In the ideal quantum Zeno (QZ) effect, repeated quantum projective measurements can freeze the coherent dynamics of a quantum system. However, in the weak QZ regime, measurement backactions can allow the sensing of semi-classical field fluctuations. In this regard, we theoretically show how to combine the controlled manipulation of a quantum two-level system, used as a probe, with a sequence of projective measurements to have direct access to the noise correlation function. We experimentally test the effectiveness of the proposed noise sensing method on a properly engineered Bose-Einstein condensate of Rb 87 atoms realized on an atom chip. We believe that our QZ-based approach can open a new path towards novel quantum sensing devices.
The classification of big data usually requires a mapping onto new data clusters which can then be processed by machine learning algorithms by means of more efficient and feasible linear separators. Recently, Ref.[10] has advanced the proposal to embed classical data into quantum ones: these live in the more complex Hilbert space where they can get split into linearly separable clusters. Here, we implement these ideas by engineering two different experimental platforms, based on quantum optics and ultra-cold atoms respectively, where we adapt and numerically optimize the quantum embedding protocol by deep learning methods, and test it for some trial classical data. We perform also a similar analysis on the Rigetti superconducting quantum computer. Therefore, we find that the quantum embedding approach successfully works also at the experimental level and, in particular, we show how different platforms could work in a complementary fashion to achieve this task. These studies might pave the way for future investigations on quantum machine learning techniques especially based on hybrid quantum technologies.
The classification of big data usually requires a mapping onto new data clusters which can then be processed by machine learning algorithms by means of more efficient and feasible linear separators. Recently, Lloyd et al. have advanced the proposal to embed classical data into quantum ones: these live in the more complex Hilbert space where they can get split into linearly separable clusters. Here, these ideas are implemented by engineering two different experimental platforms, based on quantum optics and ultra‐cold atoms, respectively, where we adapt and numerically optimize the quantum embedding protocol by deep learning methods, and test it for some trial classical data. A similar analysis is also performed on the Rigetti superconducting quantum computer. Therefore, it is found that the quantum embedding approach successfully works also at the experimental level and, in particular, we show how different platforms could work in a complementary fashion to achieve this task. These studies might pave the way for future investigations on quantum machine learning techniques especially based on hybrid quantum technologies.
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the use of the dressed chopped random basis optimal control algorithm to realize time‐reversal procedures. The latter are aimed for the implementation of quantum undo operations in quantum technology contexts as quantum computing and quantum communications. The last performed operation can be time‐reversed via the undo command so as to perfectly restore a condition in which any new operation, chosen by the external user, can be applied. By generalizing this concept, the undo command can also allow for the reversing of a quantum operation in a generic time instant of the past. Here, thanks to optimal time‐reversal routines, all these functionalities are experimentally implemented on the fivefold F=2$F=2$ Hilbert space of a Bose–Einstein condensate of non‐interacting 87Rb atoms in the ground state, realized with an atom chip. Each time‐reversal transformation is attained by designing an optimal modulated radio frequency field, achieving on average an accuracy of around 92% in any performed test. The experimental results are accompanied by a thermodynamic interpretation based on the Loschmidt echo. These findings are expected to promote the implementation of time‐reversal operations in a real scenario of gate‐based quantum computing with a more complex structure than the five‐level system considered here.
A quantum embedding protocol embeds classical data into quantum ones living in the more complex Hilbert space where, for instance, they can get split into linearly separable clusters (data classification) or processed for an hybrid quantum machine learning application. The cover image represents the result of this embedding on the Bloch sphere via a parameterized quantum circuit. In article number 2100140 by Filippo Caruso and colleagues, the successful experimental implementations have been realized on two complementary, atomic and photonic, platforms, and compared with a similar analysis running on the cloud‐accessible Rigetti superconducting quantum processor. (Image created by I. Mastroserio)
We are developing an atom-based platform for investigating the non-classical nature of mid infrared light emitted by Quantum Cascade Laser frequency combs, based on four wave mixing 87Rb.
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