A Rubik's Cube inspired approach to Clifford synthesis
arXiv:2307.08684 · doi:10.1103/PhysRevA.109.032409
Abstract
The problem of decomposing an arbitrary Clifford element into a sequence of Clifford gates is known as Clifford synthesis. Drawing inspiration from similarities between this and the famous Rubik's Cube problem, we develop a machine learning approach for Clifford synthesis based on learning an approximation to the distance to the identity. This approach is probabilistic and computationally intensive. However, when a decomposition is successfully found, it often involves fewer gates than existing synthesis algorithms. Additionally, our approach is much more flexible than existing algorithms in that arbitrary gate sets, device topologies, and gate fidelities may incorporated, thus allowing for the approach to be tailored to a specific device.
arXiv comment14 pages, 4 figures
References in corpus (13)
- Quantum Computing in the NISQ era and beyond
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Quantum algorithm for solving linear systems of equations
- Quantum Simulation
- Improved Simulation of Stabilizer Circuits
- Suppressing quantum errors by scaling a surface code logical qubit
- Quantum Computation as Geometry
- An example of the difference between quantum and classical random walks
- Scalable error mitigation for noisy quantum circuits produces competitive expectation values
- Hadamard-free circuits expose the structure of the Clifford group
- Topological Quantum Compiling with Reinforcement Learning
- 6-qubit Optimal Clifford Circuits
- The Classification of Clifford Gates over Qubits