Quantum transport simulations in a programmable nanophotonic processor

By Nicholas Christopher Harris, Gregory R. Steinbrecher, Mihika Prabhu, Y. Lahini, Jacob Mower, Darius Bunandar, Changchen Chen, F. Wong, T. Baehr‐Jones, Michael Hochberg and 2 more. Nature Photonics, 2015

504 citations recorded by the source corpus.

Environmental noise and disorder play critical roles in quantum particle and wave transport in complex media, including solid-state and biological systems. While separately both effects are known to reduce transport, recent work predicts that in a limited region of parameter space, noise-induced dephasing can counteract localization effects, leading to enhanced quantum transport. Photonic integrated circuits are promising platforms for studying such effects, with a central goal of developing large systems providing low-loss, high-fidelity control over all parameters of the transport problem. Here, we fully map the role of disorder in quantum transport using a nanophotonic processor: a mesh of 88 generalized beamsplitters programmable on microsecond timescales. Over 64,400 experiments we observe distinct transport regimes, including environment-assisted quantum transport and the ‘quantum Goldilocks’ regime in statically disordered discrete-time systems. Low-loss and high-fidelity programmable transformations make this nanophotonic processor a promising platform for many-boson quantum simulation experiments. A large-scale, low-loss and phase-stable programmable nanophotonic processor is fabricated to explore quantum transport phenomena. The signature of environment-assisted quantum transport in discrete-time systems is observed for the first time.

Subfields: Acoustics and Ultrasonics, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/7e90b834252069486bc95fea004867b97baef222

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In Acoustics and Ultrasonics

Mikhail D. Lukin

In Atomic and Molecular Physics, and Optics

Citations in the corpus

Quantum Simulators: Architectures and Opportunities (2019, background): “, gated quantum dots and photonic arrays—are on the cusp of opening unique and important new areas of investigation, such as modeling many-body quantum transport in engineered reservoirs and predicting properties of quantum complex networks for a quantum internet [17, 18].”
Integrated photonic quantum technologies (2019, methodology): “emely low-error single-qubit and two-qubit operations (Box 2), key for on-chip quantum information processing. Si-based MZIs with a 65dB on-off ratio has been achieved with near-perfect beamsplitters [41], equivalent to having a Pauli-Z error rate of < 10−6 [42]. With ultra-high precise operation”
Space-efficient optical computing with an integrated chip diffractive neural network (2022, background): “This reduction in the resource scaling from quadratic to linear will have a profound impact on the realization of large-scale silicon-photonics computing circuits with current fabrication technologies 38 – 40 .”
Classical boson sampling algorithms with superior performance to near-term experiments (2017, background): “Schemes for implementing arbitrary unitary linear optical transfer matrices using beam-splitters and phase-shifters are well known [13, 14] and have been realised successfully [15, 16].”
Topological characterization of chiral models through their long time dynamics (2017, background): “IV may be implemented by periodical modulation of the separation between the waveguides along the propagation direction [43, 45], and long-ranged tunnelings may be obtained by letting the waveguides propagate out of the plane, as possible in 3D-photonic chips [46, 47].”
Chip-to-chip quantum teleportation and multi-photon entanglement in silicon (2019, background): “Silicon quantum photonics is of particular interest, having recently shown large integration of two-photon sources and circuits [13], precise control of photon states [14, 15], and efficient single-photon detection [16].”