Approximating the quantum approximate optimization algorithm with digital-analog interactions

Headley, David; Müller, Thorge; Martin, Ana; Solano, E.; Sanz, Mikel; Wilhelm, Frank K.

doi:10.1103/physreva.106.042446

Cited by 21 publications

(10 citation statements)

References 51 publications

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“…This report explores the possibility of factoring larger numbers with compressed algorithms in given quantum computers, rather than proving any scalability of computational resources. On the other hand, further use of analog [3,4] or digital-analog encoding schemes [5][6][7] may pave the way towards factoring RSA-2048 in the NISQ era, without the long wait for faulttolerant quantum computers.…”

mentioning

confidence: 99%

“…Going beyond that with current digital quantum computers, for given gate fidelities and coherence times, looks like a challenging task. However, alternative approaches like digital-analog quantum computing, involving multiqubit analog blocks in combination with local digital steps, might tackle RSA-2048, bringing quantum advantage to the NISQ era [5][6][7]. Moreover, one could also consider this problem on programmable analog quantum simulators, like neutral atom devices [4,18], with Floquet engineering techniques.…”

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confidence: 99%

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Digitized counterdiabatic quantum optimization

Hegade

Chen

Solano

2022

Phys. Rev. Research

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We factorize a 48-bit integer using 10 trapped-ion qubits on a Quantinuum's quantum computer. This result outperforms the recent achievement by B. Yan et al., arXiv:2212.12372 (2022, increasing the success probability by a factor of 6 with a non-hybrid digitized-counterdiabatic quantum factorization (DCQF) algorithm. We expect better results with hybrid DCQF methods on our path to factoring RSA-64, RSA-128, and RSA-2048 in this NISQ era, where the latter case may need digital-analog quantum computing (DAQC) encoding.

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confidence: 99%

mentioning

confidence: 99%

Digitized counterdiabatic quantum optimization

Hegade

Chen

Solano

2022

Phys. Rev. Research

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“…However, implementing high-depth QAOA circuits can be impractical experimentally due to various sources of noise [62]. Therefore, improving QAOA with low-depth circuits has been actively pursued [63][64][65].…”

Section: Meta-learning Dc-qaoamentioning

confidence: 99%

Meta-learning digitized-counterdiabatic quantum optimization

Chandarana

Vieites

Hegade

et al. 2023

Quantum Sci. Technol.

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The use of variational quantum algorithms for optimization tasks has emerged as a crucial application for the current noisy intermediate-scale quantum computers. However, these algorithms face significant difficulties in finding suitable ansatz and appropriate initial parameters. In this paper, we employ meta-learning using recurrent neural networks to address these issues for the recently proposed digitized-counterdiabatic quantum approximate optimization algorithm. By combining meta-learning and counterdiabaticity, we find suitable variational parameters and reduce the number of optimization iterations required.We demonstrate the effectiveness of our approach by applying it to the MaxCut problem and the Sherrington-Kirkpatrick (SK) model. Our method offers a short-depth circuit ansatz with optimal initial parameters, thus improving the performance of the state-of-the-art QAOA.

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“…There is another paradigm called banged DAQC (bDAQC) in which the interaction Hamiltonian is never switched off and single-qubit rotations (SQR's) are applied on top of the analog dynamics. This introduces an inherent error, since it does not implement the exact algorithm, but when experimental errors are taken into account, it happens to scale up better than sDAQC and DQC [10,11,22]. This intuitively holds when the application time of SQRs, ∆t, is much smaller than the time scale of the analog blocks, so the error introduced is smaller than the one coming from switching on and off the Hamiltonian in sDAQC.…”

Section: Digital-analog Quantum Computingmentioning

confidence: 99%

Noise in digital and digital-analog quantum computation

García-Molina¹,

Martin²,

Sanz³

2023

Preprint

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Quantum computing uses quantum resources provided by the underlying quantum nature of matter to enhance classical computation. However, the current Noisy Intermediate-Scale Quantum (NISQ) era in quantum computing is characterized by the use of quantum processors comprising from a few tens to, at most, a few hundreds of physical qubits without implementing quantum error correction techniques. This limits the scalability in the implementation of quantum algorithms. Digital-analog quantum computing (DAQC) has been proposed as a more resilient alternative quantum computing paradigm to outperform digital quantum computation within the NISQ era framework. It arises from adding the flexibility provided by fast single-qubit gates to the robustness of analog quantum simulations. Here, we perform a careful comparison between the digital and digital-analog paradigms under the presence of noise sources. The comparison is illustrated by comparing the performance of the quantum Fourier transform and quantum phase estimation algorithms under a wide range of single- and two-qubit noise sources. Indeed, we obtain that when the different noise channels usually present in superconducting quantum processors are considered, the fidelity of these algorithms for the digital-analog paradigm outperforms the one obtained for the digital approach. Additionally, this difference grows when the size of the processor scales up, making DAQC a sensible alternative paradigm in the NISQ era. Finally, we show how to adaptthe DAQC paradigm to quantum error mitigation techniques for canceling different noise sources, including the bang error.

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Approximating the quantum approximate optimization algorithm with digital-analog interactions

Cited by 21 publications

References 51 publications

Digitized counterdiabatic quantum optimization

Digitized counterdiabatic quantum optimization

Meta-learning digitized-counterdiabatic quantum optimization

Noise in digital and digital-analog quantum computation

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