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IEEE International Conference on Acoustics Speech and Signal Processing

Quantum Variational Bayes on Manifolds

Anna Lopatnikova, Minh‐Ngoc Tran

Published May 5, 2023• 2 citations
Publisher

Abstract

Variational Bayes (VB) has become a widely-used tool for Bayesian inference in statistics and machine learning. VB algorithms are generally restricted to the case where the variational parameter space is Euclidean. Recently, the VB framework has been extended to the case where the variational parameter space is a Riemannian manifold. Such a manifold-based algorithm, by utilizing the natural gradient, can exploit the geometric structures to derive an efficient VB procedure. However, natural gradient in high dimension can be prohibitively computationally expensive to compute. This work proposes using a quantum natural gradient within the manifold VB framework. We demonstrate that the quantum natural gradient does not suffer from the well-known quantum-classical readout computational bottleneck, and hence delivers a highly computational efficiency. The resulting quantum manifold VB algorithm is provably convergent.

DOI
10.1109/icassp49357.2023.10096708

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Variational Bayes (VB) has become a widely-used tool for Bayesian inference in statistics and machine learning. VB algorithms are generally restricted to the case where the variational parameter space is Euclidean. Recently, the VB framework has been extended to the case where the variational parameter space is a Riemannian manifold. Such a manifold-based algorithm, by utilizing the natural gradient, can exploit the geometric structures to derive an efficient VB procedure. However, natural gradient in high dimension can be prohibitively computationally expensive to compute. This work proposes using a quantum natural gradient within the manifold VB framework. We demonstrate that the quantum natural gradient does not suffer from the well-known quantum-classical readout computational bottleneck, and hence delivers a highly computational efficiency. The resulting quantum manifold VB algorithm is provably convergent.
Variational Bayes (VB) is a critical method in machine learning and statistics, underpinning the recent success of Bayesian deep learning. The natural gradient is an essential component of efficient VB estimation, but it is prohibitively computationally expensive in high dimensions. We propose a hybrid quantum-classical algorithm to improve the scaling properties of natural gradient computation and make VB a truly computationally efficient method for Bayesian inference in highdimensional settings. The algorithm leverages matrix inversion from the linear systems algorithm by Harrow, Hassidim, and Lloyd [Phys. Rev. Lett. 103, 15 (2009)] (HHL). We demonstrate that the matrix to be inverted is sparse and the classical-quantum-classical handoffs are sufficiently economical to preserve computational efficiency, making the problem of natural gradient for VB an ideal application of HHL. We prove that, under standard conditions, the VB algorithm with quantum natural gradient is guaranteed to converge. Our regression-based natural gradient formulation is also highly useful for classical VB.
Variational Bayes (VB) is a critical method in machine learning and statistics, underpinning the recent success of Bayesian deep learning. The natural gradient is an essential component of efficient VB estimation, but it is prohibitively computationally expensive in high dimensions. We propose a computationally efficient regression-based method for natural gradient estimation, with convergence guarantees under standard assumptions. The method enables the use of quantum matrix inversion to further speed up VB. We demonstrate that the problem setup fulfills the conditions required for quantum matrix inversion to deliver computational efficiency. The method works with a broad range of statistical models and does not require special-purpose or simplified variational distributions.
Variational Bayes (VB) inference is one of the most important algorithms in machine learning and is widely used in engineering and industry. However, VB is known to suffer from the problem of local optima. In this paper, we generalize VB by using quantum mechanics and propose an algorithm, which we call quantum annealing variational Bayes (QAVB) inference. We then show that QAVB drastically improves the performance of VB in a clustering problem described by a Gaussian mixture model, which is essentially important from the viewpoint of optimization. Finally, we discuss an intuitive understanding of how QAVB works well.
The Variational Quantum Eigensolver (VQE) is one of the most promising algorithms for current quantum devices. It employs a classical optimizer to iteratively update the parameters of a variational quantum circuit in order to search for the ground state of a given Hamiltonian. The efficacy of VQEs largely depends on the optimizer employed. Recent studies suggest that Quantum Natural Gradient Descent (QNG) can achieve faster convergence than vanilla gradient descent (VG), but at the cost of additional quantum resources to estimate Fubini-Study metric tensor in each optimization step. The Fubini-Study metric tensor used in QNG is related to the entire quantum state space and does not incorporate information about the target Hamiltonian. To take advantage of the structure of the Hamiltonian and address the limitation of additional computational cost in QNG, we propose Hamiltonian-aware Quantum Natural Gradient Descent (H-QNG). In H-QNG, we propose to use the Riemannian pullback metric induced from the lower-dimensional subspace spanned by the Hamiltonian terms onto the parameter space. We show that H-QNG inherits the desirable features of both approaches: the low quantum computational cost of VG and the reparameterization-invariance of QNG. We also validate its performance through numerical experiments on molecular Hamiltonians, showing that H-QNG achieves faster convergence to chemical accuracy while requiring fewer quantum computational resources.
Variational Bayes (VB) inference algorithm is used widely to estimate both the parameters and the unobserved hidden variables in generative statistical models. The algorithm—inspired by variational methods used in computational physics—is iterative and can get easily stuck in local minima, even when classical techniques, such as deterministic annealing (DA), are used. We study a VB inference algorithm based on a nontraditional quantum annealing approach—referred to as quantum annealing variational Bayes (QAVB) inference—and show that there is indeed a quantum advantage to QAVB over its classical counterparts. In particular, we show that such better performance is rooted in key quantum mechanics concepts: i) The ground state of the Hamiltonian of a quantum system—defined from the given data—corresponds to an optimal solution for the minimization problem of the variational free energy at very low temperatures; ii) such a ground state can be achieved by a technique paralleling the quantum annealing process; and iii) starting from this ground state, the optimal solution to the VB problem can be achieved by increasing the heat bath temperature to unity, and thereby avoiding local minima introduced by spontaneous symmetry breaking observed in classical physics based VB algorithms. We also show that the update equations of QAVB can be potentially implemented using ⌈log K ⌉ qubits and 𝒪( K ) operations per step, where K is the number of values hidden categorical variables can take. Thus, QAVB can match the time complexity of existing VB algorithms, while delivering higher performance.
The natural gradient descent optimisation technique is an efficient optimising protocol for broad classes of classical and quantum systems that takes the underlying geometry of the parameter manifold into account by means of using either the Fisher information metric of the classical probability distribution function or the Fubini-Study tensor of the associated parametrised quantum states in the consequent update rules. Even though the natural gradient descent procedure utilises the geometry of the space of probability or states, it is, however, insensitive to the measure of parametrised distance on the space of possible outcomes when the corresponding optimising problem is considered for the expectation value of a classical or quantum observable with respect to the probability distribution or the quantum state. In this work, we introduce a generic optimising principle, where the intrinsic geometry of the space of outcomes has been taken into account suitably, either by using an ambient space construction with a base statistical manifold with the usual Fisher information metric (or the Fubini-Study tensor), where the loss hypersurface is embedded to, or by means of a first-principle construction from the overlap of nearby quantum states on the projective Hilbert space. This construction as well as a family of conformal variants yields a form of loss-aware natural gradient updates that rescale the effective step size while preserving the descent direction. We benchmark the resulting optimisers on variational quantum circuit examples and on a classical neural network task, finding that, while the standard natural gradient remains the most robust on average, the proposed conformal schemes can improve best-case convergence in favourable regimes.