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Austrian Startup ParityQC Releases Video on Optimization Problems Solved with Quantum Computers

In the field of quantum computing, researchers and companies are exploring the potential for quantum algorithms to solve complex optimization problems more efficiently than classical computers. ParityQC, an Austrian startup, is at the forefront of this endeavour, developing tools and software specifically designed to harness the power of quantum computers for optimization problem-solving.

In a recent video, Barry Mant and Josua Unger of ParityQC shed light on the role of architecture in quantum computing’s ability to tackle these challenging problems.

The Significance of Optimization Problems

Optimization problems are pervasive across various industries, ranging from engineering to telecommunications and commerce. The objective is not merely to find a viable solution but to determine the best possible solution, often while adhering to additional problem-specific constraints. Barry Mant —who works with theoretical chemistry and is a use case developer working on optimization problems at ParityQC — explains this through an example: planning an itinerary to visit multiple cities while minimizing distance and cost. As the scale and complexity of optimization problems increase, more sophisticated optimization techniques and greater computing power become essential.

Quantum Computing’s Promise in Optimization

With a background in particle and mathematical physics, quantum software engineer Josua Unger emphasizes that in the near future, quantum computers may prove highly effective in solving large and intricate optimization problems. While different approaches and hardware platforms are being developed, the fundamental method for solving optimization problems remains the same. The problem is translated into a mathematical expression, representing the energy of an arrangement of qubits. Solutions correspond to specific arrangements of qubits, and the best solution, known as the ground state, corresponds to the lowest energy arrangement. By performing operations on the qubits, quantum computers aim to find this ground state efficiently.

“Optimization problems arise in almost all industries from engineering to telecommunications and commerce. In these problems, we don’t only want to find a viable solution, but we want to find the best possible solution and this might involve additional rules for the problem too.”

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 — Barry Mant

Translating Problems for Quantum Computers

To leverage the power of quantum computers, optimization problems must be transformed into a specific mathematical form that suits quantum algorithms. Josua Unger highlights the pivotal role of use-case developers in this process. They work to convert the problem into a suitable form, optimizing the utilization of quantum resources. Making smart choices during this translation step enhances the efficiency of quantum computing, a crucial aspect given the current limitations of available qubits and runtimes.

ParityQC’s Architecture

Many hard optimization problems involve interconnected parameters, resulting in complex connections across the qubits of a quantum device. Barry Mant explains that ParityQC’s architecture offers benefits in this context. It enables the conversion of the problem into a form that necessitates only local qubit interactions, simplifying implementation. The focus on locality also allows for parallel quantum operations, reducing runtime and minimizing the potential for errors.

“It is likely that in the near future, quantum computers carrying out quantum algorithms can be used to solve large and difficult optimization problems. Various approaches and hardware platforms are currently developed, but for optimization problems, the basic approach is the same. The problem is converted to a mathematical expression for the energy of an arrangement of qubits. Solutions then correspond to an arrangement of qubits, some pointing up and some pointing down. The best solution is the lowest energy arrangement, which we call the ground state. The quantum computer runs operations on the qubits, which aims to find this ground state. Quantum computers require problems to be given in a very specific form.”

 — Josua Unger

ParityQC is making strides in harnessing the power of quantum computing to solve hard optimization problems efficiently. Barry Mant and Josua Unger, through their expertise and experience, shed light on the challenges and potential solutions in this realm. As quantum computers continue to advance, the optimization capabilities they offer hold tremendous promise across industries that rely on solving complex problems. ParityQC’s dedication to refining optimization approaches and leveraging architectural advantages exemplifies its commitment to shaping the future of quantum computing.

Featured image: ParityQC

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James Dargan

James Dargan is a writer and researcher at The Quantum Insider. His focus is on the QC startup ecosystem and he writes articles on the space that have a tone accessible to the average reader.

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The Future of Materials Discovery: Reducing R&D Costs significantly with GenMat’s AI and Machine Learning Tools

When: July 13, 2023 at 11:30am

What: GenMat Webinar

Picture of Jake Vikoren

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