Quantum Circuit Optimization with AlphaTensor
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913255123845120 |
|---|---|
| author | Ruiz, Francisco J. R. Laakkonen, Tuomas Bausch, Johannes Balog, Matej Barekatain, Mohammadamin Heras, Francisco J. H. Novikov, Alexander Fitzpatrick, Nathan Romera-Paredes, Bernardino van de Wetering, John Fawzi, Alhussein Meichanetzidis, Konstantinos Kohli, Pushmeet |
| author_facet | Ruiz, Francisco J. R. Laakkonen, Tuomas Bausch, Johannes Balog, Matej Barekatain, Mohammadamin Heras, Francisco J. H. Novikov, Alexander Fitzpatrick, Nathan Romera-Paredes, Bernardino van de Wetering, John Fawzi, Alhussein Meichanetzidis, Konstantinos Kohli, Pushmeet |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14396 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Circuit Optimization with AlphaTensor Ruiz, Francisco J. R. Laakkonen, Tuomas Bausch, Johannes Balog, Matej Barekatain, Mohammadamin Heras, Francisco J. H. Novikov, Alexander Fitzpatrick, Nathan Romera-Paredes, Bernardino van de Wetering, John Fawzi, Alhussein Meichanetzidis, Konstantinos Kohli, Pushmeet Quantum Physics Machine Learning 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. |
| title | Quantum Circuit Optimization with AlphaTensor |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2402.14396 |