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Diagnosing Barren Plateaus with Tools from Quantum Optimal Control

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Martin Larocca1,2, Piotr Czarnik2, Kunal Sharma3,2, Gopikrishnan Muraleedharan2, Patrick J. Coles2, and M. Cerezo4,5

1Departamento de Física “J. J. Giambiagi” and IFIBA, FCEyN, Universidad de Buenos Aires, 1428 Buenos Aires, Argentina
2Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
3Hearne Institute for Theoretical Physics and Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA USA
4Information Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
5Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

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Abstract

Variational Quantum Algorithms (VQAs) have received considerable attention due to their potential for achieving near-term quantum advantage. However, more work is needed to understand their scalability. One known scaling result for VQAs is barren plateaus, where certain circumstances lead to exponentially vanishing gradients. It is common folklore that problem-inspired ansatzes avoid barren plateaus, but in fact, very little is known about their gradient scaling. In this work we employ tools from quantum optimal control to develop a framework that can diagnose the presence or absence of barren plateaus for problem-inspired ansatzes. Such ansatzes include the Quantum Alternating Operator Ansatz (QAOA), the Hamiltonian Variational Ansatz (HVA), and others. With our framework, we prove that avoiding barren plateaus for these ansatzes is not always guaranteed. Specifically, we show that the gradient scaling of the VQA depends on the degree of controllability of the system, and hence can be diagnosed through the dynamical Lie algebra $mathfrak{g}$ obtained from the generators of the ansatz. We analyze the existence of barren plateaus in QAOA and HVA ansatzes, and we highlight the role of the input state, as different initial states can lead to the presence or absence of barren plateaus. Taken together, our results provide a framework for trainability-aware ansatz design strategies that do not come at the cost of extra quantum resources. Moreover, we prove no-go results for obtaining ground states with variational ansatzes for controllable system such as spin glasses. Our work establishes a link between the existence of barren plateaus and the scaling of the dimension of $mathfrak{g}$.

In this work, we provide a novel framework for diagnosing the presence or absence of Barren Plateaus (BPs) in variational quantum algorithms and quantum machine learning models. Our work leverages tools from quantum control theory to connect the scaling of the cost-function gradients with the dimension of the so-called dynamical Lie algebra (DLA), the Lie closure of the generators of the parametrized quantum circuit. Our results greatly improve our understanding of the BP phenomenon, allowing us to predict their happening in a wide range of scenarios that were not covered by previous literature. Taken together, this work provides novel strategies for an active trainability-aware design of quantum neural network architectures, and showcases the importance of the DLA in variational quantum computing.

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► References

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[2] Pejman Jouzdani, Calvin W. Johnson, Eduardo R. Mucciolo, and Ionel Stetcu, “An Alternative Approach to Quantum Imaginary Time Evolution”, arXiv:2208.10535.

[3] Christiane P. Koch, Ugo Boscain, Tommaso Calarco, Gunther Dirr, Stefan Filipp, Steffen J. Glaser, Ronnie Kosloff, Simone Montangero, Thomas Schulte-Herbrüggen, Dominique Sugny, and Frank K. Wilhelm, “Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe”, arXiv:2205.12110.

[4] Stefan H. Sack, Raimel A. Medina, Alexios A. Michailidis, Richard Kueng, and Maksym Serbyn, “Avoiding Barren Plateaus Using Classical Shadows”, PRX Quantum 3 2, 020365 (2022).

[5] Andy C. Y. Li, M. Sohaib Alam, Thomas Iadecola, Ammar Jahin, Doga Murat Kurkcuoglu, Richard Li, Peter P. Orth, A. Barış Özgüler, Gabriel N. Perdue, and Norm M. Tubman, “Benchmarking variational quantum eigensolvers for the square-octagon-lattice Kitaev model”, arXiv:2108.13375.

[6] Nic Ezzell, Elliott M. Ball, Aliza U. Siddiqui, Mark M. Wilde, Andrew T. Sornborger, Patrick J. Coles, and Zoë Holmes, “Quantum Mixed State Compiling”, arXiv:2209.00528.

[7] Matthias C. Caro, Hsin-Yuan Huang, M. Cerezo, Kunal Sharma, Andrew Sornborger, Lukasz Cincio, and Patrick J. Coles, “Generalization in quantum machine learning from few training data”, Nature Communications 13, 4919 (2022).

[8] Martin Larocca, Nathan Ju, Diego García-Martín, Patrick J. Coles, and M. Cerezo, “Theory of overparametrization in quantum neural networks”, arXiv:2109.11676.

[9] Louis Schatzki, Andrew Arrasmith, Patrick J. Coles, and M. Cerezo, “Entangled Datasets for Quantum Machine Learning”, arXiv:2109.03400.

[10] Junyu Liu, Khadijeh Najafi, Kunal Sharma, Francesco Tacchino, Liang Jiang, and Antonio Mezzacapo, “An analytic theory for the dynamics of wide quantum neural networks”, arXiv:2203.16711.

[11] Yanzhu Chen, Linghua Zhu, Chenxu Liu, Nicholas J. Mayhall, Edwin Barnes, and Sophia E. Economou, “How Much Entanglement Do Quantum Optimization Algorithms Require?”, arXiv:2205.12283.

[12] Supanut Thanasilp, Samson Wang, Nhat A. Nghiem, Patrick J. Coles, and M. Cerezo, “Subtleties in the trainability of quantum machine learning models”, arXiv:2110.14753.

[13] Annie E. Paine, Vincent E. Elfving, and Oleksandr Kyriienko, “Quantum Kernel Methods for Solving Differential Equations”, arXiv:2203.08884.

[14] Frederic Sauvage, Martin Larocca, Patrick J. Coles, and M. Cerezo, “Building spatial symmetries into parameterized quantum circuits for faster training”, arXiv:2207.14413.

[15] Daniel Bultrini, Samson Wang, Piotr Czarnik, Max Hunter Gordon, M. Cerezo, Patrick J. Coles, and Lukasz Cincio, “The battle of clean and dirty qubits in the era of partial error correction”, arXiv:2205.13454.

[16] Massimiliano Incudini, Francesco Martini, and Alessandra Di Pierro, “Structure Learning of Quantum Embeddings”, arXiv:2209.11144.

[17] Andi Gu, Angus Lowe, Pavel A. Dub, Patrick J. Coles, and Andrew Arrasmith, “Adaptive shot allocation for fast convergence in variational quantum algorithms”, arXiv:2108.10434.

[18] Nishant Jain, Brian Coyle, Elham Kashefi, and Niraj Kumar, “Graph neural network initialisation of quantum approximate optimisation”, arXiv:2111.03016.

[19] Alejandro Sopena, Max Hunter Gordon, Diego García-Martín, Germán Sierra, and Esperanza López, “Algebraic Bethe Circuits”, arXiv:2202.04673.

[20] Antonio Anna Mele, Glen Bigan Mbeng, Giuseppe Ernesto Santoro, Mario Collura, and Pietro Torta, “Avoiding barren plateaus via transferability of smooth solutions in Hamiltonian Variational Ansatz”, arXiv:2206.01982.

[21] Bingzhi Zhang, Akira Sone, and Quntao Zhuang, “Quantum computational phase transition in combinatorial problems”, npj Quantum Information 8, 87 (2022).

[22] John Napp, “Quantifying the barren plateau phenomenon for a model of unstructured variational ansätze”, arXiv:2203.06174.

[23] Xiaozhen Ge, Re-Bing Wu, and Herschel Rabitz, “The Optimization Landscape of Hybrid Quantum-Classical Algorithms: from Quantum Control to NISQ Applications”, arXiv:2201.07448.

[24] Roeland Wiersema and Nathan Killoran, “Optimizing quantum circuits with Riemannian gradient flow”, arXiv:2202.06976.

[25] Kishor Bharti, Tobias Haug, Vlatko Vedral, and Leong-Chuan Kwek, “NISQ Algorithm for Semidefinite Programming”, arXiv:2106.03891.

[26] Zeyi Tao, Jindi Wu, Qi Xia, and Qun Li, “LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks”, arXiv:2205.02666.

[27] Kaining Zhang, Min-Hsiu Hsieh, Liu Liu, and Dacheng Tao, “Gaussian initializations help deep variational quantum circuits escape from the barren plateau”, arXiv:2203.09376.

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[41] L. C. G. Govia, C. Poole, M. Saffman, and H. K. Krovi, “Freedom of the mixer rotation axis improves performance in the quantum approximate optimization algorithm”, Physical Review A 104 6, 062428 (2021).

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On Crossref’s cited-by service no data on citing works was found (last attempt 2022-09-30 14:32:31).

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