Classiq Brings Abstraction Layer to Quantum Software Stack – The New Stack

Classiq Brings Abstraction Layer to Quantum Software Stack – The New Stack


How does software work even on quantum computers and how are “quantum algorithms” created? These were my initial questions for Nir Minerbi, co-founder and CEO of Classiq, an Israeli software company that offers a software development platform for quantum computing.

“Quantum software is manipulating the quantum state vector in a way that will solve your problem,” Minerbi replied. This is done by “multiplying it by a unitary matrix,” which, in simple terms, means using a lot of math and logic. The result is that the development of quantum software, at this time, involves programming physical states on a quantum computer. The company describes it as “a bit like electronic design and a bit like assembly language.”

As you can probably guess, writing quantum software is incredibly difficult. As Minerbi said, when it comes to writing a matrix of unity, “it’s very hard to think that way, is it?” The second problem that developers will have with quantum computing is the size of the calculations. “In a 300-qubit machine,” Minerbi explained, “the size of this unitary matrix is ​​2 to the power of 300, which is greater than the number of atoms in the universe.” This means that problems must be divided into smaller workloads (1 or 2 qubit operations).

Going one layer above the quantum circuits

This is where the “quantum circuits” come into play. According to Wikipedia, “a quantum circuit is a model of quantum computing, similar to classical circuits, in which a calculus is a sequence of quantum gates, measurements, initializations of qubits to known values, and possibly other actions.”

“So today,” Minerbi said, “this is basically the level of abstraction of the quantum software stack. You have to design these circuits, which are the door-level software.”

To create a quantum circuit, Classiq described in a presentation he showed me, “one specifies which ‘qubits’ (cables) are connected to which ‘gates’ (square blocks). This is done in quantum assembly language.”

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A quantum circuit, using Classiq.

The circuits are then run on a quantum computer, which is a physical process. “Finally, each of these door-level operations will be compiled into a physical, microwave, or laser operation, which will actually be applied to the physical qubits,” Minerbi explained.

What Classiq has done is add a layer of abstraction to the current state of quantum computing, which, as noted, is at the door and machine level. “The methods and tools that were developed in the classical stack over the last 60 years, we will take them to the quantum stack,” Minerbi said.

To achieve this layer of abstraction, Classiq created a high-level functional model that can be translated into quantum assembly language. So, as a user, model the circuit design (using Python or VSCode) and then the Classiq platform turns it into real circuit code. Then run this code on one of the major quantum computing processing services, such as Qiskit or Amazon Braket.

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From the high-level model to the quantum circuit

As stated on the Classiq platform description page, developers are advised to “combine integrated quantum modules with user-defined ones” and then “set constraints such as the number of ports, the depth of the circuit, and interlacing levels “. Once all this is done, Classiq produces a quantum circuit that can be used on Qiskit, Braket or another platform.

But is there any reason why developers shouldn’t use Qiskit or Braket directly, I asked Minerbi?

“Basically what allows you to do Qiskit, which is very important, is to design door-level circuits in a convenient way,” he replied. “But it’s still at the door level, because you need to know which doors to put where and which building blocks to use. And we’re a layer on top of that: we’re on the functionality layer of high-level modeling, and our engine synthesis is generating the quantum circuit in Qiskit or any other language “.

How Python Developers Can Get Started in Quantum Computing

To create the logic of a circuit, what Classiq calls a high-level model, developers can use the company’s VSCode extension (which has drag-and-drop functionality) or a Python SDK.

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Model (in Python) on the left, circuit design in the middle and circuit code on the right.

I asked Minerbi how difficult it is for a web developer familiar with Python to start creating quantum computing software on Classiq?

“So we basically have two types of users,” he replied. “The first guy is a quantum expert. These people […] they are very familiar with quantum algorithms and use the platform to achieve state-of-the-art results that they were unable to achieve with Qiskit and other platforms. And the second [kind of user] they are Python developers or developers of machine learning, etc., who use Classiq to enter quantum computing. “

Typically, Python and ML developers start with a few use cases that are good for participants in quantum computing. An example is Grover’s algorithm, which is often used to speed up unstructured search. It’s good to begin with, Minerbi said, “because it’s about embedding classical logic in a quantum circuit.”

While Python and ML developers don’t necessarily need to understand quantum information theory to understand an algorithm like Grover’s, it helps if they have training in algebra, Minerbi added.

Because quantum computing is still very new, especially when it comes to software development, physicists outnumber computer scientists in the industry at the moment. However, this is changing and more computer scientists are entering the field.

“In our company, for example, there are about 45 people,” Minerbi said. “Some of them are doctors and postdocs of quantum information, others are computer scientists without training in physics, and the combination is powerful.”

Conclusion

The classical computer industry has added layers of abstraction over the years, with each new layer facilitating software development. It went from assembly language to higher-level languages ​​(like Python), but we’ve also seen it in the web world: in the early to mid-1990s, a web page had to be coded in “raw”. HTML, but now you can do it with any number of drag-and-drop design tools. So it’s nice to see that this same level of abstraction is starting to add up to quantum computing.

It is clear, however, that we are in the early days of defining the stack of quantum software. Classiq has some competition. A company called Horizon Quantum Computing is “developing a complete stack of compilers” for quantum software, and major service providers like IBM and Amazon offer their own SDKs. But Classiq’s solution seems to be one of the most compelling abstraction layers to date.



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