Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets
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Date
2020
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Journal Article
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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.
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published
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1 (2)
Pages / Article No.
110304
Publisher
American Physical Society
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03720 - Wallraff, Andreas / Wallraff, Andreas
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820363 - An Open Superconducting Quantum Computer (EC)
170731 - 10 Millikelvin Cryostat for Quantum Science with Tens to Hundreds of Superconducting Qubits (SNF)
170731 - 10 Millikelvin Cryostat for Quantum Science with Tens to Hundreds of Superconducting Qubits (SNF)