QuICS Special Seminar

Abstract: Demonstrations of quantum advantage for random circuit and boson sampling over the past few years have generated considerable excitement for the future of quantum computing and has further spurred the development of a wide range of gate-based digital quantum computers, which represent quantum programs as a sequence of quantum gates acting on one and two qubits.

Abstract: Decoding algorithms based on approximate tensor network contraction have proven tremendously successful in decoding 2D local quantum codes such as surface/toric codes and color codes, effectively achieving optimal decoding accuracy. We introduce several techniques to generalize tensor network decoding to higher dimensions so that it can be applied to 3D codes as well as 2D codes with noisy syndrome measurements (phenomenological noise or circuit-level noise).

Abstract: Simulating Fermionic Hamiltonians requires a mapping from fermionic to qubit operators. This mapping must obey the underlying algebra of fermionic operators; in particular, their specific anticommutation relations. The traditional mapping is the Jordan-Wigner encoding, which is simple and qubit minimal, but can incur significant overheads during simulation. This is because the qubit weight of fermionic operators is high, i.e. operators typically must involve many qubits. New mappings address this trade-off and hold other intriguing features.

Abstract: Learning properties of quantum processes is a fundamental task in physics. It is well known that full process tomography scales exponentially in the number of qubits. In this work, we consider online learning quantum processes in a mistake bounded model and prove exponentially improved bounds compared to the stronger notion of diamond norm learning. The problem can be modelled as an interactive game over any given number of rounds, T, between a learner and an adversary.

Abstract: In this session, new and current QuICS members will introduce themselves and their research. Organized by Yi-Kai Liu.

Abstract: The noncommutative sum-of-squares (ncSoS) hierarchy was introduced by Navascues--Pironio--Acin as a sequence of semidefinite programming relaxations for approximating values of "noncommutative polynomial optimization problems," which were originally intended to generalize quantum values of nonlocal games. Recent work has started to analyze the hierarchy for approximating ground energies of local Hamiltonians, initially through rounding algorithms which output product states for degree-2 ncSoS.

Abstract: Concatenating bosonic error-correcting codes with qubit codes can substantially boost the error-correcting power of the original qubit codes. It is not clear how to concatenate optimally, given that there are several bosonic codes and concatenation schemes to choose from, including the recently discovered Gottesman-Kitaev-Preskill (GKP) – stabilizer codes [Phys. Rev. Lett. 125, 080503 (2020)] that allow protection of a logical bosonic mode from fluctuations of the conjugate variables of the mode.

Abstract: Quantum dots are a promising platform to realize practical quantum computing. However, before they can be used as qubits, quantum dots must be carefully tuned to the correct regime in the voltage space to trap individual electrons. Moreover, realizable quantum computing requires tuning of large arrays, which translates to a significant increase in the number of parameters that need to be controlled and calibrated. This necessitates the development of robust and automated methods to bring the device into an operational state.

Abstract: Classical algorithms are often not effective for solving nonconvex optimization problems where local minima are separated by high barriers. In this paper, we explore possible quantum speedups for nonconvex optimization by leveraging the global effect of quantum tunneling. Specifically, we introduce a quantum algorithm termed the quantum tunneling walk (QTW) and apply it to nonconvex problems where local minima are approximately global minima.

Abstract: While quantum walk frameworks make it easy to design quantum algorithms, as evidenced by their wide application across domains, the major drawback is that they can achieve at most a quadratic speedup over the best classical algorithm.  In this work, we generalise the electric network framework – the most general of quantum walk frameworks, into a new framework that we call the multidimensional quantum walk framework, which no longer suffers from the aforementioned drawback, while still maintaining the original classical walk intuition.