Developing practical EDA tools for quantum software development may still seem distant but Classiq, a Israel-based quantum software specialist, has just posted an interesting paper — Design and synthesis of scalable quantum programs — suggesting EDA tools may be not be so far off.
While hardware development has raced ahead, says Classicsoftware and software tools have lagged. It is itself, of course a quantum software specialist and one complication that it and its quantum software brethren have had to deal with is the proliferation of different quantum hardware types (think of the many varying qubit modalities) and the frequent architecture revisions even within a single quantum computer developer.
The broad vision is development of EDA tools that rely on abstraction rather a library of handcrafted circuits, which is mostly the current practice, says Classiq.
“We present a scalable, robust approach to creating quantum programs of arbitrary size and complexity. The approach is based on the true abstraction of the problem. The quantum program is expressed in terms of a high-level model together with constraints and objectives on the final program. Advanced synthesis algorithms transform the model into a low-level quantum program that meets the user’s specification and is directed at a stipulated hardware. This separation of description from implementation is essential for scale. The technology adapts electronic design automation methods to quantum computing, finding feasible implementations in a virtually unlimited functional space,” according to the paper.
As an example, Classiq walks through developing a circuit for a “Quantum walk on a circle”.
In the Results Section, the researchers write, ‘We present results showing the power of our approach, in which a truly abstract description implies functional flexibility that brings about optimized gate-level quantum programs. We use Qmod and the Classiq open library to model the abstract functionality of the program and synthesize concrete programs under hardware constraints and optimization functions using the synthesis engine, as described in the Methods section and the Supplementary Materials. We compare our results to those achieved by today’s state-of-the-art quantum coding tools. The problems we have chosen for demonstration are the most generic one can think of, and the improvement in the results compared to state-of-the-art tools is only due to the new principles introduced by our approach.”
Figure 2 from the paper is present below comparing performance between several tools. This is a relatively short paper and is best read directly.
