Hybrid Quantum Computing From Kipu Quantum And Basque Region Boosts Optimisation with Sequential Processor Workflow.

The pursuit of enhanced computational power increasingly focuses on hybrid approaches, integrating the strengths of diverse processing architectures. Researchers are now demonstrating a method called sequential quantum computing, which leverages the complementary capabilities of both analog and digital quantum processors to tackle complex optimisation problems. By efficiently transferring information between these systems via controlled bias fields, the method circumvents individual limitations and achieves improved solution quality with reduced computational demands. This work, detailed in a recent publication by Sebastián V. Romero, Alejandro Gomez Cadavid, Enrique Solano, and Narendra N. Hegade, all from Kipu Quantum GmbH and the University of the Basque Country UPV/EHU, presents an experimental validation of sequential computing, utilising a D-Wave annealer and an IBM quantum processor to solve a combinatorial optimisation problem involving three-term interactions.

Researchers are increasingly exploring the synergy between classical computation and emerging quantum technologies, specifically through hybrid algorithms. A recent investigation details the successful implementation of a variational quantum eigensolver (VQE), a hybrid quantum-classical algorithm used to find the ground state energy of a given system, to optimise a cost function defined by a six-layer feedforward neural network. The neural network, a computational model inspired by the structure and function of biological neural networks, serves as the problem instance for the quantum computation.

The team employed the adaptive derivative-assembled pseudo-Trotter (ADAPT-VQE) ansatz, a specific form of the trial wave function used within the VQE algorithm. The pseudo-Trotter expansion is a method for approximating the time evolution operator, crucial for simulating quantum systems, while the adaptive component dynamically adjusts the expansion to improve accuracy and efficiency. This approach minimises the number of quantum gates required, a significant consideration given the current limitations of quantum hardware.

The computation leverages the capabilities of an IBM quantum processor, specifically designed to execute quantum algorithms. The processor manipulates qubits, the quantum analogue of classical bits, to perform calculations based on the principles of quantum mechanics. The results demonstrate the feasibility of using hybrid quantum-classical algorithms to tackle optimisation problems, potentially offering advantages over purely classical methods in certain scenarios. The successful execution highlights the progress in bridging the gap between theoretical quantum computation and practical implementation.

Further research focuses on scaling these algorithms to larger and more complex problems, and improving the robustness of quantum computations against noise and errors inherent in current quantum hardware. The development of more efficient ansätze, like ADAPT-VQE, and the exploration of alternative quantum algorithms remain key areas of investigation. The ultimate goal is to harness the power of quantum computation to solve problems currently intractable for classical computers, with applications spanning fields such as materials science, drug discovery, and financial modelling.