Executing Deep Quantum Optimization Algorithms on Superconducting Quantum Processors

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Superconducting circuits are a prime contender both for realizing fault-tolerant quantum processors and for executing noisy intermediate-scale quantum (NISQ) algorithms. After a brief introduction to the quantum physics of superconducting circuits I will focus on the latter. The quantum approximate optimization algorithm (QAOA) is one of a whole class of variational methods, which are believed to be promising to solve computationally hard problems on quantum computers. Also, the performance of noisy intermediate-scale quantum information processing systems is commonly benchmarked using such algorithms. Gaining computational power from QAOA critically relies on the mitigation of errors during the execution of the algorithm, which for coherence-limited operations is achievable by reducing the gate count. Here, I present an improvement of up to a factor of three in algorithmic performance, by implementing a continuous hardware-efficient gate set. This gate set allows us to perform the phase separation step in QAOA with a single operation for each pair of qubits instead of decomposing it into multiple gates. We experimentally investigate the circuit-depth-dependent performance of our QAOA implementations for finding solutions to exact cover problems mapped onto three or seven qubits using up to a total of 399 operations in up to 9 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 [1]. Using the same device architecture, we have recently demonstrated repeated error detection [2], an important step toward quantum error correction and the execution of fault-tolerant quantum algorithms.

[1] N. Lacroix et al., arXiv:2005.05275 (2020)
[2] C. K. Andersen et al., Nat. Phys. (2020). https://doi.org/10.1038/s41567-020-0920-y

(Online Event)


July 24, 2020
5:00 pm - 6:00 pm
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