320 research outputs found
Experimental Bayesian Quantum Phase Estimation on a Silicon Photonic Chip
Quantum phase estimation is a fundamental subroutine in many quantum
algorithms, including Shor's factorization algorithm and quantum simulation.
However, so far results have cast doubt on its practicability for near-term,
non-fault tolerant, quantum devices. Here we report experimental results
demonstrating that this intuition need not be true. We implement a recently
proposed adaptive Bayesian approach to quantum phase estimation and use it to
simulate molecular energies on a Silicon quantum photonic device. The approach
is verified to be well suited for pre-threshold quantum processors by
investigating its superior robustness to noise and decoherence compared to the
iterative phase estimation algorithm. This shows a promising route to unlock
the power of quantum phase estimation much sooner than previously believed
Classifying Subset Feedback Vertex Set for H-free graphs
In the FEEDBACK VERTEX SET problem, we aim to find a small set S of vertices in a graph intersecting every cycle. The SUBSET FEEDBACK VERTEX SET problem requires S to intersect only those cycles that include a vertex of some specified set T. We also consider the WEIGHTED SUBSET FEEDBACK VERTEX SET problem, where each vertex u has weight w(u)>0 and we ask that S has small weight. By combining known NP-hardness results with new polynomial-time results we prove full complexity dichotomies for SUBSET FEEDBACK VERTEX SET and WEIGHTED SUBSET FEEDBACK VERTEX SET for H-free graphs, that is, graphs that do not contain a graph H as an induced subgraph
Generation and sampling of quantum states of light in a silicon chip
Implementing large instances of quantum algorithms requires the processing of
many quantum information carriers in a hardware platform that supports the
integration of different components. While established semiconductor
fabrication processes can integrate many photonic components, the generation
and algorithmic processing of many photons has been a bottleneck in integrated
photonics. Here we report the on-chip generation and processing of quantum
states of light with up to eight photons in quantum sampling algorithms.
Switching between different optical pumping regimes, we implement the
Scattershot, Gaussian and standard boson sampling protocols in the same silicon
chip, which integrates linear and nonlinear photonic circuitry. We use these
results to benchmark a quantum algorithm for calculating molecular vibronic
spectra. Our techniques can be readily scaled for the on-chip implementation of
specialised quantum algorithms with tens of photons, pointing the way to
efficiency advantages over conventional computers
A General Theoretical Framework for Learning Smallest Interpretable Models
We develop a general algorithmic framework that allows us to obtain fixed-parameter tractability for computing smallest symbolic models that represent given data. Our framework applies to all ML model types that admit a certain extension property. By showing this extension property for decision trees, decision sets, decision lists, and binary decision diagrams, we obtain that minimizing these fundamental model types is fixed-parameter tractable. Our framework even applies to ensembles, which combine individual models by majority decision
Witnessing eigenstates for quantum simulation of Hamiltonian spectra
The efficient calculation of Hamiltonian spectra, a problem often intractable
on classical machines, can find application in many fields, from physics to
chemistry. Here, we introduce the concept of an "eigenstate witness" and
through it provide a new quantum approach which 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 towards quantum chemistry on quantum computers.Comment: 9 pages, 4 figures, plus Supplementary Material [New version with
minor typos corrected.
Multidimensional quantum entanglement with large-scale integrated optics
The ability to control multidimensional quantum systems is key for the
investigation of fundamental science and for the development of advanced
quantum technologies. Here we demonstrate a multidimensional integrated quantum
photonic platform able to robustly generate, control and analyze
high-dimensional entanglement. We realize a programmable bipartite entangled
system with dimension up to 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 key quantum applications
experimentally unexplored before, such as quantum randomness expansion and
self-testing on multidimensional states. Our work provides a prominent
experimental platform for the development of multidimensional quantum
technologies.Comment: Science, (2018
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