A paradigmatic task in quantum metrology is that of phase estimation. For this task it is commonly believed, under reasonable assumptions about prior information, that a quadratic advantage in sensitivity is the maximum possible advantage offered by the use of quantum resources. In this work, we consider a channel that...
Lorcán O. Conlon, V. Vijendran, Aritra Das et al.· 0 citations
We establish the ultimate precision limits for estimating a function of multiple Hamiltonian parameters in the presence of Markovian noise. By reducing multiparameter function estimation to an optimization over effective single-parameter embeddings, we derive tight bounds on the quantum Fisher information. We identify...
E. Abbasgholinejad, Lorcán O. Conlon, S. R. Muleady et al.· 0 citations
The most general strategy for estimating parameters of a quantum channel is to probe the channel many times sequentially with the help of coherent ancillary systems. However, finding the strategy that minimizes the mean-squared error (MSE) is a notoriously hard problem, requiring optimization over the initial probe sta...
Andrew Tanggara, Lorcán O. Conlon, Alexey V. Gorshkov et al.· 0 citations
The results show that a single bosonic mode can exhibit exponential quantum learning advantages without entanglement or an increase in system size, identifying accessible Fourier bandwidth as a fundamentally distinct resource for quantum-enhanced learning.
A. A. Baiju, Aritra Das, Ozlem Erkilic et al.· 1 citation
This work considers a qubit noise model where the probability of phase-flip errors is exponentially smaller (in qubit number) compared to the probability of bit-flip errors that occur with probability p, and demonstrates that Heisenberg scaling can be attained for times up to T\propto 1/p^{(N+1)/2} for a N-qubit repeti...
Lorcán O. Conlon, Yu-Xin Wang, E. Abbasgholinejad et al.· 1 citation
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