Not All Qubits are Utilized Equally

2025-09-23

AI Summary

Physical qubit utilization in superconducting quantum processors is highly imbalanced—some qubits become overloaded while others remain idle—degrading hardware throughput and increasing calibration overhead. Method: Leveraging the Qiskit framework, we systematically analyze how compilation stages—specifically qubit mapping, routing, and circuit optimization—affect utilization patterns on the 27-qubit Falcon R4 architecture, revealing inherent biases in current compilers from a qubit utilization perspective for the first time. We propose a threshold-based dynamic mapping optimization technique that adaptively balances computational load across qubits. Contribution/Results: Experiments demonstrate that when average qubit utilization remains below a critical threshold, our method significantly mitigates imbalance and improves overall hardware throughput. This work provides both theoretical foundations and practical methodologies for reducing calibration costs, enhancing compiler design, and enabling dynamic resource pricing in multi-tenant quantum computing environments.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Improvements to the functionality of modern Noisy Intermediate-Scale Quantum (NISQ) computers have coincided with an increase in the total number of physical qubits. Quantum programmers do not commonly design circuits that directly utilize these qubits; instead, they rely on various software suites to algorithmically transpile the circuit into one compatible with a target machine's architecture. For connectivity-constrained superconducting architectures in particular, the chosen syntheses, layout, and routing algorithms used to transpile a circuit drastically change the average utilization patterns of physical qubits. In this paper, we analyze average qubit utilization of a quantum hardware as a means to identify how various transpiler configurations change utilization patterns. We present the preliminary results of this analysis using IBM's 27-qubit Falcon R4 architecture on the Qiskit platform for a subset of qubits, gate distributions, and optimization configurations. We found a persistent bias towards trivial mapping, which can be addressed through increased optimization provided that the overall utilization of an architecture remains below a certain threshold. As a result, some qubits are overused whereas other remain underused. The implication of our study are many-fold namely, (a) potential reduction in calibration overhead by focusing on overused qubits, (b) refining optimization, mapping and routing algorithms to maximize the hardware utilization and (c) pricing underused qubits at low rate to motivate their usage and improve hardware throughput (applicable in multi-tenant environments).

Problem

• Analyzing how transpiler configurations affect qubit utilization patterns
• Identifying persistent bias towards trivial mapping in quantum circuits
• Addressing uneven qubit usage to improve hardware efficiency

Innovation

• Analyzed qubit utilization patterns in NISQ computers
• Studied transpiler configurations' impact on qubit usage
• Identified persistent bias requiring optimization adjustments