Variational quantum process tomography of unitaries

Xue, Shichuan; Liu, Yong; Wang, Yan; Zhu, Paikun; Guo, Chu; Wu, Junjie

doi:10.1103/physreva.105.032427

Cited by 19 publications

(10 citation statements)

References 36 publications

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“…Remarkably, we observe that several existing studies have successfully integrated quantum circuits with machine learning techniques to address challenges in quantum tomography. This includes advancements in quantum state tomography [54,55], quantum process tomography [53,56], and gate set tomography [52]. Notably, the concurrent studies of [53,56] propose an innovative learning protocol for unitary processes utilizing entangled states.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A noise-robust quantum dynamics learning protocol based on Choi–Jamiolkowski isomorphism: theory and experiment

Chen,

Gao,

Qiu

et al. 2024

New J. Phys.

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With the rapid development of quantum technology, the growing manipulated Hilbert space makes learning the dynamics of the quantum system a significant challenge. Machine learning technique has brought apparent advantages in some learning strategies, therefore, we introduce it to indirect learning in this paper. Based on Choi-Jamiolkowski isomorphism, we propose a protocol that learns the dynamics of an inaccessible quantum system using a quantum device at hand. For an n-qubit system, the learning task can be done iteratively, with operational complexity O(poly(n,L)/ε2) in each iteration, where L is the circuit depth and ε is the measurement error. Then we theoretically prove its noise resilience to global depolarization, state preparation and measurement noise, and unitary noise in gates implementation, where we find the learned dynamics stay invariant. Finally, we investigate the protocol experimentally on a nitrogen-vacancy center system with a natural noise source. The results show that the behavior of a relatively intractable nuclear spin can be learned through an easily accessible electron spin under different noise models, demonstrating the protocol’s feasibility.

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Section: Conclusion and Discussionmentioning

confidence: 99%

A noise-robust quantum dynamics learning protocol based on Choi–Jamiolkowski isomorphism: theory and experiment

Chen,

Gao,

Qiu

et al. 2024

New J. Phys.

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show abstract

“…GAN-based approximations of the superoperator Λ have also been proposed as an efficient method for QPT [86]. Other methods exploit NNs to generalise QPT to the characterisation of time dependent spin systems [139], while yet another class reconstructs a unitary quantum process by inverting the dynamics using a variational algorithm [140,141]. RNNs have recently been shown to be useful in learning the non-equilibrium dynamics of a many-body quantum system from its nonlinear response under random driving [142].…”

Section: A Learning Dynamics With Quantum Process Tomographymentioning

confidence: 99%

Learning Quantum Systems

Gebhart¹,

Santagati²,

Gentile³

et al. 2022

Preprint

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“…VQAs are hybrid quantum-classical algorithms where a cost function is efficiently evaluated on a quantum processor and is optimized using classical optimization routines. This approach is used by variational quantum eigensolvers [7], quantum approximate optimization algorithms [8], and quantum neural networks [9][10][11], finding applications in quantum chemistry [12][13][14], finances [15][16][17][18], and quantum tomography [19][20][21][22][23][24][25], among others.…”

Section: Introductionmentioning

confidence: 99%

Avoiding barren plateaus in the variational determination of geometric entanglement

Zambrano,

Muñoz-Moller,

Muñoz

et al. 2024

Quantum Sci. Technol.

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The barren plateau phenomenon is one of the main obstacles to implementing variational quantum algorithms in the current generation of quantum processors. Here, we introduce a method capable of avoiding the barren plateau phenomenon in the variational determination of the geometric measure of entanglement for a large number of qubits. The method is based on measuring compatible two-qubit local functions whose optimization allows for achieving a well-suited initial condition, from which a global function can be further optimized without encountering a barren plateau. We analytically demonstrate that the local functions can be efficiently estimated and optimized. Numerical simulations up to 18-qubit GHZ and W states demonstrate that the method converges to the exact value. In particular, the method allows for escaping from barren plateaus induced by hardware noise or global functions defined on high-dimensional systems. Numerical simulations with noise are in agreement with experiments carried out on IBM’s quantum processors for 7 qubits.

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Variational quantum process tomography of unitaries

Cited by 19 publications

References 36 publications

A noise-robust quantum dynamics learning protocol based on Choi–Jamiolkowski isomorphism: theory and experiment

A noise-robust quantum dynamics learning protocol based on Choi–Jamiolkowski isomorphism: theory and experiment

Learning Quantum Systems

Avoiding barren plateaus in the variational determination of geometric entanglement

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