mlsanityData to 7 Oct 2026

Quantum Circuit Optimization with AlphaTensor

arXiv 2402.14396 ↗First posted 22 Feb 2024quant-ph cs.LGcited by 36

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Abstract (arXiv)

A key challenge in realizing fault-tolerant quantum computers is circuit optimization. Focusing on the most expensive gates in fault-tolerant quantum computation (namely, the T gates), we address the problem of T-count optimization, i.e., minimizing the number of T gates that are needed to implement a given circuit. To achieve this, we develop AlphaTensor-Quantum, a method based on deep reinforcement learning that exploits the relationship between optimizing T-count and tensor decomposition. Unlike existing methods for T-count optimization, AlphaTensor-Quantum can incorporate domain-specific knowledge about quantum computation and leverage gadgets, which significantly reduces the T-count of the optimized circuits. AlphaTensor-Quantum outperforms the existing methods for T-count optimization on a set of arithmetic benchmarks (even when compared without making use of gadgets). Remarkably, it discovers an efficient algorithm akin to Karatsuba's method for multiplication in finite fields. AlphaTensor-Quantum also finds the best human-designed solutions for relevant arithmetic computations used in Shor's algorithm and for quantum chemistry simulation, thus demonstrating it can save hundreds of hours of research by optimizing relevant quantum circuits in a fully automated way.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). Builds on 8 of its 82 references that are in the map. First posted on arXiv 22 Feb 2024.

Builds on

8 of 82 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019Axial Attention in Multidimensional Transformerscited by 350
  3. 2021Reinforcement learning for optimization of variational quantum circuit architecturescited by 76
  4. 2021Learning and Planning in Complex Action Spacescited by 76
  5. 2021Quantum Architecture Search via Deep Reinforcement Learningcited by 58
  6. 2019Quantum Computing: Lecture Notescited by 13
  7. 2022Compiler Optimization for Quantum Computing Using Reinforcement Learningcited by 11
  8. 2022Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuitscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 12; 2026: 20.

36 papers.

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  1. 2025AlphaEvolve: A coding agent for scientific and algorithmic discoverycited by 763
  2. 2024Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learningcited by 21
  3. 2025Artificial intelligence for representing and characterizing quantum systemscited by 9
  4. 2024Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospectscited by 8
  5. 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardwarecited by 7
  6. 2025TensorRL-QAS: Reinforcement learning with tensor networks for improved quantum architecture searchcited by 6
  7. 2025CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphscited by 5
  8. 2025Quantum computing and artificial intelligence: status and perspectivescited by 5
  9. 2025Optimizing the non-Clifford-count in unitary synthesis using Reinforcement Learningcited by 5
  10. 2025Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networkscited by 2
  11. 2025BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture searchcited by 2
  12. 2025TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQEcited by 2
24 more
  1. 2026AlphaCNOT: Learning CNOT Minimization with Model-Based Planningcited by 2
  2. 2026Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthesiscited by 2
  3. 2026CDRL: Certification-Driven Reinforcement Learning for Neutrino Flavor Model Discoverycited by 2
  4. 2025CayleyPy Growth: Efficient growth computations and hundreds of new conjectures on Cayley graphs (Brief version)cited by 1
  5. 2025Nonstabilizerness Estimation using Graph Neural Networkscited by 1
  6. 2026When Does Adaptation Win? Scaling Laws for Meta-Learning in Quantum Controlcited by 1
  7. 2026Quantum Circuit Pre-Synthesis: Learning Local Edits to Reduce T-countcited by 1
  8. 2026QFlowNet: Fast, Diverse, and Efficient Unitary Synthesis with Generative Flow Networkscited by 1
  9. 2026Automated near-term quantum algorithm discovery for molecular ground statescited by 1
  10. 2026Replay-buffer engineering for noise-aware quantum circuit optimizationcited by 1
  11. 2026Aligning Quantum Operators with Large Language Modelscited by 1
  12. 2026Lie Group Diffusion Models for Hardware-Aware Quantum Circuit Synthesiscited by 1
  13. 2026When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesiscited by 1
  14. 2024Monte Carlo Search Algorithms Discovering Monte Carlo Tree Search Exploration Termscited by 0
  15. 2025Monte Carlo Permutation Searchcited by 0
  16. 2026Reinforcement Learning for Adaptive Composition of Quantum Circuit Optimisation Passescited by 0
  17. 2026Near-Constant Strong Violation and Last-Iterate Convergence for Online CMDPs via Decaying Safety Marginscited by 0
  18. 2026Q-Tag: Watermarking Quantum Circuit Generative Modelscited by 0
  19. 2026Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learningcited by 0
  20. 2026Program-Synthesis-Driven Autodesign of Universal Unitary Operatorscited by 0
  21. 2026Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scalingcited by 0
  22. 2026AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RLcited by 0
  23. 2026Generative Replay Mitigates Sample Starvation in Quantum Architecture Searchcited by 0
  24. 2026Bridge of Psi's: Quantum Circuit Optimization with Schr"odinger Bridgescited by 0