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
Book Volume: 1
Article Number: 020304
Journal Issue: 2
DOI: 10.1103/PRXQuantum.1.020304
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.
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://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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