Austrian Startup ParityQC Releases Video on Optimization Problem Solved with Quantum Computers
In the field of quantum computing, researchers and companies are exploring the potential of quantum algorithms to solve complex optimization problems more efficiently than classical computers. ParityQCan Austrian startup, is at the forefront of this effort, developing tools and software specifically designed to harness the power of quantum computers for optimization problem solving.
In a recent video, Barry Mant And Joshua Unger from ParityQC describes the role of architecture in quantum computing capabilities to overcome this challenging problem.
Significance of the Optimization Problem
Optimization issues are widespread across industries ranging from engineering to telecommunications and commerce. The goal is not just to find a feasible 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—explain this through an example: plan an itinerary to visit multiple cities while minimizing distance and cost. As the scale and complexity of optimization problems increases, more sophisticated optimization techniques and greater computing power become necessary.
The Promise of Quantum Computing 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 at solving large and complex optimization problems. While different approaches and hardware platforms are being developed, the basic methods for solving optimization problems remain the same. The problem is translated into a mathematical expression, which represents the energy of the qubit array. The solution corresponds to a certain qubit arrangement, and the best solution, known as the ground state, corresponds to the lowest energy setting. By performing operations on qubits, quantum computers aim to efficiently find these ground states.
“Optimization problems arise in almost all industries from engineering to telecommunications and commerce. In this problem, we not only want to find a feasible solution, but we also want to find the best solution and this may involve additional rules for the problem as well.”
— Barry Mant
Translating Problems for Quantum Computers
To harness the power of quantum computers, optimization problems must be transformed into specific mathematical forms that are compatible with quantum algorithms. Josua Unger highlights the critical role use case developers play in this process. They work to transform problems into suitable forms, optimizing the utilization of quantum resources. Making intelligent choices during this step of translation increases the efficiency of quantum computing, an important aspect given the limitations of currently available qubits and runtimes.
ParityQC architecture
Many difficult optimization problems involve parameters that are interconnected, resulting in complex connections across qubits of quantum devices. Barry Mant explains that the ParityQC architecture offers benefits in this context. This allows the conversion of matter into a form that requires only local qubit interaction, simplifying implementation. The focus on locality also enables parallel quantum operations, reducing processing times and minimizing the potential for errors.
“Possibly in the near future, quantum computers running quantum algorithms could be used to solve large and difficult optimization problems. Various approaches and hardware platforms are currently being developed, but for optimization issues, the basic approach is the same. The problem is turned into a mathematical expression for the energy of the qubit array. The solutions then match the arrangement of the qubits, some pointing up and some pointing down. The best solution is the lowest energy setting, which we call the ground state. Quantum computers perform operations on qubits, which aim to find these ground states. Quantum computers require problems to be given in a very specific form.”
— Joshua Unger
ParityQC takes steps in harnessing the power of quantum computing to efficiently solve difficult optimization problems. Barry Mant and Josua Unger, through their expertise and experience, describe the challenges and potential solutions in this area. As quantum computers continue to advance, the optimization capabilities they offer hold great promise across industries that rely on solving complex problems. ParityQC’s dedication to perfecting optimization approaches and leveraging architectural advantages demonstrates its commitment to shaping the future of quantum computing.
Featured image: ParityQC
