Duke ARTIQ Extensions (DAX) provides a framework for writing modular control software for ion-trap quantum systems. DAX allows users to interface with the system at the gate level using an intermediate representation called DAX.program. However, DAX does not have the tools needed to simulate these programs to see quantum state evolutions, as DAX only allows for simulations at the device level. We propose a modular simulation framework, DAX.program-sim (DPS), that can simulate quantum programs at the level of quantum operations. This addition to DAX for classical simulation of quantum systems is vital for testing, benchmarking, and verification of quantum hardware. The DPS pipeline is designed to have input identical to the one that runs on the hardware. Our architecture allows flexible backends for simulation, enabling both gate-level simulations and pulse-level simulations with and without noise. We demonstrate this unified workflow, executing the same DAX.program file in simulation as well as on hardware. As a specific example, we run benchmarking circuits using simulators targeting ion trap quantum computers and compare them to hardware results of the same circuits.
Duke ARTIQ Extensions (DAX) provides a framework for writing modular control software for ion-trap quantum systems. DAX allows users to interface with the system at the gate level using an intermediate representation called DAX.program. However, DAX does not have the tools needed to simulate these programs to see quantum state evolutions, as DAX only allows for simulations at the device level. We propose a modular simulation framework, DAX.program-sim (DPS), that can simulate quantum programs at the level of quantum operations. This addition to DAX for classical simulation of quantum systems is vital for testing, benchmarking, and verification of quantum hardware. The DPS pipeline is designed to have input identical to the one that runs on the hardware. Our architecture allows flexible backends for simulation, enabling both gate-level simulations and pulse-level simulations with and without noise. We demonstrate this unified workflow, executing the same DAX.program file in simulation as well as on hardware. As a specific example, we run benchmarking circuits using simulators targeting ion trap quantum computers and compare them to hardware results of the same circuits.
Quantum computing has become widely available to researchers via cloud-hosted devices with different technologies using a multitude of software development frameworks. The vertical stack behind such solutions typically features quantum language abstraction and high-level translation frameworks that tend to be open source, down to pulse-level programming. However, the lower-level mapping to the control electronics, such as controls for laser and microwave pulse generators, remains closed source for contemporary commercial cloud-hosted quantum devices. One exception is the ARTIQ (Advanced Real-Time Infrastructure for Quantum physics) open-source library for trapped-ion control electronics. This stack has been complemented by the Duke ARTIQ Extensions (DAX) to provide modularity and better abstraction. It, however, remains disconnected from the wealth of features provided by popular quantum computing languages. This paper contributes QisDAX, a bridge between Qiskit and DAX that fills this gap. QisDAX provides interfaces for Python programs written using IBM's Qiskit and transpiles them to the DAX abstraction. This allows users to generically interface to the ARTIQ control systems accessing trapped-ion quantum devices. Consequently, the algorithms expressed in Qiskit become available to an open-source quantum software stack. This provides the first open-source, end-to-end, full-stack pipeline for remote submission of quantum programs for trapped-ion quantum systems in a non-commercial setting.
Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.
Quantum Hamiltonian simulation, which simulates the evolution of quantum systems and probes quantum phenomena, is one of the most promising applications of quantum computing. Recent experimental results suggest that Hamiltonian-oriented analog quantum simulation would be advantageous over circuit-oriented digital quantum simulation in the Noisy Intermediate-Scale Quantum (NISQ) machine era. However, programming analog quantum simulators is much more challenging due to the lack of a unified interface between hardware and software. In this paper, we design and implement SimuQ, the first framework for quantum Hamiltonian simulation that supports Hamiltonian programming and pulse-level compilation to heterogeneous analog quantum simulators. Specifically, in SimuQ, front-end users specify the target quantum system with Hamiltonian Modeling Language, and the Hamiltonian-level programmability of analog quantum simulators is specified through a new abstraction called the abstract analog instruction set (AAIS) and programmed in AAIS Specification Language by hardware providers. Through a solver-based compilation, SimuQ generates executable pulse schedules for real devices to simulate the evolution of desired quantum systems, which is demonstrated on superconducting (IBM), neutral-atom (QuEra), and trapped-ion (IonQ) quantum devices. Moreover, we demonstrate the advantages of exposing the Hamiltonian-level programmability of devices with native operations or interaction-based gates and establish a small benchmark of quantum simulation to evaluate SimuQ’s compiler with the above analog quantum simulators.
Noisy gate-model quantum processing units (QPUs) are currently available from vendors over the cloud, and digital quantum programming approaches exist to run low-depth circuits on physical hardware. These digital representations are ultimately lowered to pulse-level instructions by vendor quantum control systems to affect unitary evolution representative of the submitted digital circuit. Vendors are beginning to open this pulse-level control system to the public via specified interfaces. Robust programming methodologies, software frameworks, and backend simulation technologies for this analog model of quantum computation will prove critical to advancing pulse-level control research and development. Prototypical use cases for this include error mitigation, optimal pulse control, and physics-inspired pulse construction. Here we present an extension to the XACC quantum-classical software framework that enables pulse-level programming for superconducting, gate-model quantum computers, and a novel, general, and extensible pulse-level simulation backend for XACC that scales on classical compute clusters via MPI. Our work enables custom backend Hamiltonian definitions and gate-level compilation to available pulses with a focus on performance and scalability. We end with a demonstration of this capability, and show how to use XACC for pertinent pulse-level programming tasks.
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%.
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.