QiliSDK is an open-source Python framework for designing and executing digital, analog, and hybrid quantum algorithms. Its modular structure unifies circuit-based and Hamiltonian-based workflows within a single API. It provides high-level abstractions for gates, circuits, Hamiltonians, and optimizers, while remaining fully backend-agnostic: seamlessly switch between CPU, GPU, or QPU execution. The SDK simplifies quantum development through expressive syntax for Pauli algebra, variational programs, and built-in tools for visualization, serialization, and result analysis.
QiliSDK is an open-source Python framework for designing and executing digital, analog, and hybrid quantum algorithms. Its modular structure unifies circuit-based and Hamiltonian-based workflows within a single API. It provides high-level abstractions for gates, circuits, Hamiltonians, and optimizers, while remaining fully backend-agnostic: seamlessly switch between CPU, GPU, or QPU execution. The SDK simplifies quantum development through expressive syntax for Pauli algebra, variational programs, and built-in tools for visualization, serialization, and result analysis.
This work presents a reproducible workflow for optimizing time-dependent Hamiltonian schedules using QiliSDK, an open-source, backend-agnostic Python framework for analog, digital, and hybrid quantum algorithms. The approach uses variational optimization of parametrized control schedules to improve state preparation beyond standard linear annealing, leveraging non-monotonic and near bang–bang profiles to exploit diabatic effects. The methodology follows a closed optimization loop combining quantum time evolution, expectation-value–based cost evaluation, and classical optimization. Results demonstrate fast convergence and robust improvements in excited-state preparation fidelity compared to naïve interpolation strategies. The contribution also highlights the modular architecture of QiliSDK, showcasing unified abstractions for Hamiltonians, schedules, optimizers, and execution backends, with an emphasis on reproducibility and portability across CPU, GPU, and quantum hardware.
This work presents QiliSim, the native C++ simulation backend of QiliSDK, an open-source Python framework for digital and analog quantum computing. QiliSim supports quantum circuits, Hamiltonian time evolution, and analog-digital quantum reservoirs through a common interface, allowing users to test quantum algorithms before executing them on quantum hardware. The simulator provides statevector and density-matrix methods for digital workloads, alongside fixed-step and adaptive Runge–Kutta integrators, Krylov-subspace methods, and direct matrix exponentiation for analog evolution. Monte Carlo trajectories, mid-circuit measurements, and Kraus and Lindbladian noise models support the simulation of noisy quantum systems. Numerical methods and execution settings are selected through structured configuration objects, allowing the same quantum program to run with different simulation strategies without modifying its definition. The poster includes a circuit simulation example and performance comparisons for digital circuit sampling and quantum annealing. The reported benchmarks show competitive CPU performance against Qiskit Aer and Cirq for small to medium circuits, and faster execution than QuTiP and CUDA-Q dynamics for the tested annealing task at a target fidelity of 99%.
We introduce OpenQAOA, a Python open-source multi-backend Software Development Kit to create, customise, and execute the Quantum Approximate Optimisation Algorithm (QAOA) on Noisy Intermediate-Scale Quantum (NISQ) devices and simulators. OpenQAOA facilitates the creation of QAOA workflows, removing the more tedious and repetitive aspects of implementing variational quantum algorithms. It standardises and automates tasks such as circuit creation across different backends, ansatz parametrisation, the optimisation loop, the formatting of results, and extensions of QAOA such as Recursive QAOA. OpenQAOA is designed to simplify and enhance research on QAOA, providing a robust and consistent framework for experimentation with, and deployment of, the algorithm and its variations. Importantly, a heavy emphasis is placed on the provision of tools to enable QAOA computations at the scale of hundreds or thousands of qubits.
The Intel(R) Quantum Software Development Kit (SDK) offers a comprehensive platform for developers to create quantum applications using an LLVM-based compiler with user-friendly C++ language extensions. This full-stack solution facilitates the expression and optimization of quantum circuits and includes a quantum runtime library for seamless hybrid execution between classical and quantum kernels. The runtime enables dynamic parameter determination for quantum circuits, allowing both quantum and classical components of variational algorithms to be defined within a single program, compiled once, and significantly reducing execution latency. The SDK features a suite of quantum simulators, including simulations of Intel's quantum hardware components such as qubit control processors, control electronics, and quantum dot qubits. With a unified interface, the Intel Quantum SDK allows users to target ideal qubit simulators, realistic Intel quantum hardware simulators, and future Intel quantum hardware, enabling efficient optimization, compilation, and execution of scalable hybrid quantum-classical variational algorithms.
We present QDK/Chemistry, a software toolkit for quantum chemistry workflows targeting quantum computers. The toolkit addresses a key challenge in the field: while quantum algorithms for chemistry have matured considerably, the infrastructure connecting classical electronic structure calculations to quantum circuit execution remains fragmented. QDK/Chemistry provides this infrastructure through a modular architecture that separates data representations from computational methods, enabling researchers to compose workflows from interchangeable components. In addition to providing native implementations of targeted algorithms in the quantum-classical pipeline, the toolkit builds upon and integrates with widely used open-source quantum chemistry packages and quantum computing frameworks through a plugin system, allowing users to combine methods from different sources without modifying workflow logic. This paper describes the design philosophy, current capabilities, and role of QDK/Chemistry as a foundation for reproducible quantum chemistry experiments.
Developing state-of-the-art classical simulators of quantum circuits is of utmost importance to test and evaluate early quantum technology and understand the true potential of full-blown error-corrected quantum computers. In the past few years, multiple theoretical and numerical advances have continuously pushed the boundary of what is classically simulable, hence the development of a plethora of tools which are often limited to a specific purpose or designed for a particular hardware (e.g. CPUs vs. GPUs). Moreover, such tools are typically developed using tailored languages and syntax, which makes it hard to compare results from, and create hybrid approaches using, different simulation techniques. To support unified and optimized use of these techniques across platforms, we developed HybridQ , a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to run on a variety of hardware. The philosophy behind its development has been driven by three main pillars: Easy to Use, Easy to Extend, and Use the Best Available Technology. The powerful tools of HybridQ allow users to manipulate, develop, and extend noiseless and noisy circuits for different hardware architectures. HybridQ supports large-scale high-performance computing (HPC) simulations, automatically balancing workload among different processor nodes and enabling the use of multiple backends to maximize parallel efficiency. Everything is then glued together by a simple and expressive language that allows seamless switching from one technique to another as well as from one hardware to the next, without the need to write lengthy translations, thus greatly simplifying the development of new hybrid algorithms and techniques.