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 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.
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 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%.
Abstract Quantum Annealing (QA) relies on mixing two Hamiltonian terms, a simple driver and a complex problem Hamiltonian, in a linear combination. The time-dependent schedule for this mixing is often taken to be linear in time: improving on this linear choice is known to be essential and has proven to be difficult. Here, we present different techniques for improving on the linear-schedule QA along two directions, conceptually distinct but leading to similar outcomes: 1) the first approach consists of constructing a Trotter-digitized QA (dQA) with schedules parameterized in terms of Fourier modes or Chebyshev polynomials, inspired by the Chopped Random Basis algorithm for optimal control in continuous time; 2) the second approach is technically a Quantum Approximate Optimization Algorithm (QAOA), whose solutions are found iteratively using linear interpolation or expansion in Fourier modes. Both approaches emphasize finding smooth optimal schedule parameters, ultimately leading to hybrid quantum–classical variational algorithms of the alternating Hamiltonian Ansatz type. We apply these techniques to MaxCut problems on weighted 3-regular graphs with N = 14 sites, focusing on hard instances that exhibit a small spectral gap, for which a standard linear-schedule QA performs poorly. We characterize the physics behind the optimal protocols for both the dQA and QAOA approaches, discovering shortcuts to adiabaticity -like dynamics. Furthermore, we study the transferability of such smooth solutions among hard instances of MaxCut at different circuit depths. Finally, we show that the smoothness pattern of these protocols obtained in a digital setting enables us to adapt them to continuous-time evolution, contrarily to generic non-smooth solutions. This procedure results in an optimized QA schedule that is implementable on analog devices.
We propose the combination of digital quantum simulation and variational quantum algorithms as an alternative approach to numerical methods for solving quantum control problems. As a hybrid quantum-classical framework, it provides an efficient simulation of quantum dynamics compared to classical algorithms, exploiting the previous achievements in digital quantum simulation. We analyze the trainability and the performance of such algorithms based on our preliminary works. We show that specific quantum control problems, e.g., finding the switching time for bang-bang control or the digital quantum annealing schedule, can already be studied in the noisy intermediate-scale quantum era. We foresee that these algorithms will contribute even more to quantum control of high precision if the hardware for experimental implementation is developed to the next level.
Adiabatic quantum computing is a powerful framework for state preparation, while its evolution time often scales quadratically in the inverse Hamiltonian spectral gap, leading to sub-optimal computational complexity. In this work, we introduce a nonlinear adaptive strategy for finding the time scheduling function, and show that the gap dependence can be quadratically improved to be inverse linear for a wide range of systems under a mild gap measure condition. Through variational analysis, we further demonstrate the optimality of our schedule for systems with linear gap and the partial optimality for general systems, while we also rigorously show that the commonly used linear schedule is never optimal.
Hybrid quantum-high performance computing (Q-HPC) workflows are emerging as a key strategy for running quantum applications at scale in current noisy intermediate-scale quantum (NISQ) devices. These workflows must operate seamlessly across diverse simulators and hardware backends since no single simulator offers the best performance for every circuit type. Simulation efficiency depends strongly on circuit structure, entanglement, and depth, making a flexible and backend-agnostic execution model essential for fair benchmarking, informed platform selection, and ultimately the identification of quantum advantage opportunities. In this work, we extend the Quantum Framework (QFw), a modular and HPC-aware orchestration layer, to integrate multiple local backends (Qiskit Aer, NWQ-Sim, QTensor, and TN-QVM) and a cloud-based quantum backend (IonQ) under a unified interface. Using this integration, we execute a number of non-variational as well as variational workloads. The results highlight workload-specific backend advantages: while Qiskit Aer’s matrix product state excels for large Ising models, NWQ-Sim not only leads on large-scale entanglement and Hamiltonian but also shows the benefits of concurrent subproblem execution in a distributed manner for optimization problems. These findings demonstrate that simulator-agnostic, HPC-aware orchestration is a practical path toward scalable, reproducible, and portable Q-HPC ecosystems, thereby accelerating progress toward demonstrating quantum advantage.