Searcharxiv⌕ Search

arXiv subjects

Brian Coyle

Publications and source records attributed to Brian Coyle.

21 records · Page 2Linked to original sources

Certified Randomness From Steering Using Sequential Measurements

The generation of certifiable randomness is one of the most promising applications of quantum technologies. Furthermore, the intrinsic non-locality of quantum correlations allow us to certify randomness in a device-independent way, i.e. one need not make assumptions about the devices used. Due to the work of Curchod et. al., a single entangled two-qubit pure state can be used to produce arbitrary amounts of certified randomness. However, the obtaining of this randomness is experimentally challenging as it requires a large number of measurements, both projective and general. Motivated by these difficulties in the device-independent setting, we instead consider the scenario of one-sided device independence where certain devices are trusted, and others not; a scenario motivated by asymmetric experimental set-ups such as ion-photon networks. We show how certain aspects of previous work can be adapted to this scenario and provide theoretical bounds on the amount of randomness which can be certified. Furthermore, we give a protocol for unbounded randomness certification in this scenario, and provide numerical results demonstrating the protocol in the ideal case. Finally, we numerically test the possibility of implementing this scheme on near-term quantum technologies, by considering the performance of the protocol on several physical platforms.

quant-ph↗

Robust data encodings for quantum classifiers

Data representation is crucial for the success of machine learning models. In the context of quantum machine learning with near-term quantum computers, equally important considerations of how to efficiently input (encode) data and effectively deal with noise arise. In this work, we study data encodings for binary quantum classification and investigate their properties both with and without noise. For the common classifier we consider, we show that encodings determine the classes of learnable decision boundaries as well as the set of points which retain the same classification in the presence of noise. After defining the notion of a robust data encoding, we prove several results on robustness for different channels, discuss the existence of robust encodings, and prove an upper bound on the number of robust points in terms of fidelities between noisy and noiseless states. Numerical results for several example implementations are provided to reinforce our findings.

quant-ph↗

One-Sided Device-Independent Certification of Unbounded Random Numbers

The intrinsic non-locality of correlations in Quantum Mechanics allow us to certify the behaviour of a quantum mechanism in a device independent way. In particular, we present a new protocol that allows an unbounded amount of randomness to be certified as being legitimately the consequence of a measurement on a quantum state. By using a sequence of non-projective measurements on single state, we show a more robust method to certify unbounded randomness than the protocol of Churchod et al., by moving to a one-sided device independent scenario. This protocol also does not assume any specific behaviour of the adversary trying to fool the participants in the protocol, which is an advantage over previous steering based protocols. We present numerical results which confirm the optimal functioning of this protocol in the ideal case. Furthermore, we also study an experimental scenario to determine the feasibility of the protocol in a realistic implementation. The effect of depolarizing noise is examined, by studying a potential state produced by a networked system of ion traps.

quant-ph↗