Calibrating large quantum processors
TLDRSabrina Hong, a hardware engineer at QCATS, discusses the complexities of calibrating large quantum processors. She explains the importance of calibration for translating quantum circuits into executable analog outputs and the challenges of scaling, including automation, achieving uniform performance, and maintaining stability over time. Hong details the calibration process, using the Sycamore 2 processor as an example, highlighting the need for robust automation, system-level optimizations, and regular recalibration to address parameter drifts and maintain device performance.
Takeaways
- 🔬 Calibration is crucial for translating quantum circuits into analog outputs that execute the desired operations on a quantum processor.
- 🛠️ The QCATS team focuses on improving calibration methods for large-scale quantum processors to ensure robust, rapid, and automated calibration processes.
- 🧩 Calibration involves a series of experiments to determine how to compile quantum circuits into waveforms that control the quantum processor.
- 📊 A simple calibration example involves sweeping the frequency of a readout tone to maximize the separation between qubit states, allowing for accurate readout.
- 🔄 Calibration is a bootstrapping process where parameters are calibrated in a sequence that informs and sets the stage for subsequent calibrations.
- 💻 The calibration process is visualized as a Directed Acyclic Graph (DAG), which allows for an automatable, flexible, and extensible framework.
- 🔄 The Sycamore 2 processor, with 72 flux-tunable qubits, 72 readout resonators, and 121 tunable couplers, exemplifies the scale and complexity of modern quantum processors.
- 🔗 Each component of the processor has unique characteristics requiring individual calibration, leading to thousands of parameters that need tuning.
- 🔄 Parallelization of calibration procedures is essential for reducing the time required for full device calibration, enabling rapid cycles of learning and improvement.
- 📉 The Snake optimizer is a tool used for system-level optimizations, placing qubit idle and interaction frequencies to achieve uniformly high performance across the processor.
- ⏱️ Ongoing monitoring and maintenance are necessary to address parameter drifts over time, ensuring sustained performance of quantum processors.
Q & A
What is the role of Sabrina Hong in the QCATS team?
-Sabrina Hong is a hardware engineer on the Qubit Calibration and Testing team, also known as the QCATS team, where she works on improving the calibration of large quantum processors.
Why is calibration necessary for quantum processors?
-Calibration is necessary to translate a quantum circuit into analog outputs that execute the operations requested, ensuring the quantum computer can run algorithms accurately.
What are the main challenges faced in calibrating large quantum processors?
-The main challenges include robust and rapid automation, obtaining uniformly high performance across the processor, and maintaining stable performance over time.
How does the calibration process work for readout resonator drive frequency?
-The process involves sweeping the frequency of the readout tone while varying the qubit state, then selecting the frequency that maximizes the distance between the ground state and excited state clouds.
What is meant by the term 'bootstrapping problem' in the context of calibration?
-A 'bootstrapping problem' refers to the process where individual parameters are calibrated through a sequence of in-situ experiments, with each calibration providing information for the next set of experiments.
How does the calibration graph, or directed acyclic graph (DAG), help in the calibration process?
-The calibration graph helps in ensuring devices are calibrated in a standardized way, making it easier to compare performances across different devices and to continuously develop and refine calibration procedures.
What is the significance of the 'root configuration' in device calibration?
-The 'root configuration' is the initial stage of calibration where fundamental parameters about the device are learned, such as qubit and coupler yield, flux offsets, mutual inductances, and couplings.
How does the 'snake optimizer' contribute to achieving uniform performance across the processor?
-The 'snake optimizer' performs incremental optimizations across the processor, considering constraints and error models to place qubit idle and interaction frequencies in a way that optimizes for high performance.
What is the purpose of recalibration in maintaining quantum processor performance?
-Recalibration is necessary to account for drifts in calibrated parameters over time due to factors like temperature changes or device performance fluctuations, ensuring the device maintains its performance.
How does the QCATS team handle the large number of parameters that need to be calibrated in a device like Sycamore 2?
-The team uses parallelization strategies and the snake optimizer to reduce the number of calibration experiments and to perform incremental optimizations, allowing for faster and more efficient calibration of the device.
Outlines
🔬 Introduction to Quantum Calibration
Sabrina Hong, a hardware engineer on the Qubit Calibration and Testing (QCATS) team, introduces the basics of quantum calibration. She explains that calibration is essential for translating quantum circuits into analog outputs that execute the desired operations. The process involves several calibrations to compile quantum circuits into waveforms. An example of calibrating the readout resonator drive frequency is given, where the frequency is swept while varying the qubit state, and the data is processed to select the frequency that maximizes the separation between the ground and excited states. Calibration is described as a bootstrapping problem, where individual parameters are calibrated through a sequence of in-situ experiments, providing information for subsequent experiments. The video also discusses different types of calibrations, such as direct parameter calibration and those that inform system models for future calibrations.
🌐 Scaling Calibration Challenges
The video script delves into the challenges faced when calibrating large quantum processors, using the Sycamore 2 processor as an example. With 72 flux-tunable qubits, 72 readout resonators, and 121 tunable couplers, each component is unique and requires its own set of experiments for calibration. This results in approximately 8,000 individual parameters that need calibration per device. The process can be time-consuming, taking weeks or even months to complete. To address these challenges, the QCATS team has developed robust automation and parallelization strategies to reduce the total number of calibration experiments and enable rapid cycles of learning. The script also mentions the use of the Snake optimizer, an in-house tool developed by a colleague, for system-level optimizations to achieve uniformly high performance across the processor.
🛠️ System Level Optimizations and Calibration Maintenance
The script continues with a discussion on system-level optimizations, focusing on the use of the Snake optimizer to place qubit idle and interaction frequencies in a way that optimizes performance across the entire system. The process involves incremental optimizations based on various constraints and cost functions, such as minimizing qubit frequency trajectory and avoiding frequency degeneracies to reduce crosstalk errors. The script also covers the optimization of readout parameters, which is crucial for achieving fast and accurate mid-circuit measurements. However, the calibration process does not end with the initial setup. Calibrated parameters can drift over time due to various factors, necessitating regular monitoring and maintenance. The script introduces the concept of metric-based recalibration, where performance metrics are monitored, and recalibration is triggered when performance degrades. This approach is essential for maintaining the performance of quantum processors over time.
🔄 Summary and Future Outlook
In the final paragraph, Sabrina Hong summarizes the challenges of scaling up quantum processors and the ongoing research efforts to create faster, more robust, and fully automated calibration systems. She emphasizes the importance of continuous improvement in calibration techniques to keep pace with the rapid advancements in quantum computing technology. The script concludes with a thank you note, highlighting the collaborative and progressive nature of the work in the field.
Mindmap
Keywords
💡Calibration
💡Quantum Processor
💡QCATS Team
💡Robust and Rapid Automation
💡Uniformly High Performance
💡Directed Acyclic Graph (DAG)
💡Bootstrapping
💡Spectroscopy
💡Qubit
💡Readout
💡Two-Level System (TLS)
Highlights
Introduction to ongoing work on improving calibration of large quantum processors by Sabrina Hong from the QCATS team.
Calibration is essential for translating quantum circuits into analog outputs to execute quantum operations.
Calibration involves a series of in-situ experiments to determine how to compile quantum circuits into waveforms.
Example of calibrating the readout resonator drive frequency by sweeping the frequency and varying the qubit state.
Calibration is a bootstrapping problem where individual parameters are calibrated in sequence.
Different calibrations can be direct, like sweeping a parameter, or indirect, by building models for future calibrations.
Calibration can be visualized as a graph traversal problem on a directed acyclic graph (DAG).
Calibration procedures are standardized using a calibration graph, allowing for easy comparison and continuous development.
Different device configurations are used to calibrate various parameters, starting from root configuration to single qubit configurations.
The Sycamore 2 processor, with 72 flux tunable qubits, presents challenges in calibration due to variations in fabrication and design.
Approximately 8,000 individual parameters need to be calibrated per Sycamore 2 device, a task that can take weeks or months.
Robust automation is required for rapid calibration, including handling failures and iterating on different devices.
Parallelization of calibration procedures reduces the total number of experiments needed, enabling faster device bring-up.
Achieving uniform performance across the processor involves optimizing qubit idle and interaction frequencies using the Snake optimizer.
The Snake optimizer uses incremental optimization across the processor, similar to the arcade game 'Snake'.
Readout parameters are optimized independently from qubit frequencies, considering various readout error mechanisms.
Regular monitoring and maintenance are required to maintain device performance over time due to potential parameter drifts.
Metric-based recalibration is used to identify and optimize outlier qubits and pairs whose frequencies have changed.
Research continues into creating faster, more robust, and fully automated systems for calibrating quantum processors at scale.