Quantum Software and Algorithms (QSA)

Quantum computers hold immense potential for solving major challenges in medicine, materials science, and logistics, but current machines are very error-prone. Q.InC’s strategic research focuses on creating sophisticated 'quantum firmware' – crucial software that manages errors and helps early quantum computers work effectively.

We specialise in the unique challenges of light-based (photonic) quantum computing. This focus will position Singapore as a future leader in this global niche. By making advanced photonic computers more proficient and reliable at complex problem-solving, our research can enable the acceleration of drug discovery or optimisation of vital supply networks – areas vital to Singapore's economy. Such capabilities will attract global investment, build high-tech skills locally, and cement Singapore's position as a world-leading quantum innovation hub.

We are building a practical and powerful, full software stack for the emerging era of Early Fault-Tolerant Quantum Computing (EFTQC). Through memory-efficient intermediate representations, AI-enhanced compilation, ecosystem-aware design and robust benchmarking protocols, we are bridging theoretical quantum algorithms and scalable real-world implementation for EFTQC.

Focusing on real-world integration, we tailor firmware and compilers to the unique constraints of continuous-variable (CV) and discrete-variable (DV) photonic systems, emphasising high-performance emulation and integration with evolving hardware platforms.

To enhance scalability and reduce the overhead of logical qubit operations in CV/DV hybrid platforms, we leverage AI, deploying transformer-based models to automate quantum error correction, adaptive compilation, and dynamic feedback optimisation—pushing the boundaries of what intelligent quantum firmware can achieve.

Today’s hardware is not yet at desired standards. We develop firmware that respects the realities of photonic systems – considering photon loss, non-Markovian noise, and hybrid CV-DV architectures – to enable effective translation of abstract circuits to hardware execution.

EFTQC hardware will be resource-constrained. To optimise outcomes, we aim to develop techniques that reduce gate counts, optimise circuit structure, and in anticipation of emerging technologies, dynamically adapt to evolving photonic platforms.

To ensure our solutions translate in the real world, we bridge theoretical models with experimental hardware leveraging tools like our in-house tensor-network emulator (TEmu), AI-driven compiler optimisations, and ZX-calculus-based intermediate representations. This firmware-hardware co-design enables real-world applications and facilitates end-to-end system compatibility and integration with global hardware leaders.

We are nurturing the next generation of quantum engineers and innovators by building a robust talent pipeline through interdisciplinary training initiatives at local universities, national programmes like National Quantum Scholarship Scheme (NQSS), and hands-on R&D exposure via partnerships with startups and global consortia.