Dedicated hardware for quantum-inspired algorithms
Quantum computer prototypes and exact emulators that exist today are typically capable of handling about 40 qubits, making them unsuitable for solving problems in industry. However, quantum methods inspire efficient solutions that proved to be useful in applications such as logistics, quantum chemistry and drug development, financial modelling, and materials science. Many companies (Toshiba, Fujitsu, NTT) have developed or acquired through acquisitions (Microsoft, Amazon) the so-called quantum-inspired optimisers implemented on FPGA or GPU boards, capable of solving large-scale real-world problems with sufficient accuracy for practical purposes. These solutions are usually offered in one package with exact emulators and quantum hardware (Microsoft Azure Quantum, Amazon Quantum Solutions). Another rapidly evolving field is hybrid quantum-classical systems, where small and noisy quantum chips are integrated with classical computers to solve certain sub-tasks of calculations faster. These structures form an intermediate state between fully classical and fully quantum computers and may play a decisive role in the next two decades. The aim of our work is to develop efficient quantum-inspired algorithms and hybrid quantum-classical integrated systems and to build them in hardware (CPU, GPU, FPGA and optical).