# Quantum compiling with a variational instruction set for accurate and fast quantum computing

arXiv: https://arxiv.org/abs/2203.15574 · HTML: https://mlsanity.com/paper/2203.15574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 78 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Universal Quantum Control through Deep Reinforcement Learning](https://mlsanity.com/paper/1803.01857/) (2018)
- [ScaffCC: Scalable Compilation and Analysis of Quantum Programs](https://mlsanity.com/paper/1507.01902/) (2015)
- [A Software Methodology for Compiling Quantum Programs](https://mlsanity.com/paper/1604.01401/) (2016)
- [Quipper: A Scalable Quantum Programming Language](https://mlsanity.com/paper/1304.3390/) (2013)
- [LIQUi|>: A Software Design Architecture and Domain-Specific Language for Quantum Computing](https://mlsanity.com/paper/1402.4467/) (2014)
- [Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions](https://mlsanity.com/paper/2106.01779/) (2021)
- [An Experimental Microarchitecture for a Superconducting Quantum Processor](https://mlsanity.com/paper/1708.07677/) (2017)
- [Deep learning Local Reduced Density Matrices for Many-body Hamiltonian Estimation](https://mlsanity.com/paper/2012.03019/) (2020)

## Built on by (most cited first, 3 of 3)

- [Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning](https://mlsanity.com/paper/2204.06904/) (2022)
- [Quantum Compiling with Reinforcement Learning on a Superconducting Processor](https://mlsanity.com/paper/2406.12195/) (2024)
- [LLMs Interpret, Embeddings Organize, Graphs Emerge: Agent-Driven Compilation of Scientific Knowledge](https://mlsanity.com/paper/2608.29612/) (2026)

## Abstract (arXiv)

The quantum instruction set (QIS) is defined as the quantum gates that are physically realizable by controlling the qubits in quantum hardware. Compiling quantum circuits into the product of the gates in a properly defined QIS is a fundamental step in quantum computing. We here propose the quantum variational instruction set (QuVIS) formed by flexibly designed multi-qubit gates for higher speed and accuracy of quantum computing. The controlling of qubits for realizing the gates in a QuVIS is variationally achieved using the fine-grained time optimization algorithm. Significant reductions in both the error accumulation and time cost are demonstrated in realizing the swaps of multiple qubits and quantum Fourier transformations, compared with the compiling by a standard QIS such as the quantum microinstruction set (QuMIS, formed by several one- and two-qubit gates including one-qubit rotations and controlled-NOT gates). With the same requirement on quantum hardware, the time cost for QuVIS is reduced to less than one half of that for QuMIS. Simultaneously, the error is suppressed algebraically as the depth of the compiled circuit is reduced. As a general compiling approach with high flexibility and efficiency, QuVIS can be defined for different quantum circuits and be adapted to the quantum hardware with different interactions.