Promising routes to large-scale photonic quantum computing

Scilight ◽  
2019 ◽  
Vol 2019 (24) ◽  
pp. 240007
Author(s):  
Meeri Kim
Keyword(s):  
Quantum Computing ◽  
Large Scale

scholarly journals Quantum learning with noise and decoherence: a robust quantum neural network

2019 ◽  
Author(s):  
Elizabeth Behrman ◽  
Nam Nguyen ◽  
James Steck
Keyword(s):  
Neural Network ◽  
Quantum Computing ◽  
Large Scale ◽  
Learning Approaches ◽  
Quantum Neural Network ◽  
Quantum Learning ◽  
Learning With Noise ◽  
Robustness To Noise ◽  
Robust To Noise ◽  
Arbitrary Quantum

<p>Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing. </p> <p> </p>

scholarly journals Generation of time-domain-multiplexed two-dimensional cluster state

Science ◽  
2019 ◽  
Vol 366 (6463) ◽  
pp. 373-376 ◽  
Author(s):  
Warit Asavanant ◽  
Yu Shiozawa ◽  
Shota Yokoyama ◽  
Baramee Charoensombutamon ◽  
Hiroki Emura ◽  
...  
Keyword(s):  
Quantum Computing ◽  
Large Scale ◽  
Cluster State ◽  
Continuous Variable ◽  
Error Correcting Codes ◽  
Square Lattice ◽  
Two Dimensional ◽  
Report Generation ◽  
Cluster States ◽  
Variable Cluster

Entanglement is the key resource for measurement-based quantum computing. It is stored in quantum states known as cluster states, which are prepared offline and enable quantum computing by means of purely local measurements. Universal quantum computing requires cluster states that are both large and possess (at least) a two-dimensional topology. Continuous-variable cluster states—based on bosonic modes rather than qubits—have previously been generated on a scale exceeding one million modes, but only in one dimension. Here, we report generation of a large-scale two-dimensional continuous-variable cluster state. Its structure consists of a 5- by 1240-site square lattice that was tailored to our highly scalable time-multiplexed experimental platform. It is compatible with Bosonic error-correcting codes that, with higher squeezing, enable fault-tolerant quantum computation.

Sign in / Sign up

Export Citation Format

Share Document