Classiq Provides a New Way to Building Quantum Algorithms

Article By : Maurizio Di Paolo Emilio

The concept behind designing quantum algorithms is to translate code into qubits so that quantum technology can develop at a faster pace and with less uncertainty.

A quantum computer does not only represent a hardware issue. It also represents a software issue. Many startups building quantum software are entering the market and are drawing the attention of investors. Classiq, a Tel Aviv-based startup that provides a model for building quantum algorithms, is one of them.

In an interview with EE Times Europe, Yuval Boger, chief marketing officer at Classiq, said that while algorithms for quantum computers have so far been built using low-level tools with high development times, Classiq’s platform aims to model these algorithms at a much higher level of abstraction. “It basically provides the quantum equivalent of chip design tools for conventional systems provided by companies like Cadence. This capability will improve the design and implementation techniques,” said Boger.

He added, “Hardware doesn’t go anywhere without good software. If I give you a high-end processor, and there’s no operating system, then it won’t do any good. Circuits are developed at the gate level today, and if you don’t have a PhD in quantum physics, then your chances of working in quantum computing are slim. It is important to have the right platform to then easily develop quantum circuits. Our platform synthesizes and analyses quantum circuits at the fundamental level, regardless of the number of qubits they contain.”

Quantum computing

In previous articles, we have seen that quantum computers use qubits as a computation unit, unlike traditional computers that use the 0 and 1 bit. A qubit can be set for 0 and 1 simultaneously, and this will theoretically increase the computing power. Many market experts and researchers expect quantum computers to advance artificial intelligence by training more complex models.

Quantum computing promises to dramatically impact numerous fields, from cybersecurity to finance, from supply chain to pharmaceuticals, from defense to weather forecasting.

The challenge for qubits is to make them increasingly stable in order to optimize performance. Platforms with more than 1000 qubits will appear in the coming years with several companies like IBM, Amazon, and Microsoft investing more and more. Companies are competing on several aspects: the number of qubits, the type of ports available, connectivity between qubits, error rates, operating temperature.

Quantum algorithm design

A modern CPU is useless without an operating system and support software tools in today’s computer world. The same is true in a quantum computer. As important as the hardware is, the software is also crucial to powering a quantum revolution.

The complexity of writing quantum software has another unfortunate side effect: It is difficult to find experts in quantum programming, as this differs from classical programming. Quantum programming experts need to know about both software engineering and quantum physics.

Classiq addresses the challenges in the development of quantum computing by bridging the gap of complex quantum logic. The company builds a new layer of the quantum software stack, increasing the level of abstraction and allowing developers to implement their ideas and concepts without the need to design the specific quantum circuit at the gate level.

Classiq platform interface

“Many quantum algorithms need to prepare the state; they need to load probability functions,” said Boger. “If you wrote some C code, you would organize the arithmetic as we know it. But how do you do that in quantum? You have to create some kind of circuit that does that. Our software can help you figure out which is better in terms of quantum. We allow you to design at a high level, specifying the constraints. We also synthesize an optimal circuit by estimating the computational resources to run it on various hardware.”

The evolution of digital circuit design has inspired Classiq. As digital circuits became more complex, design languages such as VHDL came to the aid of designers. With VHDL, engineers write down what they want to do with digital circuits and then compilers translate this high-level description into detailed gate interconnections with careful debugging.

Classiq is based on a variant of Python through which you write quantum constraints on what your circuit wants to achieve. Then, the circuit is synthesized to be processed by the quantum architecture. Boger argues that, as the technology improves, developers will need to have less of an understanding of how real qubits behave. Classiq’s mission, after all, is to provide an additional layer of abstraction on top of the hardware. At the same time, though, developers can optimize their algorithms for specific quantum computing hardware.

“It’s sort of like VHDL for quantum. We take a system-level approach and adapt it to the number of qubits you want to control and the entanglement levels,” Boger said.

The concept behind designing quantum algorithms is to translate code into qubits so that quantum technology can develop at a much faster pace and with much less uncertainty. “Instead of coding at the gate level, you can create a model to coordinate your algorithm and then synthesize this model into code at the gate level, taking into account the various operational constraints at the hardware level. This will make it easy to port from one architecture to another,” Boger concluded.

In January 2021, Classiq raised $10.5 million in a Series A round led by Team8 Capital and Wing Capital. Entrée Capital, crowdfunding platform OurCrowd and Sumitomo Corporation (through IN Venture) also participated in this round. In 2020, Classiq also closed a $4 million seed round led by Entrée Capital.

This article was originally published on EE Times Europe.

Maurizio Di Paolo Emilio holds a Ph.D. in Physics and is a telecommunication engineer and journalist. He has worked on various international projects in the field of gravitational wave research. He collaborates with research institutions to design data acquisition and control systems for space applications. He is the author of several books published by Springer, as well as numerous scientific and technical publications on electronics design.

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