arXiv AI
AlphaCNOT: Learning CNOT Minimization with Model-Based Planning
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arXiv AI
Aug 20
AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL
AlphaClifford is a model‑based reinforcement learning framework that uses Monte Carlo Tree Search to synthesize Clifford circuits from the H, S, and CNOT gate set. By modeling the state space with the algebraic properties of the symplectic group, it consistently reduces total and two‑qubit gate counts compared to existing heuristics. The approach also extends to hardware‑constrained transpilation and serves as a post‑synthesis optimizer in a full Clifford+T pipeline.
arXiv Machine Learning
Oct 9
Beyond QAOA: A Review of AI and Quantum Computing for Adaptive Combinatorial Optimization
The review examines how machine learning can adapt quantum combinatorial optimization workflows by learning decisions such as formulation, penalties, shot budgets, and backend selection. It categorizes the literature into three paradigms—AI for quantum optimization, quantum for AI-driven optimization, and AI‑quantum co‑optimization—and analyzes 119 papers, finding stronger evidence for AI assisting quantum methods than for quantum enhancing learned optimizers. The authors highlight limited experimental controls, introduce an M0‑M5 evidence hierarchy, and argue that scaling is a systems problem requiring careful allocation of classical and quantum resources.
arXiv AI
5d ago
AI-Assisted Discovery of CNOT Decompositions for Quantum Permutations on Constrained Topologies
arXiv:2610. 37045v1 Announce Type: new Abstract: Efficient quantum circuit compilation is challenging for quantum computers, where limited qubit connectivity can introduce substantial overhead in the implementation of logical operations.
arXiv Machine Learning
Sep 23
Bridge of $\Psi$'s: Quantum Circuit Optimization with Schr\"odinger Bridges
Bridge of Ψ's (BOPS) is a generative model that learns to transform quantum circuits into equivalent, optimized versions using Schr"odinger bridges and a custom denoiser architecture. Trained on data engineered to challenge existing optimizers, BOPS achieves a 2.46× reduction in gate count and a 2.45× reduction in depth on 8‑qubit, 64‑depth Clifford+$T$ circuits, outperforming nine baseline optimizers. This work demonstrates the first successful application of generative machine learning to quantum circuit optimization, expanding the quantum compilation stack with learned techniques.
arXiv Machine Learning
Oct 19
Learning angles for quantum approximate optimization
The paper presents a method for learning the mapping from combinatorial optimization problem instances to high‑quality variational angles for the Quantum Approximate Optimization Algorithm (QAOA). By training models that predict these angles, the authors replace the expensive per‑instance classical optimization with a single forward pass, achieving near‑identical solution quality while being several orders of magnitude faster. The learned models generalize to unseen graph topologies, and experiments on quantum hardware confirm that the sampled solutions are indistinguishable from those obtained with traditional solver‑derived angles.
arXiv Machine Learning
5d ago
Fully quantum training of parametric circuits with Quantum Hamiltonian Descent
The paper introduces a fully quantum method, Quantum Hamiltonian Descent (QHD), for training parametric quantum circuits by treating circuit parameters as quantum particle positions in a potential defined by the cost function. It demonstrates that, for smooth periodic cost functions, the simulation error is bounded independently of grid resolution and grows at most linearly with the number of parameters, while the dynamics depend on a single schedule curve that can be optimized to reduce gate count. Numerical simulations on benchmark functions and circuits show that schedules designed in this way significantly improve the probability of locating the global minimum compared to conventional approaches.