Research Pillars

QuantumAI.Cloud (QAC) Lab is a research and development initiative led by Hoa Nguyen that focuses on advancing the convergence of quantum computing, artificial intelligence, and cloud infrastructure.

QAC addresses critical challenges in quantum software engineering, quantum cloud systems, and practical quantum applications. We believe quantum computing adoption depends on three pillars: (1) quantum cloud computing systems that orchestrate heterogeneous quantum-classical resources effectively, (2) reliable AI frameworks for quantum software that enable developers to write correct quantum programs efficiently, and (3) impactful quantum applications that demonstrate potential advantages. Our research integrates these pillars through rigorous evaluation, reproducible benchmarks, and open-source contributions that advance the broader quantum software engineering ecosystem.

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Update January 2026: We are seeking highly motivated PhD and Master's students in Australia (for co-supervision opportunities), and international research collaborators and open-source software contributors. If you're passionate about advancing quantum software engineering, quantum cloud systems, or quantum machine learning applications, we invite you to explore opportunities within our lab. Please contact our PI at [email protected] with your CV, research proposal, and how they align with one or more of our three research pillars.

Pillar 1: Quantum Cloud and Distributed Computing

Quantum cloud platforms (IBM Quantum, AWS Braket, Azure Quantum) democratise access to quantum processors but face critical resource management challenges: significant idle time due to naive scheduling, unpredictable queue latencies, and suboptimal backend selection. Hybrid quantum-classical applications (VQAs, QML) require tight coordination across heterogeneous resources - QPUs, CPUs, GPUs - yet lack orchestration frameworks that optimise for fidelity, latency, cost, and energy simultaneously.

Research Focus:

Hybrid quantum-classical resource management that optimises scheduling, orchestration, and cost across QPUs, CPUs, and GPUs, explicitly targeting fidelity, latency, throughput, and cost.
Digital twins and simulations that enable policy learning and testing without prohibitive hardware costs, supporting real-time predictive optimisation.
Distributed quantum computing via circuit cutting and multi-QPU workflows, enabling applications that exceed single-processor capacity

Pillar 2: Reliable AI for Quantum Software & Systems

Quantum programming remains a critical adoption barrier. Developers must master quantum mechanics, navigate evolving SDKs (Qiskit, Cirq, PennyLane), and reason about hardware constraints, all while achieving functional correctness in a domain where classical intuition fails. Large language models (LLMs) offer promise for quantum code generation, yet current approaches achieve below 40% functional correctness. The challenge is not raw code generation but reliability: producing verifiable, executable quantum programs that correctly implement intended algorithms under real hardware constraints.

Research Focus:

LLM-driven quantum software development that generates correct, executable quantum programs through closed-loop feedback (compilation validation, simulation, program repair, hardware constraints)
Learning-assisted quantum circuit compilation and optimisation that adapts to hardware topology, calibration drift, and multi-objective trade-offs.

Pillar 3: Quantum Machine Learning and Optimisation Applications

Applications drive research priorities, validate infrastructure, and demonstrate impact. Quantum machine learning (QML) and optimisation represent the most commercially promising near-term quantum application domains, targeted by pharmaceutical companies (drug discovery), financial institutions (portfolio optimisation), logistics providers (routing), and telecommunications (network design). However, practical quantum advantages remain elusive: NISQ hardware imposes qubit limits, high noise, and the absence of formal speedup proofs.

Pillar 3 serves as our workload driver; it stresses infrastructure (Pillar 1) by demanding efficient orchestration of large workflow ensembles, and it validates developer tools (Pillar 2) by requiring correct, optimised quantum programs. By focusing on well-scoped applications with clear success metrics, we demonstrate end-to-end quantum cloud capabilities and create feedback loops that improve infrastructure and tools.

Research Focus:

Quantum machine learning for systems decisions (scheduling, backend selection, resource forecasting) integrated with strong classical baselines.
Quantum optimisation workflows (QAOA, VQE) for combinatorial problems (portfolio optimisation, vehicle routing, molecular simulation) with rigorous end-to-end evaluation and fair classical comparisons.