QuantumCircuitOpt: An Open-source Framework for Provably Optimal Quantum Circuit Design
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Abstract (arXiv)
In recent years, the quantum computing community has seen an explosion of novel methods to implement non-trivial quantum computations on near-term hardware. An important direction of research has been to decompose an arbitrary entangled state, represented as a unitary, into a quantum circuit, that is, a sequence of gates supported by a quantum processor. It has been well known that circuits with longer decompositions and more entangling multi-qubit gates are error-prone for the current noisy, intermediate-scale quantum devices. To this end, there has been a significant interest to develop heuristic-based methods to discover compact circuits. We contribute to this effort by proposing QuantumCircuitOpt (QCOpt), a novel open-source framework which implements mathematical optimization formulations and algorithms for decomposing arbitrary unitary gates into a sequence of hardware-native gates. A core innovation of QCOpt is that it provides optimality guarantees on the quantum circuits that it produces. In particular, we show that QCOpt can find up to 57% reduction in the number of necessary gates on circuits with up to four qubits, and in run times less than a few minutes on commodity computing hardware. We also validate the efficacy of QCOpt as a tool for quantum circuit design in comparison with a naive brute-force enumeration algorithm. We also show how the QCOpt package can be adapted to various built-in types of native gate sets, based on different hardware platforms like those produced by IBM, Rigetti and Google. We hope this package will facilitate further algorithmic exploration for quantum processor designers, as well as quantum physicists.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Nov 2021.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 1; 2026: 2.
6 papers.
- 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardwarecited by 7
- 2024Optimal Layout-Aware CNOT Circuit Synthesis with Qubit Permutationcited by 4
- 2022An Empirical Review of Optimization Techniques for Quantum Variational Circuitscited by 3
- 2025CNOT-Optimal Clifford Synthesis as SATcited by 3
- 2026Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesiscited by 2
- 2026Gradient descent reliably finds depth- and gate-optimal circuits for generic unitariescited by 0
- 2026Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis16 Feb 2026
- 2026Gradient descent reliably finds depth- and gate-optimal circuits for generic unitaries6 Jan 2026
- 2025CNOT-Optimal Clifford Synthesis as SAT1 Apr 2025
- 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware31 Oct 2024
- 2024Optimal Layout-Aware CNOT Circuit Synthesis with Qubit Permutation8 Aug 2024
- 2022An Empirical Review of Optimization Techniques for Quantum Variational Circuits3 Feb 2022