Simulating and Mitigating Crosstalk

Filtering Crosstalk from Bath Non-Markovianity via Spacetime Classical Shadows

From an open system perspective non-Markovian effects due to a nearby bath or neighboring qubits are dynamically equivalent. However, there is a conceptual distinction to account for: neighboring qubits may be controlled. We combine recent advances in non-Markovian quantum process tomography with the framework of classical shadows to characterize spatiotemporal quantum correlations. Observables here constitute operations applied to the system, where the free operation is the maximally depolarizing channel. Using this as a causal break, we systematically erase causal pathways to narrow down the progenitors of temporal correlations. We show that one application of this is to filter out the effects of crosstalk and probe only non-Markovianity from an inaccessible bath. It also provides a lens on spatiotemporally spreading correlated noise throughout a lattice from common environments. We demonstrate both examples on synthetic data. Owing to the scaling of classical shadows, we can erase arbitrarily many neighboring qubits at no extra cost. Our procedure is thus efficient and amenable to systems even with all-to-all interactions.

Optimal Control of Families of Quantum Gates

Quantum optimal control (QOC) enables the realization of accurate operations, such as quantum gates, and supports the development of quantum technologies. To date, many QOC frameworks have been developed, but those remain only naturally suited to optimize a single targeted operation at a time. We extend this concept to optimal control with a continuous family of targets, and demonstrate that an optimization based on neural networks can find families of time-dependent Hamiltonians realizing desired classes of quantum gates in minimal time.

Natural quantum reservoir computing for temporal information processing

Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics to learn a dynamical system and generate the target time-series. This paper proposes the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits. The performance of this natural quantum reservoir is demonstrated in a benchmark time-series regression problem and a practical problem classifying different objects based on temporal sensor data. In both cases the proposed reservoir computer shows a higher performance than a linear regression or classification model. The results indicate that a noisy quantum device potentially functions as a reservoir computer, and notably, the quantum noise, which is undesirable in the conventional quantum computation, can be used as a rich computation resource.