We are in the NISQ era of quantum computing and this technology is finally getting to a point where we are starting to see some interesting signs of the real-life application. In an attempt to harness a quantum advantage, corporations in finance, pharmaceuticals and a range of other industries have set-up in house or collaborative quantum programs.
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Although there have been some significant gains in the acceptance and adoption of quantum computing over the past few years, there are still some barriers preventing wider spread usage. An obvious example is the requirements of new software platforms for interfacing and integration with classical systems. Although most of the big players like IBM and Microsoft have their own activities in developing quantum software solutions, there are several start-ups focusing on this that have recently emerged.
In fact, over the past five years not only has there been a boom in the number of quantum computing start-ups but a significant shift in the funding landscape, away from hardware focused entities towards those focused on software/algorithm development.
It’s an indication that Quantum computing is reaching a point of maturity where the focus is not purely on the possibilities of developing quantum processors, but rather on deploying these systems and making them more accessible.
This is currently a very active space with new companies, solutions and products being pitched all the time. To help navigate through the hype, we’ve compiled a list of some the most prominent software platforms for quantum computing, taking a look at what they offer, their unique features and where they are being applied.
IBM Qiskit:
It would be difficult to have a list of influential quantum computer software platforms and not include IBMs Qiskit. IBM have been one of the biggest supporters of the quantum revolution; not only has their hardware been improving at a record pace, it’s also been accessible via the cloud since 2017. To round it all off, in 2017 IBM released an open-source SDK for working with quantum computers at the level of pulses, circuits, and application modules – Qiskit.
Being one of the earliest tool kits for quantum computing, it’s gained widespread popularity with more than 600,000 installs to date. When IBM Quantum and the Qiskit team hosted a quantum summer school last year, almost 5,000 developers participated. More recently IBM announced the IBM Quantum Developer Certification which is expected to promote the platform even further.
What makes Qiskit so popular is a combination of user-friendly API environment, tons of online resources and software tools that virtually any developer in the industry should be familiar with. It makes for a great point of entry, no matter the level of experience in either quantum computing or programming.
Having said that, the Qiskit API does seem to be focused on learning, training, and research. Beyond the very basics of quantum logic processing, most of the higher-level applications for circuit preparations are wrapped up in block box code, which can be frustrating when trying to push the limits of QC and develop something applied or outside of the research sphere.
Riverlane:
UK based Riverlane have recently been making headlines for securing a decent amount of funding for their ambitious grand vision: an operating system that makes quantum software portable across qubit technologies; that is scalable to millions of qubits; and that teases the highest possible performance out of every qubit.
With a trail version of their Deltaflow-on-ARTIQ already available, Riverlane have made their first step towards achieving this vision. What makes Riverlane unique is their multi-focus approach at quantum computing through the Deltaflow language. Deltaflow lets you define a quantum circuit/algorithm, incorporates a Deltasimulator which lets users test programs on simulated hardware, and the Deltamodels which connect the Deltasimulator to an emulator of the ARTIQ backend control system. The software is quantum agnostic, meaning it can be run on any quantum hardware and incudes protocols for error correction and bench marking (a valuable tool for the current noisy systems).
The main selling point of Riverlane’s Deltaflow is that it essentially gives a finer control of the quantum circuitry, allowing for error rate evaluation and mitigation through feedback control loops, all operating in real time. This is expected to improve logic processing when running algorithms.
Platforms like this are essential for dealing with the high error rate of the current noisy systems and Riverlane’s Deltaflow makes for a good attempt at solving this issue at the measurement and control level.
Xanadu’s Pennylane:
PennyLane is an open-source software framework developed by Toronto based Xanadu. The platform is built around the concept of quantum differentiable programming which essentially integrates classical machine learning libraries with quantum simulators and hardware, giving users the power to train quantum circuits. Considering the power and ubiquitous usage of machine learning protocols, it is an interesting route for developing software platforms.
Having said that, Pennlyane is bigger than just using quantum computers to tackle machine learning problems. Because quantum circuits are differentiable, a quantum computer itself, with the help of Pennylane, can compute the change in control parameters needed to become better at a given task.
Differentiable programming is the very basis of deep learning, implemented in software libraries such as TensorFlow and PyTorch. Essentially, it’s a programming paradigm where the algorithms are not hand-coded but learned. Incorporating this into quantum logic processing may enhance a variety of applications such as the design of quantum algorithms, the discovery of quantum error correction schemes, and the understanding of physical systems.
What makes Xanadu even more interesting is that PennyLane was developed on their very own in-house hardware; a photonic circuit-based quantum processor that is available through the cloud.
Azure Quantum:
Azure Quantum is Microsoft’s very own open-source Quantum Development Kit. Azure Quantum enables you to develop durable quantum applications for some of the most prominent quantum platforms currently available and aims to be adaptable to the scaled-up hardware of the future.
Microsoft has homed in on one of the most immediate applications of quantum computing: optimization problems. The Quantum Development Kit offers tools to formulate optimization problems to run on large-scale or hardware-accelerated compute resources in the Azure environment. This is through Q#, an open-source programming language for developing and running quantum algorithms. The Quantum Development Kit, therefore made up of an impressive toolbox which includes Q# libraries, quantum simulators, extensions for other programming environments, and API documentation.
As a programming language, Q# draws familiar elements from Python, C#, and F# and supports a basic procedural model for writing programs with loops, if/then statements, and common data types. It also introduces new quantum-specific data structures and operations. Q# programs can run as a console application, through Jupyter Notebooks, or use a Python or .NET host program. Furthermore, the Q# libraries enable you to run complex quantum operations without having to design low-level operation sequences. It’s basically everything needed to develop and deploy quantum applications in one neat package.
Classiq
An obvious barrier to mainstream usage of quantum computers is the lack of knowledge of the underlying physics of these niche technologies. Currently the only way to translate ideas into quantum algorithms is to manually program the low-level code at the circuit level. So instead of powering ahead with new innovations, progress slows down and business impact gets further out of reach. This problem has directly been attacked by Israeli based Classiq who recently launched their grand vision to move beyond the quantum gate/circuit level and into higher level programming.
Classiq are working on a platform, Quantum Algorithm Design (QAD) which automatically translates human-defined concepts into valid quantum circuits, eliminating the need to build them manually, gate by gate. This allows users to synthesize, analyse and test quantum algorithms effortlessly, and potentially enhances quantum computing deployment.
Using powerful CAD technology, Classiq enables you to automatically generate algorithms based on specific functionality and requirements, without needing to manually balance low-level aspects like load probability distributions or levels of entanglement. QAD provides the missing piece holding back quantum software development — the abstraction layer — allowing quantum beginners and experts alike to fully realize quantum applications.
Although it’s still early days for Classiq, their focus on mainstream usage and enhanced accessibility is definitely valuable for promoting the current technology, and as they claim to be a versatile solution for developing literally any type of quantum application, it will be interesting to see how this platform gets integrated into the community and what novel applications get generated.
Take away message.
Developing software platforms for quantum processors is already a multi-million-dollar industry. Although still in an early phase, quantum computing adoption is improving, this drives the need to further increase accessibility and enhance capabilities of the current systems, resulting in massive innovation in the software development space. Already several corporations have released open-source platforms with various capabilities and applications in mind. A large proportion of these are focused on gate-level programming and algorithm deployment, however there are also several companies that are starting to focus on industry specific applications. A good example of this, for example Multiverse Computing, who are developing the worlds first quantum computing SDK specifically for finance and ProteinQure who have developed a quantum-machine learning platform for the design of protein-based drugs.
Quantum software and algorithm development is an exciting space that’s helping to push push quantum computing to real-life application and mainstream usage. We’re expecting to see some very interesting developments in the near future.