We present Q u a n t u m T o o l b o x . j l , an open-source Julia package for simulating open quantum systems. Designed with a syntax familiar to users of Q u T i P (Quantum Toolbox in Python), it harnesses Julia's high-performance ecosystem to deliver fast and scalable simulations. The package includes a suite of time-evolution solvers supporting distributed computing and GPU acceleration, enabling efficient simulation of large-scale quantum systems. We also show how Q u a n t u m T o o l b o x . j l can integrate with automatic differentiation tools, making it well-suited for gradient-based optimization tasks such as quantum optimal control. Benchmark comparisons demonstrate substantial performance gains over existing frameworks. With its flexible design and computational efficiency, Q u a n t u m T o o l b o x . j l serves as a powerful tool for both theoretical studies and practical applications in quantum science.
We present Q u a n t u m T o o l b o x . j l , an open-source Julia package for simulating open quantum systems. Designed with a syntax familiar to users of Q u T i P (Quantum Toolbox in Python), it harnesses Julia's high-performance ecosystem to deliver fast and scalable simulations. The package includes a suite of time-evolution solvers supporting distributed computing and GPU acceleration, enabling efficient simulation of large-scale quantum systems. We also show how Q u a n t u m T o o l b o x . j l can integrate with automatic differentiation tools, making it well-suited for gradient-based optimization tasks such as quantum optimal control. Benchmark comparisons demonstrate substantial performance gains over existing frameworks. With its flexible design and computational efficiency, Q u a n t u m T o o l b o x . j l serves as a powerful tool for both theoretical studies and practical applications in quantum science.
As the main theoretical support of quantum metrology, quantum parameter estimation must follow the steps of quantum metrology towards the applied science and industry. Hence, optimal scheme design will soon be a crucial and core task for quantum parameter estimation. To efficiently accomplish this task, software packages aimed at computer-aided design are in high demand. In response to this need, we hereby introduce QuanEstimation.jl, an open-source Julia framework for scheme evaluation and design in quantum parameter estimation. It can be used either as an independent package or as the computational core of the recently developed hybrid-language (Python-Julia) package QuanEstimation [Phys. Rev. Res. 4 (4) (2022) 043057]. Utilizing this framework, the scheme evaluation and design in quantum parameter estimation can be readily performed, especially when quantum noises exist.
We introduce version 1.0 of the SmoQyElPhQMC.jl package, an open-source Julia code for performing scalable quantum Monte Carlo simulations of electron-phonon coupled model Hamiltonians. SmoQyElPhQMC.jl is built upon the SmoQyDQMC.jl codebase and implements improved versions of the algorithms presented in [B. Cohen-Stead \textit{et al}., Phys. Rev. E 105, 065302 (2022)] to enable (near-linear) scalable simulations of a broad class of uncorrelated e-ph models both in system size and inverse temperature. By extending the functionality of the flexible scripting interface introduced in SmoQyDQMC.jl, the SmoQyElPhQMC.jl package continues to allow users to adapt it to different workflows and interface with other software packages in the Julia ecosystem. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyElPhQMC.jl or installed using the Julia package manager. The online documentation, including examples, can be obtained from our documentation page at https://smoqysuite.github.io/SmoQyElPhQMC.jl/stable/.
Quantum computing research relies heavily on numerical simulation, linear algebra, optimization, and scientific programming. While Python has become the dominant language for quantum computing frameworks such as Qiskit, Cirq, and PennyLane, Julia has emerged as a promising alternative due to its high-performance execution model and modern language design. This paper investigates the advantages of Julia over Python for quantum computing applications. We compare the two languages from theoretical, architectural, and practical perspectives, focusing on execution speed, memory efficiency, parallelization, numerical accuracy, and suitability for large-scale quantum simulation. Furthermore, we present an experimental study based on the Quantum Approximate Optimization Algorithm (QAOA) applied to the MaxCut problem. The benchmark was implemented independently in both Qiskit/Python and Julia and executed on the same hardware platform. Preliminary experimental results demonstrate a significant reduction in execution time when using Julia, with an observed speedup of approximately 3.9 x. Source code for both implementations has been made publicly available through the author's GitHub repository to facilitate reproducibility and further research.
We present QuantumBilliards.jl, an open-source Julia library providing a suite of numerical solvers for the two-dimensional interior Helmholtz eigenproblem with Dirichlet boundary conditions, a paradigmatic model in quantum chaos. The library implements conventional wavenumber-sweep techniques together with several accelerated eigensolvers: the Vergini--Saraceno scaling method, the expanded boundary integral method (EBIM), Beyn's contour-integral method, and the Compact Rational Krylov (CORK) method. It provides a common framework for constructing billiard geometries, exploiting discrete symmetries, computing eigenvalues and eigenstates, and treating smooth, cornered, and multiply connected domains. The library further includes reconstruction of eigenfunctions from boundary normal derivatives and the computation of Poincaré--Husimi (PH) phase-space representations.
Available at: https://github.com/Quantum-Chaos-Julia/QuantumBilliards.jl.
We present QUBO.jl, an end-to-end Julia package for working with QUBO (Quadratic Unconstrained Binary Optimization) instances. This tool aims to convert a broad range of optimization problems in JuMP, Julia's mathematical programming package, for straightforward application in many physics and physics-inspired solution methods whose standard model form is equivalent to QUBO. These methods include quantum annealing, quantum gate-circuit optimization algorithms (Quantum Optimization Alternating Ansatz, Variational Quantum Eigensolver), other hardware-accelerated platforms, such as Coherent ising machines and Simulated Bifurcation Machines, and more traditional methods such as simulated annealing. In addition to working with reformulations, QUBO.jl allows its users to interface with the aforementioned hardware, sending QUBO models to these devices and retrieving results for subsequent analysis. QUBO.jl was written as a JuMP / MathOptInterface (MOI) layer that automatically maps between the input and output frames, thus providing a smooth modeling experience.
We present a free open source package for high performance simulation and numerical investigation of boson samplers and, more generally, multi-photon interferometry. Our package is written in Julia, allowing C-like performance with easy notations and fast, high-level coding. Underlying building blocks can easily be modified without complicated low-level language modifications. We present a great variety of routines for tasks related to boson sampling, such as statistical tools, optimization methods and classical samplers. Special emphasis is put on validation of experiments, where we present novel algorithms. This package goes beyond the boson sampling paradigm, allowing for the investigation of new interferometric behaviours such as bosonic bunching.