Variationally scheduled quantum simulation

Matsuura, Shunji; Buck, Samantha; Senicourt, Valentin; Zaribafiyan, Arman

doi:10.1103/physreva.103.052435

Cited by 15 publications

(12 citation statements)

References 69 publications

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“…For VQE, some notable parameters include "qubit_mapping" (e.g Jordan-Wigner, Bravyi-Kitaev, symmetry-conserving-Bravyi-Kitaev, Jiang-Kalev-Mruczkiewicz-Neven [57],...), initial variational parameters, or the choice of "ansatz". Tangelo provides BuiltInAnsatze such as UCCSD [58][59][60], k-UpCCGSD [61], HEA [62], QCC [63], RUCC [64] and Variationally Scheduled Quantum Simulation (VSQS) [65], but users can define and pass their own ansatz objects, or even simply pass any variational quantum circuit defined in Tangelo. We highlight some of these functionalities for the QCC ansatz with an illustrative example in the following paragraphs, after briefly reviewing the methodology.…”

Section: Quantum Algorithmsmentioning

confidence: 99%

Tangelo: An Open-source Python Package for End-to-end Chemistry Workflows on Quantum Computers

Senicourt¹,

Brown²,

Fleury³

et al. 2022

Preprint

Self Cite

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Tangelo (https link) is an open-source Python software package for the development of end-to-end chemistry workflows on quantum computers, released under Apache 2.0 license. It aims to support the design of successful experiments on quantum hardware, and to facilitate advances in quantum algorithm development. The software enables quick exploration of different approaches by assembling reusable building blocks and algorithms, with the flexibility to let users introduce their own. Tangelo is backend-agnostic and enables switching between various backends (Braket, Qiskit, Qulacs, Azure Quantum, QDK, Cirq...) with minimal changes in the code. The package can be used to explore quantum computing applications such as open-shell systems, excited states, or more industrially-relevant systems by leveraging problem decomposition at scale. This paper outlines the design choices, philosophy, and main features of Tangelo.

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Section: Quantum Algorithmsmentioning

confidence: 99%

Tangelo: An Open-source Python Package for End-to-end Chemistry Workflows on Quantum Computers

Senicourt¹,

Brown²,

Fleury³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Furthermore, several methods using non-adiabatic transitions and quenching for efficient QA have been studied [54][55][56][57][58][59][60]. Other approaches have also been proposed to suppress non-adiabatic transitions and decoherence by using variational methods [61][62][63]. It is worth noting that such variational methods have been adopted to find a ground state of the Hamiltonian by using variational algorithms with near-term intermediate-scale quantum devices [64,65].…”

Section: Quantum Annealingmentioning

confidence: 99%

Quantum annealing with twisted fields

et al. 2022

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Quantum annealing is a promising method for solving combinational optimization problems and performing quantum chemical calculations. The main sources of errors in quantum annealing are the effects of decoherence and non- adiabatic transition. We propose a method for suppressing both these effects using inhomogeneous twist operators corresponding to the twist angles of transverse fields applied to qubits. Furthermore, we adopt variational methods to determine the optimal inhomogeneous twist operator for minimizing the energy of the state after quantum annealing. Our approach is useful for increasing the energy gap and/or making the quantum states robust against decoherence during quantum annealing. In summary, our results can pave the way to a new approach for realizing practical quantum annealing.

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“…In addition, there are several schemes to improve the performance of QA by using non-adiabatic transitions and quenching [69][70][71][72][73][74][75][76] and degenerating two-level systems [77]. Variational methods have also been applied to QA to suppress non-adiabatic transitions and decoherence [78][79][80][81].…”

Section: Quantum Annealingmentioning

confidence: 99%

Quantum annealing with error mitigation

Shingu¹,

Nikuni²,

Kawabata³

et al. 2022

Preprint

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Quantum annealing (QA) is one of the efficient methods to calculate the ground-state energy of a problem Hamiltonian. In the absence of noise, QA can accurately estimate the ground-state energy if the adiabatic condition is satisfied. However, in actual physical implementation, systems suffer from decoherence. On the other hand, much effort has been paid into the noisy intermediate-scale quantum (NISQ) computation research. For practical NISQ computation, many error mitigation (EM) methods have been devised to remove noise effects. In this paper, we propose a QA strategy combined with the EM method called dual-state purification to suppress the effects of decoherence. Our protocol consists of four parts; the conventional dynamics, single-qubit projective measurements, Hamiltonian dynamics corresponding to an inverse map of the first dynamics, and post-processing of measurement results. Importantly, our protocol works without two-qubit gates, and so our protocol is suitable for the devices designed for practical QA. We also provide numerical calculations to show that our protocol leads to a more accurate estimation of the ground energy than the conventional QA under decoherence.

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Variationally scheduled quantum simulation

Cited by 15 publications

References 69 publications

Tangelo: An Open-source Python Package for End-to-end Chemistry Workflows on Quantum Computers

Tangelo: An Open-source Python Package for End-to-end Chemistry Workflows on Quantum Computers

Quantum annealing with twisted fields

Quantum annealing with error mitigation

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