The News: After putting version 0.3 on GitHub in 2017 and more than six years of community development, IBM has released the open-source Qiskit 1.0 software development kit (SDK). This release brings stability, performance improvements and heralds a planned cadence for future major and minor releases. See the announcement, release notes, and migration guide on the IBM Quantum website.
Quantum in Context: Quantum Software Development Kit Qiskit Turns 1.0
Analyst Take: It’s not unusual for software to be in a pre-release for years; Google Mail was in beta from 2004 to 2009. Qiskit was in beta for what I consider the first generation of quantum computing, when machines scaled from 5 qubits to more than 1000 and we started seeing partial implementation of logical qubits. IBM’s move with Qiskit to version 1.0 is what we now expect for any professionally maintained software, whether closed or open source. The improvements and stability in the API and the reliable release schedule should give quantum developers the confidence to move ahead with projects that feel much less experimental than in the past.
Qiskit by the Numbers
Blake Johnson, Quantum Engine Lead at IBM Quantum, shared the following statistics with me during a private briefing on March 21:
- Almost 580,000 registered users of the IBM Quantum Computing systems, most of whom either use Qiskit or the interactive IBM Composer
- More than 700 colleges and universities using Qiskit to teach quantum computing classes
- 540 Qiskit Advocates
- Nearly 1,200 certified Qiskit developers
- Almost 2,900 published and preprint papers using Qiskit
The Importance of Quantum Software Development Tools
An SDK is a set of tools and libraries that simplifies how the writing of applications to run on computer hardware. Microsoft’s Visual Studio and Apple’s Xcode are examples of such kits that developers have used for many years to code classical applications.
Qiskit includes a set of Python libraries that allow coders to create and execute circuits on quantum computing hardware or simulator backends. Used in this context, a quantum simulator is classical software that responds to instructions as if it were real quantum hardware. There are many varieties of quantum simulators, and Qiskit ships with seven of them. The Qiskit architecture allows a coder to write high-level Python code and decide which hardware or simulator backend to use. In this sense, Qiskit is a de facto standard for the interface between the quantum circuit description and the circuit execution model.
I did a quick review of the Python Package Index and found Qiskit backend support for quantum hardware from vendors including Alice & Bob, Alpine Quantum Technologies, Amazon Braket, IonQ, IQM, Microsoft Azure Quantum, Oxford Ionics, and Rigetti Computing. It is usually the provider’s responsibility to develop and maintain the backend. NVIDIA provides a cuQuantum quantum circuit simulator backend for Qiskit.
Why would IBM build a software development kit and then allow its competitors to use it? The answer is easy and well-established: open source. IBM benefits by having a community of active contributors to Qiskit. The software gets better, and IBM can use that improved code. It is otherwise IBM’s responsibility to ensure that Qiskit runs well on its quantum hardware, as with the other vendors. This is similar to the model allowing Linux to run on many classical computer systems.
Preparing Quantum Development Software for Bigger and Better Quantum Systems
Writing a basic software library that supports quantum gates and circuits for 5 or 10 qubits is very straightforward. There are some tricky bits, but I need not be too careful with my implementation because there are so few qubits. Things run fast enough on the classical laptop or desktop on which I write my Python code. As I use more qubits, I may realize that some parts of the libraries or tools are lagging. Perhaps my code is quadratic in the number of qubits, meaning the time or memory use increases as the square of the qubit count.
As an example: If the computation time takes 0.1 times the square of the number of qubits in seconds, for 5 qubits, this is 2.5 seconds. For 20 qubits, it is 40 seconds. The situation is even worse if my implementation is exponential in the qubit count. Suppose the time is 0.1 times 2 raised to the power equal to the number of qubits. For 5 qubits, the execution time is 0.1 times 25, which equals 3.2 seconds. For 20 qubits, the time would be 104,857.6 seconds, or approximately 29 hours. Something has to change to make this code usable.
Qiskit 1.0 has improved many internal algorithms and refactored the code for efficiency. Rather than having a pure Python implementation, the developers focused on rewriting more of the system using the Rust language with a Python interface over it. Good Rust code is much faster and memory-efficient than Python. This way, developers get Python language, convenience, and tools but with better performance from Rust underneath.
The X.Y.Zs of Quantum Software Versions
You may have used software or apps that have version numbers like 3.1.4. This numbering follows the X.Y.Z convention of a major version (X = 3), minor version (Y = 1), and patch version (Z = 4). At the time of writing, Qiskit is at version 1.0.2. IBM plans to do patch versions when necessary, minor versions every three months for enhancements and bug fixes that do not break correct user code, and major versions once a year. Developers beware: Major versions may break existing code, and it behooves IBM to give plenty of warning and support to coders affected by the changes.
Key Takeaway: Qiskit 1.0 Provides a Solid Foundation for Developing Software for the Next Generation of Quantum Computing Systems
Qiskit 1.0 is the open-source quantum software development kit’s most stable and best-performing version yet. IBM was the first to break the 1,000 qubit barrier with its experimental Condor chip for gate-and-circuit quantum computers, and other vendors will soon be scaling up their systems to that level and beyond. We need quantum development tools that scale similarly to systems that provide utility and eventual Practical Quantum Advantage.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author is a former IBM employee and holds an equity position in the company. The author’s book Dancing with Python introduces quantum software development using Qiskit.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
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Author Information
Dr. Bob Sutor is a Consulting Analyst for Futurum and an expert in quantum technologies with 40+ years of experience. He is an accomplished author of the quantum computing book Dancing with Qubits, Second Edition. Bob is dedicated to evolving quantum to help solve society's critical computational problems. For Futurum, he helps clients understand sophisticated technologies and how to make the best use of them for success in their organizations and industries.
He’s the author of a book about quantum computing called Dancing with Qubits, which was published in 2019, with the Second Edition released in March 2024. He is also the author of the 2021 book Dancing with Python, an introduction to Python coding for classical and quantum computing. Areas in which he’s worked: quantum computing, AI, blockchain, mathematics and mathematical software, Linux, open source, standards management, product management and marketing, computer algebra, and web standards.
