Quantum Computing and Simulations: from benchmarking existing devices to developing new platforms based on molecular spin qudits

Since the beginning, in the late 40s, computation was based on classical principles such as mechanics, electromechanics and, nowadays, electronics: these principles represented the basic technology to implement numeric calculation, driven by the need to speed up and automatize the process. Huge improvements have been achieved through the decades, and countless inventions and innovations have contributed to raise computational power while minimizing the size of computers. Although the classical computation has reached incredible peaks, such as IBM Summit with more than 200 quadrillion calculations per second, there are still many problems that cannot be addressed adequately by a classical computer in terms of required resources or computation time. This awareness started to grow since the early 80s, when the need of a computer able to simulate nature without approximations exposed the limits of a classical approach to computation, especially when simulating quantum mechanical systems. Quantum Computation represents the new frontier of information science and could be a breakthrough to solve some kind of problems impossible to be solved with a classical computer. Nowadays we are in the NISQ (Noisy Intermediate Scale Quantum) Computing era, and some market players have released basic versions of quantum processors using different technologies: in this context, IBM is one of the most advanced player, as in 2016 was the first to release on the Cloud an open-source quantum platform called IBM Quantum, with superconducting qubits as basis of quantum hardware, and an initial open-source software stack. Currently there are several technologies to build qubits, for example superconducting transmons, ion traps, molecules and photons. In the NISQ era, the available number of qubits of near-term quantum devices is ? 10 ? 100 and the errors are still important: to define Quantum Computers power, a new set of metrics called have been developed, such as Quantum Volume, taking into account not only the number of qubits available, but also their quality. One of the most useful steams is to understand how to use Quantum Computers to solve important problems, starting from simple but scalable models. While today’s technology is constantly improving over the years in terms of chip quality, speed and scalability, together with the software and application stack, it is s clear that new approaches should be investigated in order to overcome current limitations. Investigations on possible paradigm changes are due to address some current problems, in particular related to noise reduction and Quantum Error Correction implementation.