qBraid Lab Integrates Rigetti Cepheus-1-108Q Processor and Kvantify Qrunch Chemistry Stack

Quantum cloud platform qBraid has announced a double expansion of its qBraid Lab ecosystem, introducing hardware and production-grade software modules to its developer base. The cloud framework has established direct integration with Rigetti Computing’s Cepheus-1-108Q, a 108-qubit device utilizing a multi-chiplet superconducting topology. In tandem, the platform has integrated Qrunch, a specialized quantum chemistry software package engineered by Copenhagen-based developer Kvantify. The unified additions aim to lower access friction for enterprise quantum teams, material scientists, and algorithm engineers by combining high-density physical processing targets with preconfigured simulation and execution environments. Modular Scaling and Topological Hardware Migration The addition of the Rigetti Cepheus-1-108Q processor triples the qubit and chiplet density of the preceding Cepheus-1-36Q architecture. The hardware integrates twelve independent 9-qubit silicon chiplets into a single, cohesive processing unit. Operating with a median single-qubit gate fidelity of 99.9% and a median two-qubit gate fidelity of 99.1% at a native 60 ns gate execution speed, the modular architecture utilizes native Controlled-Z (CZ) operations optimized for error-correction circuits. Concurrently, Rigetti’s older Ankaa-3 system is being retired from the platform. Because Cepheus-1-108Q shares the native Rigetti Quantum Cloud Services (QCS) backend, legacy programs will port directly, though developers may need to alter qubit routing configurations to match the new 108-qubit hardware connectivity graph. [ Legacy Ankaa-3 ] ──► ( Retired from qBraid Lab Platform ) [ New Cepheus-1-108Q ] ──► 12x 9-Qubit Chiplets ──► 108 Qubits ──► Native CZ Gates (99.1% 2Q Fidelity) Hardware-Agnostic Compilation and Quantum Error Correction The 108-qubit capacity addresses structural scaling limits that have challenged the execution of deep variational algorithms and quantum error correction (QEC) protocols. By hosting the system via direct software integration, qBraid Lab users can write, execute, and monitor larger-scale computational experiments without configuring independent cloud accounts or managing detached third-party authorization layers. The open-source qBraid SDK leverages a native cross-framework transpilation engine that accepts code written in Qiskit, Cirq, or pyQuil. The compiler automatically maps and reformats the high-level syntax into a target-appropriate representation tailored for the Cepheus modular layout, allowing researchers to evaluate physical-to-logical qubit encoding ratios directly inside active notebooks. Production-Grade Quantum Chemistry and Active Space Modeling Complementing the physical hardware scale-up, the integration of Kvantify Qrunch gives the platform’s developer community access to dedicated molecular modeling workflows. Qrunch combines hybrid quantum-classical algorithms with efficient active space mathematical restrictions to simulate molecular dynamics that exceed standard classical calculation boundaries. The computational chemistry package is paired with a series of preconfigured, ready-to-run interactive tutorials designed to anchor quantum execution inside industries with near-term commercial relevance, including energy infrastructure, sustainable manufacturing, and molecular pharmacology.

Targeted Simulation Verticals and Algorithmic Case Studies The Qrunch initial launch package features five distinct, domain-specific chemistry tutorials running natively within qBraid Lab: Butyronitrile Dissociation: Simulates C≡N triple-bond breaking using variational energy minimizations to characterize the stability of electrolyte materials for lithium-ion batteries and photovoltaic cells.

Carbon Capture Optimization: Models CO2 gas binding dynamics within the molecular cavities of COF-999 frameworks, calculating how ambient moisture profiles modulate transition-state kinetic energy paths.

Covalent Ligand Binding: Evaluates protein-ligand interaction states using the specialized BEAST Variational Quantum Eigensolver (VQE) algorithm, which compresses hardware requirements through efficient qubit-to-orbital mappings. Dehalogenase Reaction (SN2): Investigates enzyme-catalyzed haloalkane decomposition mechanisms to inform biological waste mitigation and bioremediation workflows. Ionization Potentials: Determines electron extraction energies for nitrogen-based compounds like ammonia (NH3) by comparing base neutral states against ionized electronic configurations.

Unified Cloud Architecture and Enterprise Deployment Paths The twin integration of Rigetti hardware and Kvantify software emphasizes the broader trend toward collocated, end-to-end quantum application development. Rather than maintaining isolated infrastructure pipelines, chemical engineers and deep-tech developers can utilize qBraid Lab as a central operating system. Workflows transition within a single environment from initial active space configuration in Qrunch to direct, hardware-agnostic execution across diverse physical processor modalities. By aggregating a backend suite that includes superconducting, neutral-atom, and trapped-ion systems from IBM, IonQ, Rigetti, QuEra, Atom Computing, AQT, and Microsoft, the platform establishes a structured pathway for verifying quantum chemistry simulations against competing physical hardware topologies. The software setup, open-source code repositories, and hardware allocation schedules can be reviewed in the official qBraid Kvantify Qrunch Release here, on the qBraid Cepheus Integration Report here, and via the qBraid GitHub Repository here. June 24, 2026