Quobly and Hon Hai Release Open-Source Quantum Phase Estimation Toolbox

On May 12, 2026, Quobly and Hon Hai Research Institute announced the release of an open-source numerical toolbox dedicated to the Quantum Phase Estimation (QPE) algorithm, a cornerstone of fault-tolerant quantum computing with major applications in quantum chemistry and materials science.

The collaboration brings together a French quantum computing pioneer and the R&D arm of Hon Hai Technology Group (Foxconn) to address a persistent gap in the field: while QPE's theoretical properties and asymptotic cost scalings are well understood, practical resource estimates and realistic performance trade-offs remain largely unexplored due to the difficulty of simulating QPE beyond toy models.

According to the official announcement from Hon Hai Research Institute, the newly released toolbox aims to bridge this gap by providing researchers with a practical environment to explore QPE implementations and their resource implications, with a strong focus on understanding algorithmic building blocks and their practical implementation constraints.

The QPE Toolbox is designed to give quantum algorithm practitioners a hands-on, numerical understanding of the full QPE workflow, from chemistry preprocessing to phase estimation, in a regime that challenges classical simulation while remaining computationally tractable. Built on advanced tensor network techniques, the toolbox enables users to prepare physically motivated initial states using DMRG and matrix product states, encode molecular Hamiltonians into quantum circuits via trotterization or block-encoding methods, compare textbook QPE with single-ancilla Robust Phase Estimation (RPE), and analyze circuit depth, gate counts, and error sources without necessarily executing the circuit.

The toolbox relies on the open-source quimb library and interfaces with standard quantum chemistry tools such as PySCF, ensuring compatibility with established workflows. This integration matters because researchers don't want to abandon their existing toolchains just to test a new algorithm (a friction point that has killed many quantum software projects before).

Illustrative use cases enabled by the toolbox include full circuit executions for approximately 10–20 qubits and circuits ranging from under 1,000 to around 100,000 gates, ground state preparation for systems up to approximately 20–30 qubits, and Hamiltonian encoding for systems up to approximately 20–30 qubits, typically within a few hours or less on a standard laptop.

These capabilities allow researchers to study trade-offs between precision, circuit depth, and resource requirements, and to build practical intuition about the behavior of QPE building blocks. The toolbox is therefore designed primarily as a pedagogical and exploratory platform, helping bridge the gap between theoretical proposals and their concrete implementation constraints.

Independent reporting from Electronics Weekly corroborates the technical specifications and confirms the toolbox's positioning as an educational framework rather than a production-grade quantum simulator.

Thibaud Louvet, Quantum Algorithms Scientist at Quobly, stated: "Our goal is to provide a practical, numerical playground for QPE, one that helps researchers move beyond purely theoretical cost models and develop realistic intuition for fault-tolerant quantum algorithms."

Min-Hsiu Hsieh, Director of the Quantum Computing Research Center at Hon Hai Research Institute, added: "By combining state-of-the-art quantum algorithms with advanced tensor-network techniques, this toolbox offers researchers a structured environment to explore and better understand the practical requirements of future quantum applications."

The jointly developed software is free for use by academics and researchers. This collaboration reflects a shared commitment by Quobly and Hon Hai Research Institute to advancing algorithm-hardware co-design and accelerating progress toward practical fault-tolerant quantum computing.

Quobly, founded in 2022 in Grenoble, France, is a pioneer in quantum microelectronics, developing silicon-based quantum chips using proven semiconductor manufacturing processes. The company builds on over 15 years of collaborative research between world-class institutions CEA-Leti and CNRS, combining expertise in quantum physics and microelectronics. Co-founded by Maud Vinet, Ph.D. in quantum physics with 300+ papers and 70+ patents, and Tristan Meunier, a leading expert in semiconductor quantum engineering trained under Nobel laureate Serge Haroche, Quobly bridges science and industry to make quantum computing scalable and manufacturable.

The company has a strategic partnership with STMicroelectronics to accelerate the industrialization of its silicon quantum chips. In 2023, Quobly raised €19 million, a record European seed round for a quantum hardware startup at the time.

The QPE Toolbox is released as open source and is intended to evolve with the community. Future developments will include variational circuit synthesis, compressed fermionic encodings, and larger-scale tensor-network simulations. The toolbox is available on GitHub at https://github.com/quobly-sw/qpe-toolbox, with documentation and example workflows provided to help researchers explore the different components of the QPE pipeline.

What this actually means for the field is nuanced. Researchers can now test algorithmic choices, initialization fidelity, and Hamiltonian encoding strategies in regimes accessible to classical computation, where they can observe concrete trade-offs rather than relying on asymptotic analysis alone. This is valuable because theoretical cost models often fail to capture the practical realities of gate decomposition, error propagation, and state preparation overhead.

The physical reality of using this toolbox involves running simulations on standard laptops, waiting hours for results on larger circuits, and iterating through different encoding strategies while watching gate counts climb. It's not glamorous, but it's the kind of work that separates viable quantum algorithms from theoretical curiosities.

Whether this toolbox will meaningfully accelerate fault-tolerant quantum computing depends on whether the insights gained from these classical simulations translate to actual quantum hardware performance. The gap between simulation and reality remains substantial, and many researchers have learned the hard way that what works on paper doesn't always work on qubits.

The open-source nature of the release is significant, as it invites community contributions and validation. However, the real test will be whether academic and industrial researchers actually adopt the tool and publish results that inform hardware design decisions. Until then, it remains a promising but unproven resource in the crowded quantum software landscape.

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn