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4152. Solving an MBDA's use case related to optimal assignment on current IBM Quantum Computers
Invited abstract in session MA-42: Hybrid Classical-Quantum Algorithms, stream Quantum Computing Optimization.
Monday, 8:30-10:00Room: 98 (building: 306)
Authors (first author is the speaker)
| 1. | Edouard Debry
|
| MBDA | |
| 2. | Davide Boschetto
|
| ENSTA, Paris | |
| 3. | Rachel Roux
|
| MBDA | |
| 4. | Janis Aiad
|
| Polytechnique | |
| 5. | Alexandre Kotenkoff
|
| MBDA |
Abstract
In this communication, we aim to present the solving of an MBDA's use case related to
optimal assignment, onto IBM online QPUs. The Quantum Approximate Optimization Algorithm
(QAOA) (Farhi et al. 2014) is the base of our Variational Quantum Algorithm developed.
We compare two methods to account for constraints, first primarily by integrating them
into the Cost Hamiltonian with Lagrangian multipliers and second, by adapting the Mixer
Hamiltonian according to (Wang et al. 2022) and (Fuchs et al. 2022). For the former,
determining the optimal Lagrangian multipliers is generally a challenging task and the
integration of constraints into the Cost Hamiltonian can significantly increase the
associated circuit depth. The latter method aims to reduce the overall Hilbert space to
only feasible solutions, which lets get rid of Lagrangian multipliers but may
significantly enlarge the circuit associated to the Mixer Hamiltonian and make the
initial state harder. It is then interesting to compare the circuit depth of both
methods with respect to how well they are able to statistically put forward optimal
solutions against non-optimal and non-feasible ones, still for relatively small sized
instances, to fit on current QPUs.
Keywords
- Combinatorial Optimization
- Programming, Integer
- Programming, Quadratic
Status: accepted
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