Quantum circuit architecture search for variational quantum algorithms

Abstract

Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS can not only alleviate the influence of quantum noise and barren plateaus, but also outperforms VQAs with pre-selected ansatze.

Final version. See also a concurrent paper [arXiv:2010.08561]

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Cited by in corpus (49)

• Barren Plateaus in Variational Quantum Computing
• KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search
• Quantum Architecture Search: A Survey
• Absence of barren plateaus in finite local-depth circuits with long-range entanglement
• Efficient variational synthesis of quantum circuits with coherent multi-start optimization
• Training variational quantum algorithms with random gate activation
• Topology-Driven Quantum Architecture Search Framework
• Efficient estimation of trainability for variational quantum circuits
• Automated Quantum Circuit Design with Nested Monte Carlo Tree Search
• Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning
• Distributed quantum architecture search
• Quantum algorithms for scientific computing
• GSQAS: Graph Self-supervised Quantum Architecture Search
• EHA: Entanglement-variational Hardware-efficient Ansatz for Eigensolvers
• A Toffoli Gate Decomposition via Echoed Cross-Resonance Gates
• Estimating the randomness of quantum circuit ensembles up to 50 qubits
• Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator
• Enhancing variational quantum state diagonalization using reinforcement learning techniques
• Sequential optimal selection of a single-qubit gate and its relation to barren plateau in parameterized quantum circuits
• Subsystem Information Capacity in Random Circuits and Hamiltonian Dynamics
• Universal imaginary-time critical dynamics on a quantum computer
• A quantum information theoretic analysis of reinforcement learning-assisted quantum architecture search
• Quantum Architecture Search for Quantum Monte Carlo Integration via Conditional Parameterized Circuits with Application to Finance
• Application of ZX-calculus to Quantum Architecture Search
• Framework for Learning and Control in the Classical and Quantum Domains
• Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search
• Exploring the topological sector optimization on quantum computers
• Cascaded variational quantum eigensolver algorithm
• Advantage of discrete variable representation in variational quantum eigensolvers for vibrational energy calculations
• Restricting to the chip architecture maintains the quantum neural network accuracy
• Hamiltonian-based graph-state ansatz for variational quantum algorithms
• Leveraging commuting groups for an efficient variational Hamiltonian ansatz
• Learning to learn with an evolutionary strategy applied to variational quantum algorithms
• Variational post-selection for ground states and thermal states simulation
• Evaluating Variational Quantum Circuit Architectures for Distributed Quantum Computing
• Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges
• An Efficient Gradient Sensitive Alternate Framework for VQE with Variable Ansatz
• Multi-target quantum compilation algorithm
• Improving Parameter Training for VQEs by Sequential Hamiltonian Assembly
• Generating Generalised Ground-State Ansatzes from Few-Body Examples
• Benchmarking of quantum fidelity kernels for Gaussian process regression
• QAS-QTNs: Curriculum Reinforcement Learning-Driven Quantum Architecture Search for Quantum Tensor Networks
• Batched Line Search Strategy for Navigating through Barren Plateaus in Quantum Circuit Training
• Direct entanglement ansatz learning (DEAL) with ZNE on error-prone superconducting qubits
• Designing a Machine Learning-Driven, Cross-Hardware Emulator for Noisy Quantum Computers with Gate-Based Protocols
• Exploiting biased noise in variational quantum models
• Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern
• Hamiltonian Expressibility for Ansatz Selection in Variational Quantum Algorithms
• Quantum feature-map learning with reduced resource overhead