Quantum computing (QC) offers a promising computational model that can potentially solve intractable problems by exploiting quantum phenomena such as entanglement and superposition. QC has important applications in cryptography, database search, chemistry simulations, portfolio optimization, machine learning, etc. Remarkable progress in engineering and materials science has created multiple quantum processing units (QPUs) featuring dozens to hundreds of qubits. These QPUs are typically offered by all major cloud providers: IBM, Microsoft Azure, AWS, etc.

Left: The gold casing is a cryostat fridge that cools the QPU to almost absolute zero degrees. It is one of the coldest places in the universe.

Right: The actual QPU chip (similar size to a CPU chip).

However, QPUs face several significant challenges that limit their widespread adoption and efficiency. First, the size of QPUs, measured by the number of qubits, remains small, restricting the complexity of problems they can solve. Second, QPUs are highly sensitive to noise, leading to frequent errors during computations. Third, the quantum ecosystem is highly heterogeneous: various quantum technologies (e.g., superconducting qubits, trapped ions), architectures, and qubits themselves often exhibit uneven performance and error rates. This spatial diversity is compounded by temporal heterogeneity, as QPUs require frequent recalibration to maintain their functionality. Finally, the lack of robust systems software—such as virtualization, compilers, and operating systems—hinders efficient management, programming, and scaling of quantum resources. These challenges collectively make it difficult to fully exploit the potential of quantum computing.

Our research group aims to design, implement, and make novel system software stacks that tackle these challenges to enable high-performance and scalable QC.

NOTE: We’re looking for students who want to write BSc/MSc theses or participate in Guided Research. If you are interested, please check the application instructions.

Current Active Projects

# Hybrid Distributed Quantum Computing (Hybrid DQC)

Practical applications of quantum computers require hundreds of thousands to millions of physical qubits, and it will be challenging for individual quantum processors to reach such qubit numbers due to control hardware, cooling, and other technology limitations.

One approach to running large quantum applications on small quantum computers is through circuit cutting & knitting. This is a divide-and-conquer approach, where the quantum program (circuit) is cut into smaller fragments that can run on current QPUs, and then the results are post-processed to reconstruct the original computation. However, this technique incurs exponential overheads in quantum and classical computation. The second approach is to leverage quantum teleportation, which enables interactions between qubits that live on different but interconnected QPUs. However, this approach incurs latency and noise in the computation, which deteriorates the quality of the final results.

This project aims to create a compiler infrastructure that automatically leverages both techniques, hence the term “hybrid,” to minimize the limitations of both techniques: minimal exponential overheads and quality deterioration.

Keywords: Distributed Systems, Quantum Networks, Hybrid quantum-classical

# Quantum Error Correction Benchmarking

Quantum error correction (QEC) is a vital technique for making quantum computers more reliable by correcting errors that occur during computation, which are caused by noise and imperfections in quantum devices. Since quantum systems are highly sensitive to errors, QEC helps improve the fidelity of quantum operations by encoding logical qubits in a way that allows for error detection and correction without directly measuring the qubits.

This project focuses on systematically benchmarking various quantum error correction codes (QECCs) to understand their performance in different scenarios. The pipeline developed for this benchmarking addresses several key questions, such as how QPU topology size and connectivity impact the effectiveness of QECCs in suppressing errors, how error probabilities vary across quantum devices, and how factors like qubit error rates, mapping, and translation to a backend’s basis set influence overall performance. Additionally, the project explores the challenges of executing QECCs on distributed quantum processing units (QPUs) and investigates how much error accumulation occurs during the error correction process itself. This work aims to comprehensively and fairly compare QECCs, contributing valuable insights into their practical application across different quantum technologies and devices.

Keywords: Quantum Error Correction, Error Correction Codes, Benchmarking

# Neutral Atom QPU Multi-programming

Neutral atoms have become prominent hardware architecture for realizing quantum computers in recent years due to their long coherence times compared to other traditional candidates like superconducting quantum computers. They provide new features like qubit shutting, where specific qubits are temporarily removed from the computation to save resources, and atom swapping, allowing the exchange of qubits between atoms for better connectivity. They also support 3+ qubit gates, enabling more complex operations and parallel gate execution, which allows multiple gates to run simultaneously on different qubits.

This project explores multi-programming multiple quantum circuits on Neutral Atom QPUs, which offer unique capabilities for enhanced quantum computation. The project aims to optimize how multiple quantum circuits can be managed on these devices, making efficient use of their capabilities to improve scalability and performance. By investigating the interaction of these advanced features, the project seeks to understand how to best allocate quantum resources and minimize errors while running several quantum programs concurrently on Neutral Atom QPUs.

Keywords: Neutral Atoms, Multi-programming, Compilation, Resource Efficiency

Past Projects

# QVM: Quantum Virtual Machine

Quantum computers promise breakthroughs in optimization, factorization, and quantum simulation but are hindered by noise and state decoherence, limiting the size and fidelity of quantum programs. Gate virtualization (GV) addresses these issues by replacing binary qubit gates with sampled single-qubit operations, improving circuit scalability and fidelity. However, existing GV methods lack automation and efficiency, suffering from high computational overhead.

To overcome these limitations, we propose the Quantum Gate Virtualization Machine (QVM), a system designed for scalable, reliable execution of quantum circuits on small, noisy QPUs. QVM comprises an abstraction for managing virtual gates, circuit decompositions, and data structures to streamline GV, a modular pipeline for optimizing circuits with GV, and A scalable execution framework that manages distributed QPU resources, processes virtual circuits, and leverages parallel post-processing.

Keywords: Virtualization, Quantum Optimization, Compiler, Intermediate Representation

Publication: https://arxiv.org/abs/2406.18410

# QOS: Quantum Operating System

QPUs present unique challenges, such as inherent noise, limited capacity, unpredictable behavior, and interference during multi-programming. Current quantum cloud software is rudimentary, addressing these issues in isolation, which prevents a cohesive solution.

QOS is a unified system stack designed to tackle these challenges holistically. It combines compiler and runtime mechanisms to optimize quantum computation and resource management, meeting the goals of both users (high-quality computation, low waiting times) and operators (efficiency, scalability). Key components include: (1) The Qernel Abstraction: A shared structure supporting policies across the system. (2) Enhances execution quality through program optimization. (3) Automates QPU selection, supports multi-programming, and enables load-aware scheduling. By unifying these mechanisms into a cohesive system, QOS eprovides a scalable, high-performance framework for practical quantum computing in the cloud.

Keywords: Quantum Resource Management, Multi-programming, Scheduling

Publication: https://arxiv.org/abs/2406.19120

# Orchestrating Quantum Cloud Environments with Qonductor

Quantum computing promises to solve problems beyond the reach of classical systems, but current Quantum Processing Units (QPUs) are specialized, noisy, and require hybrid workflows combining quantum and classical computing. These workflows rely heavily on classical pre- and post-processing steps to correct errors, often using accelerators like GPUs. The quantum cloud landscape adds further complexity, with scarce and highly varied QPUs, and increasing demand creating challenges in resource management and performance optimization.

Qonductor is a cloud-based orchestration system for hybrid quantum-classical workloads. Qonductor simplifies hybrid application development through standardized APIs, optimizes resource use with a resource estimator, and employs a scheduler to balance execution fidelity and job completion times. By addressing inefficiencies in hybrid workflows and resource allocation, Qonductor enhances performance and usability for quantum cloud applications.

Keywords: Quantum-Classical Resource Management, Estimation, HPC and Cloud

Publication: https://arxiv.org/abs/2408.04312

# Weaver: A Retargetable Compiler Framework for FPQA Quantum Architectures

Modern quantum processors, primarily based on superconducting technologies but also including emerging alternatives like trapped ions, neutral atoms, and photonics, are mostly accessed via cloud platforms. These technologies present trade-offs in performance, manufacturing complexity, and operational requirements, such as coherence times, gate speeds, error rates, and cooling needs. To leverage the diverse strengths of these platforms, retargetable quantum compilers are essential for adapting quantum programs seamlessly across hardware without requiring algorithm redesigns.

To address challenges in extensibility, performance, and verifiability, we propose Weaver as the first retargetable quantum compiler framework designed for superconducting qubits and neutral atom technologies, particularly Field-Programmable-Quantum-Arrays (FPQAs). Weaver extends OpenQASM with FPQA-specific instructions, optimizes programs for parallelism and reduced execution time through wOptimizer, and ensures functional equivalence via wChecker, advancing scalable and high-fidelity quantum computing.

Keywords: Retargetable Compiler, Neutral Atoms, ISA extension, Equivalence Checking

Publication: https://arxiv.org /abs/2409.07870

# Quantum Tensor Processor Unit (qTPU)

Quantum computing has the potential to revolutionize certain types of computations, but current quantum processors face significant challenges due to noise and limited scalability. To address these issues, researchers are exploring hybrid approaches that combine quantum and classical resources, such as quantum circuit knitting, which breaks down large quantum tasks into smaller, more manageable pieces. However, existing methods are often inefficient, with high computational overheads in the classical postprocessing phase.

qTPU is a framework designed to make hybrid quantum-classical computing more scalable and efficient. By leveraging advanced techniques like tensor networks and specialized compilers, qTPU reduces overhead, optimizes circuit decomposition, and accelerates processing across clusters of quantum and classical resources. The framework aims to achieve better performance than classical simulators while mitigating errors from quantum hardware.

Keywords: Tensor networks, Hybrid Tensors, Quantum-Classical, Compilation

Publication: https://arxiv.org/abs/2410.15080