The increasing complexity of quantum computers is driving a surprising reliance on classical computing infrastructure, as Nvidia announced in April new AI-based software designed to accelerate the essential classical tasks that underpin quantum operations. While quantum bits, or qubits, are inherently requiring constant calibration and error correction, digital computer chips operate flawlessly and can perform trillions of operations without error, a stark contrast highlighting the current need for robust classical support. Sydney-based Q-CTRL is now leveraging Nvidia’s agent-based system with its own automatic calibration algorithm to address this challenge, joining IBM Quantum, Riverlane, and Google Quantum AI in developing similar tools. “The cheapest and fastest way to execute most computer programs is to run them on a classical computer—even if a quantum computer is available,” says Adam Zalcman, a quantum software engineer at Google Quantum AI, emphasizing that hybrid quantum-classical architectures are likely the most practical path forward. Qubit Calibration Processes for Quantum Hardware Tuning The stark contrast between the reliability of classical and quantum computing components necessitates increasingly sophisticated calibration processes for qubits. This disparity underscores the significant classical infrastructure currently required to operate even early quantum systems, a point often underappreciated as the field advances. Calibration isn’t simply an initial setup; it’s a continuous process vital for mitigating the inherent instability of qubits and ensuring accurate computation. Tuning these quantum components begins with a painstaking “bring up” phase, determining crucial parameters like resonance frequency, quantum state coherence, and sensitivity to control pulses. All of these factors directly influence a qubit’s error propensity and response to signals. Historically, this process has been a manual undertaking, requiring expertise and consuming considerable time, potentially days or even weeks, according to Jay Guilmart, lead product manager at Q-CTRL. Recognizing the limitations of this approach, companies like Q-CTRL are driving automation, developing intelligent