Efficient decomposition of unitary matrices in quantum circuit compilers
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Abstract (arXiv)
Unitary decomposition is a widely used method to map quantum algorithms to an arbitrary set of quantum gates. Efficient implementation of this decomposition allows for translation of bigger unitary gates into elementary quantum operations, which is key to executing these algorithms on existing quantum computers. The decomposition can be used as an aggressive optimization method for the whole circuit, as well as to test part of an algorithm on a quantum accelerator. For selection and implementation of the decomposition algorithm, perfect qubits are assumed. We base our decomposition technique on Quantum Shannon Decomposition which generates O((3/4)*4^n) controlled-not gates for an n-qubit input gate. The resulting circuits are up to 10 times shorter than other methods in the field. When comparing our implementation to Qubiter, we show that our implementation generates circuits with half the number of CNOT gates and a third of the total circuit length. In addition to that, it is also up to 10 times as fast. Further optimizations are proposed to take advantage of potential underlying structure in the input or intermediate matrices, as well as to minimize the execution time of the decomposition.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jan 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 2; 2025: 0; 2026: 2.
7 papers.
- 2023Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirscited by 16
- 2024Quantum Compiling with Reinforcement Learning on a Superconducting Processorcited by 4
- 2021QKSA: Quantum Knowledge Seeking Agentcited by 3
- 2022Optimisation-free Classification and Density Estimation with Quantum Circuitscited by 1
- 2024A Novel Single-Layer Quantum Neural Network for Approximate SRBB-Based Unitary Synthesiscited by 1
- 2026From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Datacited by 0
- 2026AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimizationcited by 0
- 2026AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimization13 Aug 2026
- 2026From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data2 May 2026
- 2024A Novel Single-Layer Quantum Neural Network for Approximate SRBB-Based Unitary Synthesis4 Dec 2024
- 2024Quantum Compiling with Reinforcement Learning on a Superconducting Processor18 Jun 2024
- 2023Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs24 Jul 2023
- 2022Optimisation-free Classification and Density Estimation with Quantum Circuits28 Mar 2022
- 2021QKSA: Quantum Knowledge Seeking Agent3 Jul 2021