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qBraid Building the Quantum Cloud - Tarini Hardikar

Hardikar, Tarini

Published Nov 10, 2025• 0 citations
Publisher

Abstract

Quantum computing is transitioning from a field of isolated experiments to an interconnected ecosystem of hardware, software, and users. At qBraid, we build the infrastructure to make that transition possible, from cloud-based access to quantum devices and simulators, to education and training platforms used by thousands of students, researchers, and developers worldwide.This talk will explore how qBraid’s ecosystem, including qBraid Lab, qBook, and the qBraid SDK, accelerates learning, experimentation, and collaboration across hardware and frameworks. We’ll discuss lessons learned in designing accessible quantum platforms, the challenges of hybrid quantum–classical workflows, and how open-source and cloud-native tools are shaping the next phase of quantum software innovation.

DOI
10.5281/zenodo.17678042

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•Feb 24, 2026•0 citations
Quantum computing is transitioning from a field of isolated experiments to an interconnected ecosystem of hardware, software, and users. At qBraid, we build the infrastructure to make that transition possible, from cloud-based access to quantum devices and simulators, to education and training platforms used by thousands of students, researchers, and developers worldwide.This talk will explore how qBraid’s ecosystem, including qBraid Lab, qBook, and the qBraid SDK, accelerates learning, experimentation, and collaboration across hardware and frameworks. We’ll discuss lessons learned in designing accessible quantum platforms, the challenges of hybrid quantum–classical workflows, and how open-source and cloud-native tools are shaping the next phase of quantum software innovation.
The emergence of quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. The ability of quantum computers to scale computations beyond what the current computers are capable of implies better performance and efficiency for certain algorithmic tasks. However, to benefit from such improvement, quantum computers must be integrated with existing software systems, a process that is not straightforward. In this paper, we propose a unified execution model that addresses the challenges that emerge from building hybrid classical-quantum applications at scale. Following the Design Science Research methodology, we proposed a convention for mapping quantum resources and artifacts to Kubernetes concepts. Then, in an experimental Kubernetes cluster, we conducted experiments for scheduling and executing quantum tasks on both quantum simulators and hardware. The experimental results demonstrate that the proposed platform Qubernetes (or Kubernetes for quantum) exposes the quantum computation tasks and hardware capabilities following established cloud-native principles, allowing seamless integration into the larger Kubernetes ecosystem. The quantum computing potential cannot be realized without seamless integration into classical computing. By validating that it is practical to execute quantum tasks in a Kubernetes infrastructure, we pave the way for leveraging the existing Kubernetes ecosystem as an enabler for hybrid classical-quantum computing.
Quantum Computing (QC) has evolved into a computational model that can potentially solve problems of classes that cannot be solved by classical systems. This review gives a discussion on the innovation of software frameworks, experimental platforms and quantum computing tools used to compute quantum algorithm development, simulation and implementation. It concentrates on widely used programming environments, which compile abstract quantum models into practical workflows, including Cirq, TensorFlow Quantum, ProjectQ and Qiskit. The paper, besides describing the long-lasting problems related to scalability, error correction, and hardware reliability, also considers the architecture of the modern quantum computing platforms. Finally, new advances are outlined, such as quantum cloud service and the integration of artificial intelligence and quantum computing, and their role in the development of QC to viable and lucrative deployments. All in all, the discussion suggests that even now quantum computing still needs to be taken to the next level in terms of hardware infrastructure and software development that would allow progressing to the next stage of quantum technology testing and implementation.
The commercial and scientific momentum behind quantum computing is accelerating rapidly, yet a critical gap remains between theoretical promise and practical implementation. This talk bridges that gap through some of the hands-on projects spanning quantum simulation and combinatorial optimization, implemented on real quantum software frameworks including Intel Quantum SDK, Eviden myQLM, PennyLane, and Qiskit.We begin by motivating the two most tractable near-term application domains - simulation and optimization, through current industry trends, before examining concrete implementations: a GAN-accelerated surrogate simulator for neutrino interactions in the INO-ICAL particle physics detector; a quantum-accelerated Quantum Monte Carlo workflow for correlated electron systems, developed in collaboration with Intel and presented at ISC 2025; a QAOA-based solution to the flight-gate assignment problem, developed in industry collaboration with Atos-Eviden; and a hardware-aware Variational Quantum Eigensolver (VQE).The talk concludes with honest lessons from implementing these algorithms on real platforms, the theory-hardware gap, and why hybrid classical-quantum workflows remain the practical frontier. Attendees will leave with a grounded understanding of where quantum advantage is being engineered today, and what it actually takes to get there
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Quantum computing can be explored and understood today without owning a quantum computer. Classical emulation provides a cost-effective, accessible, and pedagogical bridge between traditional HPC and the quantum future. By developing algorithms and skills through open-source simulators, 28researchers and engineers can prepare for the quantum advantage era while contributing to the refinement of quantum algorithms and hybrid computing models.The development of this talk and demonstrations draws inspiration from the open-source quantum software community, in particular contributors to Qiskit, Cirq, and Quantum++. Their work has been instrumental in democratizing access to quantum experimentation and education.ABOUT THE AUTHORJordi Blasco Pallares is a computational physicist from the University of Barcelona with over two decades of experience in HPC. His career has spanned multiple national-level supercomputing centers and international research institutions, where he has architected, optimized, and managed large-scale computing systems supporting scientific and industrial innovation. As Co-Founder and Director of Do IT Now - New Zealand (formerly HPCNow!) Jordi leads strategic consulting and solution design across the APAC region and the West Coast of the USA. His work focuses on the development and deployment of advanced HPC and AI infrastructures. He has played a key role in enabling research and engineering teams to achieve peak computational efficiency through performance tuning, benchmarking, and automation.Marco De La Pierre is a Computational Scientist at Do IT Now, a global HPC services and consulting company. Building on a background in computational materials science and on 15 years of experience in High-Performance Computing, he enjoys investigating new software technologies to design and deploy HPC solutions that facilitate user experience and productivity. Active across various aspects of applications and user support, his interests include workflow automation, reproducibility and portability, scientific software stack management, and user training. -----For more information about the eResearch NZ / eRangahau Aotearoa conference, visit: https://eresearchnz.co.nz/