Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets
Lacroix N, Hellings C, Andersen CK, Di Paolo A, Remm A, Lazar S, Krinner S, Norris GJ, Gabureac M, Heinsoo J, Blais A, Eichler C, Wallraff A (2020)
Publication Type: Journal article
Publication year: 2020
Journal
Book Volume: 1
Article Number: 020304
Journal Issue: 2
DOI: 10.1103/PRXQuantum.1.020304
Abstract
Variational quantum algorithms are believed to be promising for solving computationally hard problems on noisy intermediate-scale quantum (NISQ) systems. Gaining computational power from these algorithms critically relies on the mitigation of errors during their execution, which for coherence-limited operations is achievable by reducing the gate count. Here, we demonstrate an improvement of up to a factor of 3 in algorithmic performance for the quantum approximate optimization algorithm (QAOA) as measured by the success probability, by implementing a continuous hardware-efficient gate set using superconducting quantum circuits. This gate set allows us to perform the phase separation step in QAOA with a single physical gate for each pair of qubits instead of decomposing it into two CZ gates and single-qubit gates. With this reduced number of physical gates, which scales with the number of layers employed in the algorithm, we experimentally investigate the circuit-depth-dependent performance of QAOA applied to exact-cover problem instances mapped onto three and seven qubits, using up to a total of 399 operations and up to nine layers. Our results demonstrate that the use of continuous gate sets may be a key component in extending the impact of near-term quantum computers.
Authors with CRIS profile
Involved external institutions
Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich
Switzerland (CH)
Université de Sherbrooke
Canada (CA)
Switzerland (CH)
Université de Sherbrooke
Canada (CA)
How to cite
APA:
Lacroix, N., Hellings, C., Andersen, C.K., Di Paolo, A., Remm, A., Lazar, S.,... Wallraff, A. (2020). Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets. PRX Quantum, 1(2). https://dx.doi.org/10.1103/PRXQuantum.1.020304
MLA:
Lacroix, Nathan, et al. "Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets." PRX Quantum 1.2 (2020).
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