Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers
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Abstract (arXiv)
Recent developments in engineering and algorithms have made real-world applications in quantum computing possible in the near future. Existing quantum programming languages and compilers use a quantum assembly language composed of 1- and 2-qubit (quantum bit) gates. Quantum compiler frameworks translate this quantum assembly to electric signals (called control pulses) that implement the specified computation on specific physical devices. However, there is a mismatch between the operations defined by the 1- and 2-qubit logical ISA and their underlying physical implementation, so the current practice of directly translating logical instructions into control pulses results in inefficient, high-latency programs. To address this inefficiency, we propose a universal quantum compilation methodology that aggregates multiple logical operations into larger units that manipulate up to 10 qubits at a time. Our methodology then optimizes these aggregates by (1) finding commutative intermediate operations that result in more efficient schedules and (2) creating custom control pulses optimized for the aggregate (instead of individual 1- and 2-qubit operations). Compared to the standard gate-based compilation, the proposed approach realizes a deeper vertical integration of high-level quantum software and low-level, physical quantum hardware. We evaluate our approach on important near-term quantum applications on simulations of superconducting quantum architectures. Our proposed approach provides a mean speedup of 5×, with a maximum of 10×. Because latency directly affects the feasibility of quantum computation, our results not only improve performance but also have the potential to enable quantum computation sooner than otherwise possible.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Feb 2019.
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Citing papers per year: 2022: 3; 2023: 1; 2024: 1.
5 papers.
- 2024'Eliv'agar: Efficient Quantum Circuit Search for Classificationcited by 12
- 2022NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithmscited by 4
- 2022FrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot Nodescited by 4
- 2022Quantum Circuit Compiler for a Shuttling-Based Trapped-Ion Quantum Computercited by 2
- 2023RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Trainingcited by 2
- 2024'Eliv'agar: Efficient Quantum Circuit Search for Classification17 Jan 2024
- 2023RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Training27 Nov 2023
- 2022FrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot Nodes31 Oct 2022
- 2022NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms2 Aug 2022
- 2022Quantum Circuit Compiler for a Shuttling-Based Trapped-Ion Quantum Computer5 Jul 2022