QAP-Router models qubit routing as dynamic QAP and applies RL with a solution-aware Transformer to cut CNOT counts by 12-30% versus industry compilers on real circuit benchmarks.
Practical and efficient quantum circuit synthesis and transpiling with reinforcement learning
11 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 11representative citing papers
Equivariant RL agent synthesizes near-optimal Clifford circuits up to 30 qubits with lower two-qubit gate counts than Qiskit baselines.
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 compilation tasks.
Quantum circuit partitioning is formalized as a maze path problem, revealing a percolation phase transition that separates partitionable from non-partitionable regimes when the CNOT-to-qubit ratio is near one.
New boundary condition approach for QLBM using one coherent operation on the full boundary, claimed to use fewer resources asymptotically and practically for bounce-back and specular reflection.
A microarchitecture-aware compiler for lattice surgery that exploits C-Phase commutativity to enable concurrent multi-target operations and dynamic event-driven scheduling, cutting execution time by up to 59.7 times versus standard baselines.
Reinforcement learning policy for qubit mapping reduces SWAP overhead by 65-85% versus standard quantum compilers on MQTBench and Queko benchmark circuits.
AlphaCNOT combines reinforcement learning with Monte Carlo Tree Search planning to reduce CNOT gate counts by up to 32% versus heuristics in quantum circuit synthesis.
TeleSABRE extends SABRE to combine intra-core SWAPs with inter-core teleportation, reporting a 28% reduction in inter-core operations on benchmarks for multi-core quantum architectures.
Qudit encoding of the vibrational Hamiltonian yields the most accurate population transfer simulations for CO2 and H2O compared to binary and direct qubit encodings when entangling gate error rates are held equal.
Qiskit is an open-source SDK that supports quantum circuit design, optimization at multiple abstraction levels, execution on hardware, and dynamic quantum-classical computations.
citing papers explorer
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QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning
QAP-Router models qubit routing as dynamic QAP and applies RL with a solution-aware Transformer to cut CNOT counts by 12-30% versus industry compilers on real circuit benchmarks.
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Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthesis
Equivariant RL agent synthesizes near-optimal Clifford circuits up to 30 qubits with lower two-qubit gate counts than Qiskit baselines.
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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 compilation tasks.
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Quantum circuit partition as a maze: emerging percolation transition via path finding
Quantum circuit partitioning is formalized as a maze path problem, revealing a percolation phase transition that separates partitionable from non-partitionable regimes when the CNOT-to-qubit ratio is near one.
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Efficient and Expressive Boundary Conditions in Quantum Lattice Boltzmann Methods
New boundary condition approach for QLBM using one coherent operation on the full boundary, claimed to use fewer resources asymptotically and practically for bounce-back and specular reflection.
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C-Phase-Aware Compilation for Efficient Fault-Tolerant Quantum Execution
A microarchitecture-aware compiler for lattice surgery that exploits C-Phase commutativity to enable concurrent multi-target operations and dynamic event-driven scheduling, cutting execution time by up to 59.7 times versus standard baselines.
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CO-MAP: A Reinforcement Learning Approach to the Qubit Allocation Problem
Reinforcement learning policy for qubit mapping reduces SWAP overhead by 65-85% versus standard quantum compilers on MQTBench and Queko benchmark circuits.
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AlphaCNOT: Learning CNOT Minimization with Model-Based Planning
AlphaCNOT combines reinforcement learning with Monte Carlo Tree Search planning to reduce CNOT gate counts by up to 32% versus heuristics in quantum circuit synthesis.
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TeleSABRE: Layout Synthesis in Multi-Core Quantum Systems with Teleport Interconnect
TeleSABRE extends SABRE to combine intra-core SWAPs with inter-core teleportation, reporting a 28% reduction in inter-core operations on benchmarks for multi-core quantum architectures.
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Simulation of vibrational dynamics using qubits and qudits
Qudit encoding of the vibrational Hamiltonian yields the most accurate population transfer simulations for CO2 and H2O compared to binary and direct qubit encodings when entangling gate error rates are held equal.
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Quantum computing with Qiskit
Qiskit is an open-source SDK that supports quantum circuit design, optimization at multiple abstraction levels, execution on hardware, and dynamic quantum-classical computations.