Quantum Divide and Compute: Hardware Demonstrations and Noisy Simulations

Ayral, Thomas; Régent, François-Marie Le; Saleem, Zain H.; Alexeev, Yuri; Suchara, Martin

doi:10.1109/isvlsi49217.2020.00034

Cited by 26 publications

(33 citation statements)

References 6 publications

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“…In this study, we focus on the noise processes whose quantitative levels are reported by the hardware manufacturer, IBM (see Table 1 for a summary of the numerical values used in the noisy simulations below). This pragmatic approach is justified a posteriori by the reasonable One-qubit-gate error rate (1) avg 0.041% Two-qubit-gate error rate (2) avg 0.202% Relaxation time T 1 65 μs Dephasing time T 2 agreement of our numerical simulations with the experimental data (see Ref. [1], and "Results").…”

Section: Noisy Simulationmentioning

confidence: 53%

“…This pragmatic approach is justified a posteriori by the reasonable One-qubit-gate error rate (1) avg 0.041% Two-qubit-gate error rate (2) avg 0.202% Relaxation time T 1 65 μs Dephasing time T 2 agreement of our numerical simulations with the experimental data (see Ref. [1], and "Results"). It should nevertheless be emphasized that (i) it uses rather simple noise models, that should be compared to noise models extracted from a full process tomography of the processor, and that (ii) it excludes some noise processes that are suspected to affect the final quantum state distribution in a non-negligible way, e.g., crosstalk (spatially correlated noise) and temporally correlated noise (like 1/f noise).…”

Section: Noisy Simulationmentioning

confidence: 53%

“…In [1], we investigated the performance of the circuit-cutting procedure for a simple GHZ-type circuit shown in Fig. 1a.…”

Section: Summary Of Previous Resultsmentioning

confidence: 99%

“…One exciting application of the circuit-cutting technique is to allow to execute much larger circuits. It can be done in two ways: split circuits and run sequentially on a quantum device (as we demonstrated in [1]), or run at the same time on multiple quantum devices. The latter way can lead to an exciting new era of how quantum computation is done-distributed quantum computing.…”

Section: Homogeneous and Heterogeneous Quantum Computingmentioning

confidence: 99%

“…In this article, we follow up on our previous work on the topic [1]: we start by giving a detailed derivation of the formula for the output reconstruction of the original circuit from the outputs of its fragments, and a description of the noise models we chose to reproduce the experimental results ("Methods"). We quantify the performance of the QDC method by recalling our previous results [1] on a 20-qubit IBM processor for different qubit counts and fragment sizes ("Summary of Previous Results"). Then in "Results", based on noisy simulations, we quantify the differential influence of various noise sources such as readout error, gate error and decoherence on the success probability of the algorithm for different qubit counts and fragment sizes.…”

Section: Introductionmentioning

confidence: 99%

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Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

et al. 2021

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Our recent work (Ayral et al. in Proceedings of IEEE computer society annual symposium on VLSI, ISVLSI, pp 138–140, 2020. 10.1109/ISVLSI49217.2020.00034) showed the first implementation of the Quantum Divide and Compute (QDC) method, which allows to break quantum circuits into smaller fragments with fewer qubits and shallower depth. This accommodates the limited number of qubits and short coherence times of quantum processors. This article investigates the impact of different noise sources—readout error, gate error and decoherence—on the success probability of the QDC procedure. We perform detailed noise modeling on the Atos Quantum Learning Machine, allowing us to understand tradeoffs and formulate recommendations about which hardware noise sources should be preferentially optimized. We also describe in detail the noise models we used to reproduce experimental runs on IBM’s Johannesburg processor. This article also includes a detailed derivation of the equations used in the QDC procedure to compute the output distribution of the original quantum circuit from the output distribution of its fragments. Finally, we analyze the computational complexity of the QDC method for the circuit under study via tensor-network considerations, and elaborate on the relation the QDC method with tensor-network simulation methods.

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Section: Noisy Simulationmentioning

confidence: 53%

Section: Noisy Simulationmentioning

confidence: 53%

“…In [1], we investigated the performance of the circuit-cutting procedure for a simple GHZ-type circuit shown in Fig. 1a.…”

Section: Summary Of Previous Resultsmentioning

confidence: 99%

Section: Homogeneous and Heterogeneous Quantum Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

et al. 2021

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Simulating noisy variational quantum eigensolver with local noise models

Zeng

Cao

et al. 2021

Quantum Engineering

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The variational quantum eigensolver (VQE) is a promising algorithm to demonstrate quantum advantage on near‐term noisy‐intermediate‐scale quantum (NISQ) computers. One central problem of VQE is the effect of noise, especially physical noise, on realistic quantum computers. We systematically study the effect of noise for the VQE algorithm by performing numerical simulations with various local noise models, including amplitude damping, dephasing, and depolarizing noise. We show that the ground state energy will deviate from the exact value as the noise probability increase, and typically, the noise will accumulate as the circuit depth increase. The results suggest that the noisy quantum system can remain entanglement at the noise level of NISQ devices by comparing the VQE solution with the mean‐field solution for the many‐body ground state problem. We build a noise model to capture the noise in a real quantum computer, and the corresponding numerical simulation is consistent with experimental results on IBM Quantum computers through cloud. Our work sheds new light on the practical research of noisy VQE, and the deep understanding of the noise effect of VQE will also help develop error mitigation techniques on near‐term quantum computers.

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