Analyst(s): Dr. Bob Sutor
Publication Date: September 24, 2024
While most software application users only see the finished product, developers undertake a repetitive and time-consuming process to create the code. In the quantum and classical coding worlds, any speed or quality improvements in programming tools are greatly appreciated by developers and are reflected in better user experiences.
What Is Covered in This Article:
- The Qiskit Open-Source Quantum Development Toolkit
- How software developers create quantum circuits with Qiskit
- How well Qiskit benchmarks against its competitors in code generation speed and quality
The News: On September 16, IBM announced the results of a suite of code generation benchmarks for quantum software development kits, including its own Qiskit, Google’s Cirq, Amazon’s Braket, and Quantinuum’s TKET. Qiskit could complete more tests and, on average, run them faster, using fewer two-qubit gates or both. For additional details, see the “Qiskit leads quantum software development kits in performance, testing shows” blog post.
Quantum in Context: IBM Qiskit Boosts Software Development Speed
Analyst Take: Writing software is a wonderful, constructive process. You get an idea, write some code, use the best libraries of predefined routines you can find, and build the structure of an application. You then invoke tools to take your beautiful edifice and turn it into something a computer, classical or quantum, can execute. Then, you start fixing what you wrote and repeating the above process.
IBM’s announcement shows that it has significantly speed up Qiskit’s time to go from your code to an optimized version that can run on quantum hardware. Moreover, according to IBM and its tests, this “transpilation” (translation plus compilation) process is faster than its competitors, on average. The code created is better and uses fewer expensive operations.
This work is a very worthwhile effort by IBM. It demonstrates how reaching Practical Quantum Advantage (PQA) will be a matter of having high-performing scaled hardware plus optimized software on top that developers and their tools expertly create.
Also, on September 16, IBM announced its Qiskit Functions Catalog. I recommend you read my Futurum Group analysis “Quantum in Context: IBM’s New Qiskit Functions Accelerate Development” to get the full picture of Qiskit’s status.
What Is Qiskit?
Qiskit originally stood for “Quantum Information Science Kit” and is a quantum software development kit (SDK). IBM put the initial source code on GitHub in 2017 under the Apache License 2.0. For most of its life, it was a set of libraries primarily in Python, though IBM has rewritten much of the underlying code in Rust since the 1.0 release earlier this year for faster performance and to handle larger circuits with more qubits. This is a leading source of recent performance improvements, though better code generation algorithms have contributed significantly.
IBM has built a global ecosystem of tens of thousands of developers through an online textbook, challenges, videos, and courses. There are at least a dozen books about the SDK, with translations into many languages. My book Dancing with Python: Learn to Code with Python and Quantum Computing teaches classical and quantum coding and uses Qiskit examples.
The Quantum Software Development Process
At the heart of “digital” quantum computing are gates and circuits.
The diagram shows a circuit of two qubits using Grover Search, one of the earliest and best-known quantum algorithms. It is important because it offers a quadratic improvement over brute force search on unordered and unstructured entries. The search might be part of a larger quantum algorithm or a subroutine in a classical workflow.
For example, in the worst case, the brute force method to find one number in a list of 100 numbers in random order would take 100 peeks at the values (or 99, if you know the number is in the list). Using Grover, you can find it in approximately 10 = √(100) peeks. The circuit comprises H Hadamard, X, two-qubit CNOT, and S gates, with 13 steps from left to right.
One way of creating this circuit is via the Qiskit Python code:
There are more efficient ways of making the same circuit, but you get the idea. The role of transpilation is to take this high-level code and put it in an optimized form to run on specific quantum hardware. This is not a “one size fits all” problem. The architecture, qubit layout, and qubit and gate fidelities can affect what code the transpiler produces.
How IBM Is Improving the Speed and Quality of Software Development via Qiskit
Optimization becomes much more difficult as circuits get longer and use more qubits. Researchers have been developing optimization techniques for many years. It should be no surprise that they are now investigating AI and new methods to improve code generation. This must be balanced, however, with the time spent constructing runnable code. If I am iteratively writing and fixing my quantum code, I can’t wait long for the circuit to be usable.
Suppose the quantum application is for scientific discovery or an in-production financial calculation. In that case, I may allow the optimizer to run for hours for the best result it can produce.
IBM published a pre-print of the paper “Benchmarking the performance of quantum computing software,” detailing their work to speed up transpilation time and improve code generation. They also open-sourced a suite of tests called Benchpress on GitHub.
Of 1,066 tests, Qiskit could complete 1,044. Cirq was at the other end, completing only 7. I’m curious how Google will respond to raise this number for Cirq and provide additional tests it can perform but Qiskit cannot. You should read the disclaimers in the IBM blog post for all reported results and claims.
Regarding the time to complete the tests it could do, Qiskit took 49 minutes. TKET took 27 hours to finish 957 tests.
Two-qubit gates are essential for quantum computing. For example, you cannot entangle two qubits without gates such as CNOT or CZ. If you need to exchange the quantum states in two qubits, you need the two-qubit SWAP gate. However, they can be expensive time-wise and usually have higher error rates than one-qubit operations. Minimizing the number of such operations is critical for efficient circuit code generation.
In their tests, IBM showed that TKET generated 1.31 times the number of two-qubit gates as Qiskit. Staq’s multiple was 2.8.
These are impressive results. I suspect the developers of Qiskit’s competitors will not stand still. Also, I think it would be best if the industry moved the benchmarks and publishing of results to an independent third party, such as what happened with machine learning, MLCommons, and MLPerf.
What to Watch:
- Further improvements as IBM converts more of the code underlying the Qiskit Python interface to Rust
- Performance and code generation improvements to Cirq, Braket, and TKET by Google, Amazon, and Quantinuum, respectively
- Whether the Qiskit improvements help IBM grow its SDK adoption share among quantum software developers
For additional details, see the “Qiskit leads quantum software development kits in performance, testing shows” blog post.
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 does not hold any equity positions with any company mentioned in this article.
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.
Other insights from The Futurum Group:
Quantum in Context: A Qubit Primer
Quantum in Context: IBM’s New Qiskit Functions Accelerate Development
Quantum in Context: IBM Extends Qiskit 1.0 via AI Tools
Quantum in Context: Quantum Software Development Kit Qiskit Turns 1.0
Author Information
Dr. Bob Sutor has been a technical leader and executive in the IT industry for over 40 years. Bob’s industry role is to advance quantum and AI technologies by building strong business, partner, technical, and educational ecosystems. The singular goal is to evolve quantum and AI to help solve some of the critical computational problems facing society today. Bob is widely quoted in the press, delivers conference keynotes, and works with industry analysts and investors to accelerate understanding and adoption of quantum technologies. Bob is the Vice President and Practice Lead for Emerging Technologies at The Futurum Group. He helps clients understand sophisticated technologies in order to make the best use of them for success in their organizations and industries. He is also an Adjunct Professor in the Department of Computer Science and Engineering at the University at Buffalo, New York, USA. More than two decades of Bob’s career were spent in IBM Research in New York. During his time there, he worked on or led efforts in symbolic mathematical computation, optimization, AI, blockchain, and quantum computing. He was also an executive on the software side of the IBM business in areas including middleware, software on Linux, mobile, open source, and emerging industry standards. He was the Vice President of Corporate Development and, later, Chief Quantum Advocate, at Infleqtion, a quantum computing and quantum sensing company based in Boulder, Colorado USA. Bob is a theoretical mathematician by training, has a Ph.D. from Princeton University, and an undergraduate degree from Harvard College.
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.
