Distributed FPGA-based Architecture for Real-time Superconducting Qubit Control with Mid-circuit Measurement Feedback

Основні поняття

A distributed FPGA-based architecture with lightweight processor cores is developed to enable real-time control and feedback for superconducting qubit systems, supporting dynamic quantum circuits with mid-circuit measurement and feedforward operations.

Анотація

The authors have developed a custom FPGA-based distributed control architecture for superconducting qubit systems. The architecture consists of a bank of lightweight, configurable processor cores that are designed to tightly integrate with the pulse generation and signal processing hardware.
Each processor core is responsible for controlling a small number of signal generator channels (1-3) and can execute parameterized control and readout pulses, as well as perform arbitrary control flow based on mid-circuit measurement results. The cores are designed to have low latency (< 100 ns) to enable real-time feedback for superconducting qubits with coherence times around 100 μs.
The authors also provide a Python/JSON-based intermediate representation (QubiC-IR) for writing and compiling dynamic quantum programs. QubiC-IR supports both gate-level and pulse-level abstractions, as well as high-level control flow constructs like conditional branching and loops. The compiler stack is designed to integrate with existing quantum software tools and programming languages.
The distributed architecture and QubiC-IR compiler have been implemented and deployed on the QubiC 2.0 system, which uses a Xilinx RFSoC FPGA. The authors demonstrate the capabilities of their system by performing a quantum state teleportation experiment on an 8-qubit superconducting transmon system at the LBNL Advanced Quantum Testbed.

Статистика

The coherence time of superconducting qubits is around 100 μs.
The end-to-end feedback latency requirement is around 100 ns.
The FPGA implementation uses 8 distributed processor cores, one for each qubit.
Each processor core controls 3 signal generator channels: 1 for qubit drive, 1 for readout drive, and 1 for readout demodulation.
The drive and readout DACs operate at 8 GSPS, and the readout ADCs operate at 2 GSPS.

Цитати

"Quantum circuits utilizing real time feedback techniques (such as active reset and mid-circuit measurement) are a powerful tool for NISQ-era quantum computing."
"For superconducting qubits, with coherence times ∼100 μs, real-time feedback requires a controller with latencies ∼100 ns."

Ключові висновки, отримані з

Distributed Architecture for FPGA-based Superconducting Qubit Control

by Neelay Fruit... о arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.15260.pdf

Глибші Запити

How can the distributed architecture be extended to support larger qubit systems with more complex control and readout requirements?

The distributed architecture can be extended to support larger qubit systems by implementing a scalable design that allows for the addition of more processor cores to accommodate the increased number of qubits. This scalability can be achieved by maintaining a modular approach where each core is responsible for controlling a small number of qubits, and new cores can be added as the qubit count grows. Additionally, the architecture can be optimized for parallelism to ensure efficient distribution of control and readout tasks across multiple cores.
Furthermore, the architecture can incorporate advanced synchronization mechanisms to ensure precise timing and coordination between the processor cores, especially in systems with a large number of qubits. This synchronization is crucial for maintaining the coherence of the quantum states and executing complex quantum algorithms that involve entanglement and superposition across multiple qubits.
To support more complex control and readout requirements, the architecture can be enhanced with specialized modules for error correction, calibration, and optimization of quantum operations. These modules can provide advanced functionalities such as real-time feedback, optimal control techniques, and adaptive algorithms to improve the performance and reliability of the quantum computations.

What are the potential limitations or challenges in scaling the distributed processor cores to hundreds or thousands of qubits?

Scaling the distributed processor cores to hundreds or thousands of qubits poses several challenges and limitations that need to be addressed:

Resource Constraints: As the number of qubits increases, the resource requirements for the processor cores, memory, and interconnects also grow significantly. This can lead to limitations in terms of FPGA resources such as logic elements, memory blocks, and routing capabilities, potentially hindering the scalability of the architecture.

Latency and Communication Overhead: With a large number of qubits, the communication overhead between the processor cores can increase, leading to higher latencies in executing quantum operations. Managing this latency and ensuring efficient data transfer between cores becomes a critical challenge in scaling the system.

Complexity of Control: Controlling hundreds or thousands of qubits simultaneously requires sophisticated control algorithms and synchronization mechanisms. Coordinating the operations of multiple cores to maintain coherence and avoid errors becomes increasingly complex as the system scales up.

Error Rates and Fault Tolerance: As the number of qubits grows, the probability of errors and decoherence also increases. Ensuring fault tolerance and error correction mechanisms at scale is a significant challenge in large quantum systems.

Scalability of Compiler Infrastructure: The compiler infrastructure needs to be robust and scalable to handle the complexity of programming and optimizing quantum circuits for hundreds or thousands of qubits. Ensuring efficient compilation and resource allocation for large-scale systems is a non-trivial task.

How can the QubiC-IR and compiler infrastructure be further integrated with existing quantum programming languages and tools to provide a seamless development experience for users?

To enhance the integration of QubiC-IR and the compiler infrastructure with existing quantum programming languages and tools, several strategies can be implemented:

Standardization: Aligning the QubiC-IR format with industry standards such as OpenQASM can facilitate interoperability with other quantum programming languages. This standardization enables seamless translation of quantum programs between different platforms and tools.

API Compatibility: Developing APIs that allow seamless interaction between QubiC-IR and popular quantum programming frameworks like Qiskit, Cirq, or Forest can streamline the development process for users. This compatibility enables users to leverage the features of these frameworks while utilizing the capabilities of the QubiC architecture.

Plugin Architecture: Implementing a plugin architecture that allows users to extend the compiler infrastructure with custom modules and optimizations can enhance the flexibility and adaptability of the system. Users can integrate domain-specific tools and algorithms into the compilation process, tailoring it to their specific requirements.

Visualization and Debugging Tools: Integrating visualization tools and debugging capabilities into the compiler infrastructure can aid users in understanding and optimizing their quantum programs. Visual representations of quantum circuits, optimization steps, and error diagnostics can enhance the development experience and facilitate rapid prototyping.

Community Engagement: Encouraging community contributions and feedback on the QubiC-IR and compiler infrastructure can drive innovation and improvement. Providing documentation, tutorials, and support channels for users to collaborate and share insights can foster a vibrant ecosystem around the platform.

By implementing these strategies, the integration of QubiC-IR and the compiler infrastructure with existing quantum programming languages and tools can offer users a seamless and efficient development experience, enabling them to harness the full potential of the distributed architecture for quantum computing.