Optimizing Quantum Algorithms on Bipotent Architectures
Yanjun Ji, Kathrin F. Koenig, and Ilia Polian

TL;DR
This paper explores the trade-offs between hardware-level and algorithm-level optimizations in bipotent quantum architectures, demonstrating that pulse-level improvements currently offer greater benefits for certain quantum algorithms like QAOA.
Contribution
It provides an empirical analysis of the effectiveness of pulse-level versus gate-level optimizations on bipotent quantum hardware, highlighting practical guidance for qubit selection and architecture improvements.
Findings
Pulse-level optimizations outperform gate-level improvements in current bipotent architectures.
Gate fidelity alone does not predict overall algorithm performance; gate type and duration matter.
Efficient pulse optimization of SWAP gates significantly benefits dense QAOA problems.
Abstract
Vigorous optimization of quantum gates has led to bipotent quantum architectures, where the optimized gates are available for some qubits but not for others. However, such gate-level improvements limit the application of user-side pulse-level optimizations, which have proven effective for quantum circuits with a high level of regularity, such as the ansatz circuit of the Quantum Approximate Optimization Algorithm (QAOA). In this paper, we investigate the trade-off between hardware-level and algorithm-level improvements on bipotent quantum architectures. Our results for various QAOA instances on two quantum computers offered by IBM indicate that the benefits of pulse-level optimizations currently outweigh the improvements due to vigorously optimized monolithic gates. Furthermore, our data indicate that the fidelity of circuit primitives is not always the best indicator for the overall…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Low-power high-performance VLSI design
