A semi-agnostic ansatz with variable structure for quantum machine learning

Abstract

Quantum machine learning -- and specifically Variational Quantum Algorithms (VQAs) -- offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.

20 pages, 14 figures, 1 table, updated to published version

References in corpus (22)

• Variational Quantum Algorithms
• Noisy intermediate-scale quantum (NISQ) algorithms
• Simulated Quantum Computation of Molecular Energies
• The power of quantum neural networks
• The quest for a Quantum Neural Network
• Connecting ansatz expressibility to gradient magnitudes and barren plateaus
• Generalization in quantum machine learning from few training data
• Absence of Barren Plateaus in Quantum Convolutional Neural Networks
• Quantum Circuit Simplification and Level Compaction
• Software Mitigation of Crosstalk on Noisy Intermediate-Scale Quantum Computers
• Effect of barren plateaus on gradient-free optimization
• Diagnosing Barren Plateaus with Tools from Quantum Optimal Control
• Entanglement Devised Barren Plateau Mitigation
• Modelling and Simulating the Noisy Behaviour of Near-term Quantum Computers
• Avoiding barren plateaus using classical shadows
• Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
• Random quantum circuits are approximate unitary $t$-designs in depth $O\left(n{t}^{5+o(1)}\right)$
• Variational Quantum Algorithm for Estimating the Quantum Fisher Information
• Quantum Assisted Simulator
• Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus
• Benchmarking adaptive variational quantum eigensolvers
• Factorization and criticality in finite XXZ systems of arbitrary spin

Cited by in corpus (17)

• Barren Plateaus in Variational Quantum Computing
• Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
• A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
• On the practical usefulness of the Hardware Efficient Ansatz
• Quantum Architecture Search: A Survey
• Distributed quantum architecture search
• Variational quantum computing for quantum simulation: principles, implementations, and challenges
• Application of ZX-calculus to Quantum Architecture Search
• Mitigating Quantum Gate Errors for Variational Eigensolvers Using Hardware-Inspired Zero-Noise Extrapolation
• Deep Circuit Compression for Quantum Dynamics via Tensor Networks
• Cascaded variational quantum eigensolver algorithm
• Advantage of discrete variable representation in variational quantum eigensolvers for vibrational energy calculations
• Expressivity of deterministic quantum computation with one qubit
• Multi-channel convolutional neural quantum embedding
• Sub-universal variational circuits for combinatorial optimization problems
• Generating Generalised Ground-State Ansatzes from Few-Body Examples
• Hamiltonian Expressibility for Ansatz Selection in Variational Quantum Algorithms