Differentiable Quantum Architecture Search

Abstract

Quantum architecture search (QAS) is the process of automating architecture engineering of quantum circuits. It has been desired to construct a powerful and general QAS platform which can significantly accelerate current efforts to identify quantum advantages of error-prone and depth-limited quantum circuits in the NISQ era. Hereby, we propose a general framework of differentiable quantum architecture search (DQAS), which enables automated designs of quantum circuits in an end-to-end differentiable fashion. We present several examples of circuit design problems to demonstrate the power of DQAS. For instance, unitary operations are decomposed into quantum gates, noisy circuits are re-designed to improve accuracy, and circuit layouts for quantum approximation optimization algorithm are automatically discovered and upgraded for combinatorial optimization problems. These results not only manifest the vast potential of DQAS being an essential tool for the NISQ application developments, but also present an interesting research topic from the theoretical perspective as it draws inspirations from the newly emerging interdisciplinary paradigms of differentiable programming, probabilistic programming, and quantum programming.

9.1 pages + Appendix, 5 figures

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Cited by in corpus (47)

• Barren Plateaus in Variational Quantum Computing
• Solving nonlinear differential equations with differentiable quantum circuits
• QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
• TensorCircuit: a Quantum Software Framework for the NISQ Era
• Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
• Recent advances for quantum classifiers
• Neural Predictor based Quantum Architecture Search
• Variational Quantum-Neural Hybrid Eigensolver
• KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search
• Quantum approximate optimization via learning-based adaptive optimization
• Quantum Architecture Search: A Survey
• Absence of barren plateaus in finite local-depth circuits with long-range entanglement
• Quantum Architecture Search with Meta-learning
• Quantum Architecture Search via Deep Reinforcement Learning
• Probing many-body localization by excited-state VQE
• Hierarchical quantum circuit representations for neural architecture search
• Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits
• Training variational quantum algorithms with random gate activation
• Topology-Driven Quantum Architecture Search Framework
• Neural network encoded variational quantum algorithms
• Schrödinger-Heisenberg Variational Quantum Algorithms
• Automated Quantum Circuit Design with Nested Monte Carlo Tree Search
• Distributed quantum architecture search
• The quantum cost function concentration dependency on the parametrization expressivity
• Quantum algorithms for scientific computing
• GSQAS: Graph Self-supervised Quantum Architecture Search
• Quantum Chemistry Calculations using Energy Derivatives on Quantum Computers
• Reinforcement learning-based architecture search for quantum machine learning
• Enhancing variational quantum state diagonalization using reinforcement learning techniques
• Quantum circuit synthesis via a random combinatorial search
• Sequential optimal selection of a single-qubit gate and its relation to barren plateau in parameterized quantum circuits
• Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic
• Subsystem Information Capacity in Random Circuits and Hamiltonian Dynamics
• Quantum data encoding as a distinct abstraction layer in the design of quantum circuits
• A quantum information theoretic analysis of reinforcement learning-assisted quantum architecture search
• Universal imaginary-time critical dynamics on a quantum computer
• Stabilizer ground states for simulating quantum many-body physics: theory, algorithms, and applications
• Quantum Architecture Search for Quantum Monte Carlo Integration via Conditional Parameterized Circuits with Application to Finance
• Application of ZX-calculus to Quantum Architecture Search
• Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search
• Exploring the topological sector optimization on quantum computers
• Hamiltonian-based graph-state ansatz for variational quantum algorithms
• Variational post-selection for ground states and thermal states simulation
• Learning to learn with an evolutionary strategy applied to variational quantum algorithms
• Leveraging commuting groups for an efficient variational Hamiltonian ansatz
• Variational LOCC-assisted quantum circuits for long-range entangled states
• Differentiable Architecture Search for Adversarially Robust Quantum Computer Vision