Quantum computing for electric mobility: a case study
Electric mobility will play a key role in solving major environmental and public health problems in the next decades, mainly by reducing greenhouse gas and fine particle emissions. In addition to this, the use of vehicle batteries both as energy storage and power supply devices could significantly improve the flexibility of our electric system, and – providing that the batteries store ‘clean’ energy – also reduce the use of fossil fuels in times of peak demand.
However, a few difficult problems lie ahead of this scheme. The electric system must be managed optimally in terms of cost, while also satisfying various hard technical constraints. These include for example: the modulation of the electricity demand according to different electric vehicle loads, the needs of the users, and the availability of buffer required to guarantee the frequency stability of the grid. If we look at them from a computational perspective, many such problems take the form of typical scheduling and Operational Research problems: they are large-sized combinatorial optimization problems, many of them known to be NP-hard/complete.
Investigating how quantum computing could deal with this kind of problems – specifically in electric mobility – is the topic of the paper “Qualifying quantum approaches for hard industrial optimization problems. A case study in the field of smart-charging of electric vehicles”. The paper, published in 2021, was written by an interdisciplinary group of researchers from both academia and industry: Constantin Dalyac, Loïc Henriet, Emmanuel Jeandel, Simon Perdrix, Marc Porcheron, Margarita Veshchezerova and ParityQC’s co-founder Wolfgang Lechner.
In this paper, the researchers presented two problems drawn from the rapidly growing sector of smart-charging of electrical vehicles, and outline their modeling for quantum resolution. The case study involved France’s main energy provider, EDF, and the quantum hardware manufacturer (and ParityQC’s partner) PASQAL. Tailored implementations of the Quantum Approximate Optimization Algorithm (QAOA) were developed for both problems, and tested numerically with classical resources either by emulation of PASQAL’s Rydberg atom-based quantum device or using Atos Quantum Learning Machine. In both cases, quantum algorithms exhibited the same approximation ratios as conventional approximation algorithms, or even improved them. One of the main findings of this paper was that the ParityQC Architecture allows one to implement the smart charging problem with less qubits compared to the conventional gate model.
These are very encouraging results, although still for instances of limited size, as allowed by studies on classical computing resources. These results will have to be confirmed on larger instances, on actual devices, and for more complex versions of the problems addressed. One of the goals of the paper was also to illustrate the practical issues faced when trying to address real industrial problems with available NISQ frameworks. Achieving good performances with quantum approaches requires the design of hardware-efficient procedures, that exploit the strengths of a given quantum processor. In the study, hardware and software were jointly developed in order to optimize the execution of the overall implementation.