Stefano Paesani

60 dB high-extinction auto-configured Mach-Zehnder interferometer.

Imperfections in integrated photonics manufacturing have a detrimental effect on the maximal achievable visibility in interferometric architectures. These limits have profound implications for further technological developments in photonics and in particular for quantum photonic technologies. Active optimization approaches, together with reconfigurable photonics, have been proposed as a solution to overcome this. In this Letter, we demonstrate an ultrahigh (>60  dB) extinction ratio in a silicon photonic device consisting of cascaded Mach-Zehnder interferometers, in which additional interferometers function as variable beamsplitters. The imperfections of fabricated beamsplitters are compensated using an automated progressive optimization algorithm with no requirement for pre-calibration. This work shows the possibility of integrating and accurately controlling linear-optical components for large-scale quantum information processing and other applications.

Multidimensional quantum entanglement with large-scale integrated optics

Large-scale integrated quantum optics The ability to pattern optical circuits on-chip, along with coupling in single and entangled photon sources, provides the basis for an integrated quantum optics platform. Wang et al. demonstrate how they can expand on that platform to fabricate very large quantum optical circuitry. They integrated more than 550 quantum optical components and 16 photon sources on a state-of-the-art single silicon chip, enabling universal generation, control, and analysis of multidimensional entanglement. The results illustrate the power of an integrated quantum optics approach for developing quantum technologies. Science, this issue p. 285 Large-scale integrated quantum optical circuitry is demonstrated on a single silicon chip. The ability to control multidimensional quantum systems is central to the development of advanced quantum technologies. We demonstrate a multidimensional integrated quantum photonic platform able to generate, control, and analyze high-dimensional entanglement. A programmable bipartite entangled system is realized with dimensions up to 15 × 15 on a large-scale silicon photonics quantum circuit. The device integrates more than 550 photonic components on a single chip, including 16 identical photon-pair sources. We verify the high precision, generality, and controllability of our multidimensional technology, and further exploit these abilities to demonstrate previously unexplored quantum applications, such as quantum randomness expansion and self-testing on multidimensional states. Our work provides an experimental platform for the development of multidimensional quantum technologies.

Witnessing eigenstates for quantum simulation of Hamiltonian spectra

We introduce the concept of an eigenstate witness and use it to find energies of quantum systems with quantum computers. The efficient calculation of Hamiltonian spectra, a problem often intractable on classical machines, can find application in many fields, from physics to chemistry. We introduce the concept of an “eigenstate witness” and, through it, provide a new quantum approach that combines variational methods and phase estimation to approximate eigenvalues for both ground and excited states. This protocol is experimentally verified on a programmable silicon quantum photonic chip, a mass-manufacturable platform, which embeds entangled state generation, arbitrary controlled unitary operations, and projective measurements. Both ground and excited states are experimentally found with fidelities >99%, and their eigenvalues are estimated with 32 bits of precision. We also investigate and discuss the scalability of the approach and study its performance through numerical simulations of more complex Hamiltonians. This result shows promising progress toward quantum chemistry on quantum computers.

Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond

The efficient characterization and validation of the underlying model of a quantum physical system is a central challenge in the development of quantum devices and for our understanding of foundational quantum physics. However, the impossibility to efficiently predict the behaviour of complex quantum models on classical machines makes this challenge to be intractable to classical approaches. Quantum Hamiltonian Learning (QHL) [1, 2] combines the capabilities of quantum information processing and classical machine learning to allow the efficient characterisation of the model of quantum systems. In QHL the behaviour of a quantum Hamiltonian model is efficiently predicted by a quantum simulator, and the predictions are contrasted with the data obtained from the quantum system to infer the system Hamiltonian via Bayesian methods.

Noise resilience of Bayesian quantum phase estimation tested on a Si quantum photonic chip

Quantum Phase Estimation (PE) is a fundamental building block in the framework of Quantum Computing. Accurate estimation of the true eigenphase φ0 of a known eigenstate |φ〉 is fundamental for the implementation of many promising quantum algorithms. The interest in PE is also due to the modest quantum hardware requirements of the Iterative Phase Estimation Algorithm (IPEA) implementation [1], successfully implemented on small-scale devices [2]. However, IPEA makes hard decisions at each step of the algorithm, relying on majority voting schemes for its robustness against noise. It has been observed how non-error-corrected machines may enter a regime where error-rates for IPEA diverge quickly, making this approach impractical [3].