastructure, sustainable and inclusive human settlement, advancing justice and strong institutions, and fostering partnership at the global level.

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Section: Limiations and Challenges Of Xai For 6gmentioning

confidence: 99%

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

Wang¹,

Qureshi²,

Miralles-Pechuaán³

et al. 2021

Preprint

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show abstract

“…This limits the minimum average degree a graph can have based on the number of nodes (each of which correspond to DQM variables in this case), since the minimum connected graph with q nodes is either a star or line graph with q − 1 edges, and therefore an average degree of d q = 2 q−1 q . For example, a three node connected graph cannot have a degree less than 4 3 and a four node connected graph cannot have degree less than 3 2 , since 3 2 > √ 2 > d crit , it follows that an auxiliary based encoding cannot be more efficient than the domain-wall encoding for problems on graphs with more than three nodes (and therefore containing more than three DQM variables). As discussed in [17] increasing the variable size with a fixed number of variables is not a scalable way to perform quantum computation, so therefore the domain-wall encoding is the most efficient encoding for general interactions for all cases of practical interest.…”

Section: Substituting In Variables Yieldsmentioning

confidence: 99%

“…Quantum computing shows great promise for combinatorial optimisation problems, and many proof-of-concept experiments have been performed demonstrating the potential in a variety of areas including vehicle scheduling [1], traffic flow optimisation [2,3], hydrology [4], computational biology [5,6], community detection [7], graph theoretical problems [8][9][10], and supply chain logistics [11]. While this is an area with great promise, available devices exist in relatively early stages of development, which is often termed the noisy intermediate-scale quantum (NISQ) [12] era of quantum computing.…”

Section: Introductionmentioning

confidence: 99%

Understanding domain-wall encoding theoretically and experimentally

Berwald¹,

Chancellor²,

Dridi³

2021

Preprint

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We analyze the performance of encoding pairwise interactions of higher-than-binary discrete variables (these models are sometimes referred to as discrete quadratic models) into binary variables based on domain walls on one dimensional Ising chains. We discuss how this is relevant to quantum annealing, but also many gate model algorithms such as VQE and QAOA. We theoretically show that for problems of practical interest for quantum computing and assuming only quadratic interactions are available between the binary variables, it is not possible to have a more efficient general encoding in terms of number of binary variables per discrete variable. We furthermore use a D-Wave Advantage 1.1 flux qubit quantum annealing computer to show that the dynamics effectively freeze later for a domain-wall encoding compared to a traditional one-hot encoding. This second result could help explain the dramatic performance improvement of domain wall over one hot which has been seen in a recent experiment on D-Wave hardware. This is an important result because usually problem encoding and the underlying physics are considered separately, our work suggests that considering them together may be a more useful paradigm. We argue that this experimental result is also likely to carry over to a number of other settings, we discuss how this has implications for gate-model and quantum-inspired algorithms.

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“…While small for the application, this is larger than could be solved on D-Wave QPUs at the time of experiments. Instead, a proprietary hybrid classical-quantum algorithm offered by D-Wave Systems was used, called the Hybrid Solver Service (HSS), which has been used in previous applications [27], and admits QUBOs with up to 10k binary variables. The HSS uses a QPU to optimize clusters of variables, allowing one to leverage the use of a quantum processor without the overhead of embedding.…”

Section: Experiments and Datamentioning

confidence: 99%

Solving the Shipment Rerouting Problem with Quantum Optimization Techniques

Yarkoni

Huck

Schülldorf

et al. 2021

Lecture Notes in Computer Science

Self Cite

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In this work we develop methods to optimize an industriallyrelevant logistics problem using quantum computing. We consider the scenario of partially filled trucks transporting shipments between a network of hubs. By selecting alternative routes for some shipment paths, we optimize the trade-off between merging partially filled trucks using fewer trucks in total and the increase in distance associated with shipment rerouting. The goal of the optimization is thus to minimize the total distance travelled for all trucks transporting shipments. The problem instances and techniques used to model the optimization are drawn from real-world data describing an existing shipment network in Europe. We show how to construct this optimization problem as a quadratic unconstrained binary optimization (QUBO) problem. We then solve these QUBOs using classical and hybrid quantum-classical algorithms, and explore the viability of these algorithms for this logistics problem.

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Quantum Shuttle: traffic navigation with Quantum computing

Cited by 30 publications

References 16 publications

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

Understanding domain-wall encoding theoretically and experimentally

Solving the Shipment Rerouting Problem with Quantum Optimization Techniques

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