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Quantum Architecture Search via Deep Reinforcement Learning
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Quantum Architecture Search via Deep Reinforcement Learning
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Recent advances in quantum computing have drawn considerable attention to building realistic application for and using quantum computers. However, designing a suitable quantum circuit architecture requires expert knowledge. For example, it is non-trivial to design a quantum gate sequence for generating a particular quantum state with as fewer gates as possible. We propose a quantum architecture search framework with the power of deep reinforcement learning (DRL) to address this challenge. In the proposed framework, the DRL agent can only access the Pauli-$X$, $Y$, $Z$ expectation values and a predefined set of quantum operations for learning the target quantum state, and is optimized by the advantage actor-critic (A2C) and proximal policy optimization (PPO) algorithms. We demonstrate a successful generation of quantum gate sequences for multi-qubit GHZ states without encoding any knowledge of quantum physics in the agent. The design of our framework is rather general and can be employed with other DRL architectures or optimization methods to study gate synthesis and compilation for many quantum states.
Forward citations
Cited by 11 Pith papers
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Generative Quantum-inspired Kolmogorov-Arnold Eigensolver
GQKAE uses quantum-inspired Kolmogorov-Arnold networks to reduce parameters by 66% in generative quantum eigensolvers while achieving chemical accuracy on H4, N2, LiH, and other molecules.
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Magic-Informed Quantum Architecture Search
A Monte Carlo Tree Search with GNN-based magic estimation biases quantum circuit search toward target nonstabilizerness levels and yields better results on ground-state energy and state approximation problems.
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Replay-buffer engineering for noise-robust quantum circuit optimization
Treating the replay buffer as a central lever in RL for quantum circuit optimization yields 4-32x sample efficiency gains, up to 67.5% faster episodes, and 85-90% fewer steps to accuracy on noisy molecular and compila...
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Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search
Progressive widening MCTS with sampling action space automates quantum circuit design, cutting evaluations 10-100x and CNOT gates up to 3x versus prior MCTS on chemistry and linear-equation tasks.
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Hamiltonian-reconstruction distance as a success metric for the Variational Quantum Eigensolver
Hamiltonian-reconstruction distance is shown to correlate with ground-state fidelity and serves as a practical success metric for VQE on 1D and 2D Ising models in simulation and on trapped-ion hardware.
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Observable Geometry for Effective Quantum Circuits
A stabilizer-overlap score built from a Hamiltonian's eigenspaces predicts which variational circuit generators are redundant and should be removed.
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Zero-shot Quantum Neural Architecture Search
MZeQAS accelerates quantum architecture search for VQAs by replacing full training of candidates with a zero-shot performance estimate derived from QNTK Gram-matrix convergence.
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Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation
The paper introduces Recursive QLSTM via metacore recursion, numerically tests variants on sequence lengths, and offers theoretical arguments for better temporal propagation.
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Hybrid Quantum-Classical Neural Architecture Search
Demonstrates FLOPs-aware neural architecture search for hybrid quantum-classical neural networks to produce accurate yet computationally efficient models suitable for NISQ hardware.
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A Review of Variational Quantum Algorithms: Insights into Fault-Tolerant Quantum Computing
A literature review of VQAs covering ansatz design, classical optimization, barren plateaus, error mitigation strategies, and theoretical adaptations for fault-tolerant quantum computing.
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Recent Advances in Quantum Architecture Search
A survey of core concepts, representative methodologies, applications, challenges, and future directions in Quantum Architecture Search for variational quantum algorithms.
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