Quantum Application Lab
Application (roadmap) development for quantum computing
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Towards useful applications for quantum computing
+ Participants
+ QAL consortium
+ Industry
and others
and others
Need��
Better compute capabilities for solving complex problems in optimization, simulation and machine learning – including:
clustering / imaging / sampling / ranking / search / classification / …�
Want��
How and when will my organization use quantum computing?
Demand��
Support in application (roadmap) development �
QAL offering��
Benefits��
H3: disruptieve innovatie
20%
Public
Industry
Private
Private
Private
Science
End-users (participants) are actively involved in application development projects (from +/- 4 months to 12+ months).
Participants may have joint interests in terms of application focus (optimization, machine learning) and/or industry focus (aviation, healthcare etc).
Building on experience with joint innovation ecosystems we expect to deliver a multiplier on investments for quantum applications of 3-8.
Knowledge institutes
Aviation�
Problem
At Air France KLM over 7000 employees work in shifts with different contracts, skill levels and authorizations. Efficiently scheduling all these employees becomes a complex combinatorial tasks. To solve it classically, the problem is being simplified. While this task is classically intractable, quantum computing heuristics have shown potential to handle combinatorial problems effectively.
Solution
The solution employs quantum annealing, a technique used to find the global minimum in a given function, revolutionizing rostering processes. By formulating this rostering problem as a Quadratic Unconstrained Optimization (QUBO) problem, we solved it using quantum annealing, classical-quantum hybrid annealing and simulated annealing.
Benefits
The quantum annealers already achieve close to the optimal schedule, while the hybrid solvers outperform both the quantum annealers and simulated annealers. By utilizing quantum annealing, the solver could in future generate optimal rosters that conform to regulations, preferences, and operational needs – far more efficiently than traditional methods.
�Earth observation�
Problem
Bathymetry is the study of water depths and at S[&]T this is done through the analysis of remote sensing data. The data consists of 13 spectral bands with a temporal resolution of 5 days and spatial resolution between 10 and 60m, leading to an incredibly large amount of data to process.
Solution
Quantum neural networks have the potential to arrive at high accuracies using small amounts of data, due to their ability to explore a large parameter space. The downside is the difficulty in embedding large data sets. To take full advantage of quantum hardware, a classical dimensionality-reduction was used to reduce the high spectral dimensionality, after which the data was embedded in a variational quantum classifier to classify data into depth intervals.
Benefits
We worked with S[&]T to turn their existing ML models into quantum models. The quantum model has been able to faithfully reproduce the classical ML results. We’re actively investigating what (dis)advantages the quantum approach has (such as generalization with fewer data samples). In the process of working out the use case, we gained additional insights into ways to improve the classical solution.
�Energy�
Problem
Dutch DSO Alliander is faced with the challenge of increasing the capacity of their network within the next 10 years by the same amount as within the past 100 years. On top of that, novel technologies like solar panels and electric vehicles add additional complexities to this future network.
Solution
During this initial collaboration, 3 potential quantum computing solutions were researched. Our 1st method was based on amplitude amplification. By preparing a superposition over many different network configurations, we exploit quantum parallelism to efficiently find a valid network reconfiguration. The 2nd approach used quantum annealing, encoding our problem as a QUBO. The 3rd approach used Gaussian Boson Samplers for efficiently checking connectivity of networks after a cable outage disconnects them.
Benefits
The amplitude amplification algorithm was tested on quantum emulators and was successfully run to solve the N-1 problem (graphs up to size 7). Results indicate that as hardware quality and size advance, robustness of large systems that is classically incomputable, could potentially be verified on a quantum computer.
�Hydrogen production�
Problem
Toyota Motor Europe is delving into the realm of photocatalysis for water splitting, a pivotal process in the sustainable production of hydrogen. The accurate simulation of photocatalytic reactions stands as a complex, resource-intensive endeavor that demands innovative computational methodologies.
Solution
The project employs a state-averaged orbital optimized variational quantum eigensolver to calculate the interaction energies of heterogeneous photocatalysis for water splitting in the ground as well as excited states. This advanced quantum algorithm allows for the precise and efficient modeling of complex photocatalytic processes, providing an unprecedented level of detail and insight into the interactions at play during photocatalysis.
Benefits
Simulating chemical processes, such as the splitting of water into hydrogen, promises to be among the main future applications. Calculations were run on a quantum emulator and as the collaboration continues and hardware advances, we expect to be able to model larger and more-realistic systems.
Thank you for your attention!�
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https://www.linkedin.com/company/quantum-application-lab/
https://github.com/QuantumApplicationLab
Mark Buningh
+31 (0)6 1568 2559