Indexed on: 13 Apr '16Published on: 13 Apr '16Published in: Quantum Physics
Control engineering techniques are emerging as a promising approach to realize the stabilisation of quantum systems, and a powerful complement to attempts to design-in passive robustness. However, applications to date have largely been limited by the challenge that projective measurement of quantum devices causes the collapse of quantum superposition states. As a result significant tradeoffs have been mandated in applying the concept of feedback, and experiments have relied on open-loop control, weak measurements, access to ancilla states, or largely sacrificing quantum coherence in the controlled system. In this work we use techniques from control theory and machine learning to enable the real-time feedback suppression of semiclassical decoherence in a qubit when access to measurements is limited. Using a time-series of measurements of a qubit's phase we are able to predict future stochastic evolution without requiring a deterministic model of qubit evolution. We demonstrate this capability by preemptively stabilising predicted qubit dephasing in two experiments. First, we realise periods of stabilised qubit operation during which projective measurements are not performed via a non-destructive time-division multiplexed approach. Second, we implement predictive feedback in a periodic loop where the presence of long free-evolution periods normally causes decorrelation between measurement outcomes and the qubit state at the time of control actuation. Both experiments demonstrate quantitative improvements in qubit phase stability relative to "traditional" measurement-based feedback approaches, including enhanced long-term qubit phase stabilisation. Our approach is extremely simple and applicable to any qubit with the ability to perform projective measurement, requiring no hardware modifications, alternate measurement strategies, or access to exotic ancilla states.