A universal duplication-free quantum neural network

Hou, Xiaokai; Zhou, Guanyu; Li, Qingyu; Jin, Shan; Wang, Xiaoting

doi:10.48550/arxiv.2106.13211

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…On the algorithms side, variational quantum algorithms (VQAs) [34] are among the most promising approaches for solving a range of both optimization and simulation problems on NISQ era devices due to their resilience to noise [35]. Although VQAs have been developed for applications including quantum chemistry [12,14,36], optimization problems [37,38], quantum neural networks [39][40][41][42] and many more, our focus is on the electronic structure problem, which deals with finding the low-lying energy states of a molecule [12,36]. The electronic structure problem is the cornerstone of quantum chemical calculations, which are believed to be one of the leading fields in which quantum computers can demonstrate an advantage over classical computers.…”

Section: Introductionmentioning

confidence: 99%

Minimizing state preparation times in pulse-level variational molecular simulations

Asthana¹,

Liu²,

Economou³

et al. 2022

Preprint

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Quantum simulation on NISQ devices is severely limited by short coherence times. A variational pulse-shaping algorithm known as ctrl-VQE was recently proposed to address this issue by eliminating the need for parameterized quantum circuits, which lead to long state preparation times. Here, we find the shortest possible pulses for ctrl-VQE to prepare target molecular wavefunctions for a given device Hamiltonian describing coupled transmon qubits. We find that the time-optimal pulses that do this have a bang-bang form consistent with Pontryagin's maximum principle. We further investigate how the minimal state preparation time is impacted by truncating the transmons to two versus more levels. We find that leakage outside the computational subspace (something that is usually considered problematic) speeds up the state preparation, further reducing device coherencetime demands. This speedup is due to an enlarged solution space of target wavefunctions and to the appearance of additional channels connecting initial and target states.

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Section: Introductionmentioning

confidence: 99%

Minimizing state preparation times in pulse-level variational molecular simulations

Asthana¹,

Liu²,

Economou³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In such algorithms, the complexity of the system is divided between a quantum simulator and a classical optimizer, allowing an imperfect shallow NISQ circuit to eventually achieve quantum advantage over classical computers. The quantum-classical variational algorithms have been found useful for several applications in various fields, including computational chemistry [9][10][11][12][13][14], simulating strongly correlated systems [15][16][17][18] and their phase detection [19], optimization [20][21][22][23][24], solving linear [25][26][27] and nonlinear [28] equations, classification problems [29,30], generative models [31][32][33] and quantum neural networks [34,35]. Among these algorithms, the Variational Quantum Eigensolver (VQE) [10,36,37], as a special type of VQAs, has been developed for efficiently generating the ground state of many-body systems on quantum simulators.…”

Section: Introductionmentioning

confidence: 99%

Robust resource-efficient quantum variational ansatz through evolutionary algorithm

Huang,

Li,

Hou

et al. 2022

Preprint

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Variational quantum algorithms (VQAs) are promising methods to demonstrate quantum advantage on near-term devices as the required resources are divided between a quantum simulator and a classical optimizer. As such, designing a VQA which is resource-efficient and robust against noise is a key factor to achieve potential advantage with the existing noisy quantum simulators. It turns out that a fixed VQA circuit design, such as the widely-used hardware efficient ansatz, is not necessarily robust against imperfections. In this work, we propose a genome-length-adjustable evolutionary algorithm to design a robust VQA circuit that is optimized over variations of both circuit ansatz and gate parameters, without any prior assumptions on circuit structure or depth. Remarkably, our method not only generates a noise-effect-minimized circuit with shallow depth, but also accelerates the classical optimization by substantially reducing the number of parameters. In this regard, the optimized circuit is far more resource-efficient with respect to both quantum and classical resources. As applications, based on two typical error models in VQA, we apply our method to calculating the ground energy of the hydrogen molecule and the Heisenberg model. Simulations suggest that compared with conventional hardware efficient ansatz, our circuit-structure-tunable method can generate circuits apparently more robust against both coherent and incoherent noise, and hence more likely to be implemented on near-term devices.

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A universal duplication-free quantum neural network

Cited by 2 publications

References 30 publications

Minimizing state preparation times in pulse-level variational molecular simulations

Minimizing state preparation times in pulse-level variational molecular simulations

Robust resource-efficient quantum variational ansatz through evolutionary algorithm

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