Variational hybrid quantum-classical algorithms (VHQCAs) have the potential to be useful in the era of near-term quantum computing. However, recently there has been concern regarding the number of measurements needed for convergence of VHQCAs. Here, we address this concern by investigating the classical optimizer in VHQCAs. We introduce a novel optimizer called individual Coupled Adaptive Number of Shots (iCANS). This adaptive optimizer frugally selects the number of measurements (i.e., number of shots) both for a given iteration and for a given partial derivative in a stochastic gradient descent. We numerically simulate the performance of iCANS for the variational quantum eigensolver and for variational quantum compiling, with and without noise. In all cases, and especially in the noisy case, iCANS tends to out-perform state-of-the-art optimizers for VHQCAs. We therefore believe this adaptive optimizer will be useful for realistic VHQCA implementations, where the number of measurements is limited.
However, there is a legitimate concern over whether or not the number of times a quantum state must be prepared and measured (i.e. the number of “shots” taken on the quantum device) in order for the variational algorithm to converge will be prohibitive. This and related questions have sparked a recent interest in researching which classical optimizer should be used in variational algorithms.
In this work, we propose a shot-frugal optimization strategy for variational algorithms that dynamically adjusts the number of shots expended (and thus the precision) for each update step in a stochastic gradient descent procedure that we name iCANS (individual Coupled Adaptive Number of Shots). Allocating measurement resources separately for each component of the gradient estimates, iCANS takes advantage of very inexpensive update steps requiring few shots early in the optimization and smoothly increases the number of shots used in order to achieve a high precision optimization result. We present comparisons between iCANS and other optimizers that have been discussed for the context of variational algorithms and find that iCANS often performs better than these other methods.
 J. R. McClean, J. Romero, R. Babbush, and A. Aspuru-Guzik, The theory of variational hybrid quantum-classical algorithms, New Journal of Physics 18, 023023 (2016).
 A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O'brien, A variational eigenvalue solver on a photonic quantum processor, Nature Communications 5, 4213 (2014).
 J. Romero, J. P. Olson, and A. Aspuru-Guzik, Quantum autoencoders for efficient compression of quantum data, Quantum Science and Technology 2, 045001 (2017).
 A. Arrasmith, L. Cincio, A. T. Sornborger, W. H. Zurek, and P. J. Coles, Variational consistent histories as a hybrid algorithm for quantum foundations, Nature Communications 10, 3438 (2019).
 T. Jones, S. Endo, S. McArdle, X. Yuan, and S. C. Benjamin, Variational quantum algorithms for discovering hamiltonian spectra, Physical Review A 99, 062304 (2019).
 C. Kokail, C. Maier, R. van Bijnen, T. Brydges, M. Joshi, P. Jurcevic, C. Muschik, P. Silvi, R. Blatt, C. Roos, et al., Self-verifying variational quantum simulation of lattice models, Nature 569, 355 (2019).
 K. Sharma, S. Khatri, M. Cerezo, and P. Coles, Noise resilience of variational quantum compiling, New Journal of Physics (2020), 10.1088/1367-2630/ab784c.
 J. Carolan, M. Mosheni, J. P. Olson, M. Prabhu, C. Chen, D. Bunandar, N. C. Harris, F. N. Wong, M. Hochberg, S. Lloyd, et al., Variational quantum unsampling on a quantum photonic processor, arXiv:1904.10463 (2019).
 S. McArdle, T. Jones, S. Endo, Y. Li, S. C. Benjamin, and X. Yuan, Variational ansatz-based quantum simulation of imaginary time evolution, npj Quantum Information 5, 1 (2019).
 C. Cirstoiu, Z. Holmes, J. Iosue, L. Cincio, P. J. Coles, and A. Sornborger, Variational fast forwarding for quantum simulation beyond the coherence time, arXiv:1910.04292 (2019).
 Y. Cao, J. Romero, J. P. Olson, M. Degroote, P. D. Johnson, M. Kieferová, I. D. Kivlichan, T. Menke, B. Peropadre, N. P. Sawaya, et al., Quantum chemistry in the age of quantum computing, Chemical reviews (2018), 10.1021/acs.chemrev.8b00803.
 A. F. Izmaylov, T.-C. Yen, R. A. Lang, and V. Verteletskyi, Unitary partitioning approach to the measurement problem in the variational quantum eigensolver method, arXiv:1907.09040 (2019).
 T.-C. Yen, V. Verteletskyi, and A. F. Izmaylov, Measuring all compatible operators in one series of a single-qubit measurements using unitary transformations, arXiv:1907.09386 (2019).
 P. Gokhale, O. Angiuli, Y. Ding, K. Gui, T. Tomesh, M. Suchara, M. Martonosi, and F. T. Chong, Minimizing state preparations in variational quantum eigensolver by partitioning into commuting families, arXiv:1907.13623 (2019).
 W. J. Huggins, J. McClean, N. Rubin, Z. Jiang, N. Wiebe, K. B. Whaley, and R. Babbush, Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers, arXiv:1907.13117 (2019).
 G. Verdon, M. Broughton, J. R. McClean, K. J. Sung, R. Babbush, Z. Jiang, H. Neven, and M. Mohseni, Learning to learn with quantum neural networks via classical neural networks, arXiv:1907.05415 (2019).
 R. M. Parrish, J. T. Iosue, A. Ozaeta, and P. L. McMahon, A Jacobi diagonalization and Anderson acceleration algorithm for variational quantum algorithm parameter optimization, arXiv:1904.03206 (2019).
 L. Balles, J. Romero, and P. Hennig, in Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence (UAI) (2017) pp. 410–419.
 J. C. Spall, Multivariate stochastic approximation using a simultaneous perturbation gradient approximation, IEEE transactions on automatic control 37, 332 (1992).
 J. S. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl, in Advances in Neural Information Processing Systems 24 (2011) pp. 2546–2554.
 J. C. Spall, Implementation of the simultaneous perturbation algorithm for stochastic optimization, IEEE Transactions on aerospace and electronic systems 34, 817 (1998).
 A. Kandala, A. Mezzacapo, K. Temme, M. Takita, M. Brink, J. M. Chow, and J. M. Gambetta, Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets, Nature 549, 242 (2017).
 M. J. Powell, An efficient method for finding the minimum of a function of several variables without calculating derivatives, The Computer Journal 7, 155 (1964).
 R. P. Brent, Algorithms for Minimization Without Derivatives (Dover Publications, 2013).
 IBM Q 16 Melbourne backend specification, https://github.com/Qiskit/ibmq-device-information/tree/master/backends/melbourne/V1 (2018).
 R. Sweke, F. Wilde, J. Meyer, M. Schuld, P. K. Fährmann, B. Meynard-Piganeau, and J. Eisert, Stochastic gradient descent for hybrid quantum-classical optimization, arXiv:1910.01155 (2019).
 Igor O. Sokolov, Panagiotis Kl. Barkoutsos, Lukas Moeller, Philippe Suchsland, Guglielmo Mazzola, and Ivano Tavernelli, "Microcanonical and finite-temperature ab initio molecular dynamics simulations on quantum computers", Physical Review Research 3 1, 013125 (2021).
 Aram W. Harrow and John C. Napp, "Low-Depth Gradient Measurements Can Improve Convergence in Variational Hybrid Quantum-Classical Algorithms", Physical Review Letters 126 14, 140502 (2021).
 Philip Mocz and Aaron Szasz, "Toward Cosmological Simulations of Dark Matter on Quantum Computers", The Astrophysical Journal 910 1, 29 (2021).
 Andrea Mari, Thomas R. Bromley, and Nathan Killoran, "Estimating the gradient and higher-order derivatives on quantum hardware", Physical Review A 103 1, 012405 (2021).
 Cristina Cîrstoiu, Zoë Holmes, Joseph Iosue, Lukasz Cincio, Patrick J. Coles, and Andrew Sornborger, "Variational fast forwarding for quantum simulation beyond the coherence time", npj Quantum Information 6 1, 82 (2020).
 Wim Lavrijsen, Ana Tudor, Juliane Muller, Costin Iancu, and Wibe de Jong, 2020 IEEE International Conference on Quantum Computing and Engineering (QCE) 267 (2020) ISBN:978-1-7281-8969-7.
 Ryan Sweke, Frederik Wilde, Johannes Meyer, Maria Schuld, Paul K. Faehrmann, Barthélémy Meynard-Piganeau, and Jens Eisert, "Stochastic gradient descent for hybrid quantum-classical optimization", Quantum 4, 314 (2020).
 Tyler Volkoff and Patrick J Coles, "Large gradients via correlation in random parameterized quantum circuits", Quantum Science and Technology 6 2, 025008 (2021).
 M. Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J. Coles, "Cost function dependent barren plateaus in shallow parametrized quantum circuits", Nature Communications 12 1, 1791 (2021).
 Chris Cade, Lana Mineh, Ashley Montanaro, and Stasja Stanisic, "Strategies for solving the Fermi-Hubbard model on near-term quantum computers", Physical Review B 102 23, 235122 (2020).
 Carlos Bravo-Prieto, Diego García-Martín, and José I. Latorre, "Quantum singular value decomposer", Physical Review A 101 6, 062310 (2020).
 Ophelia Crawford, Barnaby van Straaten, Daochen Wang, Thomas Parks, Earl Campbell, and Stephen Brierley, "Efficient quantum measurement of Pauli operators in the presence of finite sampling error", Quantum 5, 385 (2021).
 Sukin Sim, Jonathan Romero, Jérôme F Gonthier, and Alexander A Kunitsa, "Adaptive pruning-based optimization of parameterized quantum circuits", arXiv:2010.00629, Quantum Science and Technology 6 2, 025019 (2021).
 Guoming Wang, Dax Enshan Koh, Peter D. Johnson, and Yudong Cao, "Minimizing Estimation Runtime on Noisy Quantum Computers", PRX Quantum 2 1, 010346 (2021).
 Carlos Bravo-Prieto, Ryan LaRose, M. Cerezo, Yigit Subasi, Lukasz Cincio, and Patrick J. Coles, "Variational Quantum Linear Solver", arXiv:1909.05820.
 M. Cerezo, Kunal Sharma, Andrew Arrasmith, and Patrick J. Coles, "Variational Quantum State Eigensolver", arXiv:2004.01372.
 Kunal Sharma, M. Cerezo, Lukasz Cincio, and Patrick J. Coles, "Trainability of Dissipative Perceptron-Based Quantum Neural Networks", arXiv:2005.12458.
 Kunal Sharma, Sumeet Khatri, M. Cerezo, and Patrick J. Coles, "Noise resilience of variational quantum compiling", New Journal of Physics 22 4, 043006 (2020).
 Seth Lloyd, Maria Schuld, Aroosa Ijaz, Josh Izaac, and Nathan Killoran, "Quantum embeddings for machine learning", arXiv:2001.03622.
 Bálint Koczor and Simon C. Benjamin, "Quantum natural gradient generalised to non-unitary circuits", arXiv:1912.08660.
 Andrew Arrasmith, Lukasz Cincio, Rolando D. Somma, and Patrick J. Coles, "Operator Sampling for Shot-frugal Optimization in Variational Algorithms", arXiv:2004.06252.
 Barnaby van Straaten and Bálint Koczor, "Measurement cost of metric-aware variational quantum algorithms", arXiv:2005.05172.
 Kevin J. Sung, Jiahao Yao, Matthew P. Harrigan, Nicholas C. Rubin, Zhang Jiang, Lin Lin, Ryan Babbush, and Jarrod R. McClean, "Using models to improve optimizers for variational quantum algorithms", Quantum Science and Technology 5 4, 044008 (2020).
 Bálint Koczor, Suguru Endo, Tyson Jones, Yuichiro Matsuzaki, and Simon C. Benjamin, "Variational-state quantum metrology", New Journal of Physics 22 8, 083038 (2020).
 M. Cerezo, Alexander Poremba, Lukasz Cincio, and Patrick J. Coles, "Variational Quantum Fidelity Estimation", arXiv:1906.09253.
 Dan-Bo Zhang and Tao Yin, "Collective optimization for variational quantum eigensolvers", Physical Review A 101 3, 032311 (2020).
 Eric Sillekens, Wenting Yi, Daniel Semrau, Alessandro Ottino, Boris Karanov, Sujie Zhou, Kevin Law, Jack Chen, Domanic Lavery, Lidia Galdino, Polina Bayvel, and Robert I. Killey, "Experimental Demonstration of Learned Time-Domain Digital Back-Propagation", arXiv:1912.12197.
The above citations are from Crossref's cited-by service (last updated successfully 2021-04-21 00:18:21) and SAO/NASA ADS (last updated successfully 2021-04-21 00:18:23). The list may be incomplete as not all publishers provide suitable and complete citation data.
This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions.