Revolutionizing Quantum Computing: Modular Compilation for Quantum Chiplet Architectures

Introduction

As quantum computing scales beyond early prototypes, the industry faces significant challenges in efficiently compiling quantum circuits for modular architectures. Traditional quantum compilers struggle with inter-chiplet communication and varying gate fidelities. Enter SEQC (Stratify-Elaborate Quantum Compiler)—a groundbreaking compilation pipeline designed to optimize modular quantum chiplet architectures.

In this blog, we explore how SEQC is paving the way for scalable quantum computing by improving circuit fidelity, execution time, and compilation efficiency.

The Challenge of Modular Quantum Architectures

Modern quantum processors are increasingly adopting chiplet-based architectures to overcome fabrication limitations and scalability constraints. However, this modular approach introduces unique challenges:

• Inter-chiplet Communication Overhead – Unlike monolithic quantum processors, inter-chiplet links do not support a universal gate set, making qubit allocation complex.
• Varying Gate Fidelity & Latency – The fidelity of quantum gates varies significantly between intra-chiplet and inter-chiplet operations, affecting overall circuit performance.
• Scalability Bottlenecks – Traditional compilation methods scale quadratically (O(n²)) with the number of qubits, making them inefficient for large quantum systems.

Introducing SEQC: A Two-Stage Compilation Pipeline

The SEQC pipeline is inspired by classical computing techniques and is designed to tackle these challenges head-on. It consists of two key stages:

1. Stratification Stage (One-Time Process Per Architecture)

• Partitioning Circuits into Subcircuits – The quantum circuit is split into subcircuits, ensuring that each subcircuit fits within a chiplet while minimizing inter-chiplet communication.
• Qubit Allocation with Simulated Annealing – A novel qubit-to-subcircuit mapping method reduces inter-chiplet SWAP operations.
• Chiplet Allocation & Routing – SEQC extends the SABRE algorithm, incorporating fidelity-aware routing strategies.

2. Elaboration Stage (Recurrent Process Per Execution)

• Parallel Compilation of Subcircuits – Each subcircuit is optimized and compiled in parallel for its target chiplet.
• Inter-Chiplet SWAP Optimization – SEQC categorizes SWAPs as symbiotic, commensalistic, or parasitic, prioritizing the most efficient ones.
• Hardware-Aware Optimization – The compilation process dynamically adapts to hardware constraints such as varying gate fidelities.

Key Innovations & Performance Gains

SEQC introduces several cutting-edge innovations that set it apart from existing quantum compilers:

🔹 Modularity Awareness – Unlike standard compilers (e.g., Qiskit), SEQC inherently understands and optimizes for hardware modularity.
🔹 Optimized Qubit Routing – The compiler prioritizes inter-chiplet SWAPs with lower error rates, significantly improving fidelity.
🔹 Scalability Improvements – SEQC reduces compilation complexity from O(n²) to O(k²) (where k is the number of qubits per chiplet), enabling efficient scaling to larger quantum processors.
🔹 Significant Speed & Fidelity Gains – SEQC achieves up to 36% higher circuit fidelity, 2-4x faster compilation time, and 1.92x lower execution time compared to a chiplet-aware Qiskit baseline.

Experimental Results: SEQC in Action

Benchmark tests using Supermarq quantum circuits (GHZ, VQE, Hamiltonian simulation, etc.) demonstrate:

✅ 2-4× faster compilation compared to traditional quantum compilers.
✅ 36% higher circuit fidelity, crucial for achieving reliable quantum computations.
✅ Reduction in inter-chiplet SWAP operations, leading to improved quantum coherence.
✅ Improved execution times, reducing the cost of running quantum workloads.

Future Directions: Towards Scalable Quantum Computing

The authors suggest future enhancements in:

• Stratification Algorithms – More sophisticated circuit partitioning methods to further minimize inter-chiplet communication.
• Alternative Chiplet Topologies – Exploring new physical layouts to optimize inter-chiplet connections.
• Machine Learning-Based Qubit Allocation – Leveraging AI to predict optimal qubit placements dynamically.

Conclusion

SEQC represents a major leap in quantum compilation for modular architectures, addressing the critical bottlenecks of inter-chiplet communication, scalability, and fidelity. As quantum hardware evolves, intelligent compilers like SEQC will play a pivotal role in unlocking large-scale, fault-tolerant quantum computing.

Quambase: Innovating at the Frontier of Quantum Computing

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