Todays quantum computers are imperfect. They are made of dozens or hundreds of qubits that can be prepared in highly nonclassical states but, being very sensitive to noise, their ability to preserve quantum properties is very limited. Noise not only arises from the interaction with their external environment, but encompasses all the imperfections in the sophisticated quantum hardware and control system. This is why, despite the discovery of algorithms that, in principle, would allow us to simulate interesting and currently intractable problems in chemistry and materials, many scientists in academia and companies are shifting their attention away from near-term quantum computers and towards fault-tolerant devices.
In this talk I will argue that, as we move towards fault-tolerant quantum computers, hybrid quantum algorithms on near-term quantum computers can lead to quantum advantage already in the near future. One of the key ingredients to unlock quantum advantage in noisy devices is the use of informationally complete (IC) generalised measurements (IC POVMs) [1-4]. I will present results showing how hybrid variational quantum-classical algorithms using IC data allow for unprecedented noise mitigation , runtime reduction , and ansatz generation.
The combination of these three achievements will unlock quantum advantage on near-term devices.
 “Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms”, Guillermo García-Pérez, Matteo A. C. Rossi, Boris Sokolov, Francesco Tacchino, Panagiotis Kl. Barkoutsos, Guglielmo Mazzola, Ivano Tavernelli, Sabrina Maniscalco, PRX Quantum 2, 040342 (2021)
 “Adaptive POVM implementations and measurement error mitigation strategies for near-term quantum devices”, Adam Glos, Anton Nykänen, Elsi-Mari Borrelli, Sabrina Maniscalco, Matteo A. C. Rossi, Zoltán Zimborás, Guillermo García-Pérez, arXiv:2208.07817
 “Ancilla-free implementation of generalized measurements for qubits embedded in a qudit space”,Laurin E. Fischer, Daniel Miller, Francesco Tacchino, Panagiotis Kl. Barkoutsos, Daniel J. Egger, Ivano Tavernelli, Phys. Rev. Research 4, 033027 (2022)-
 “Virtual linear map algorithm for classical boost in near-term quantum computing”, Guillermo García-Pérez, Elsi-Mari Borrelli, Matea Leahy, Joonas Malmi, Sabrina Maniscalco, Matteo A. C. Rossi, Boris Sokolov, Daniel Cavalcanti, arXiv:2207.01360
The Seminar will be followed by light refreshments
served in the Leonardo Building lobby