30,948 research outputs found
Hamiltonian Simulation by Qubitization
We present the problem of approximating the time-evolution operator
to error , where the Hamiltonian is the
projection of a unitary oracle onto the state created by
another unitary oracle. Our algorithm solves this with a query complexity
to both oracles that is optimal
with respect to all parameters in both the asymptotic and non-asymptotic
regime, and also with low overhead, using at most two additional ancilla
qubits. This approach to Hamiltonian simulation subsumes important prior art
considering Hamiltonians which are -sparse or a linear combination of
unitaries, leading to significant improvements in space and gate complexity,
such as a quadratic speed-up for precision simulations. It also motivates
useful new instances, such as where is a density matrix. A key
technical result is `qubitization', which uses the controlled version of these
oracles to embed any in an invariant subspace. A large
class of operator functions of can then be computed with optimal
query complexity, of which is a special case.Comment: 23 pages, 1 figure; v2: updated notation; v3: accepted versio
Relativistic Spheres
By analyzing the Einstein's equations for the static sphere, we find that
there exists a non-singular static configuration whose radius can approach its
corresponding horizon size arbitrarily.Comment: 8 pages revtex, 1 ps figur
Use of Machine Learning for Partial Discharge Discrimination
Partial discharge (PD) measurements are an important tool for assessing the condition of power equipment. Different sources of PD have different effects on the insulation performance of power apparatus. Therefore, discrimination between PD sources is of great interest to both system utilities and equipment manufacturers. This paper investigates the use of a wide bandwidth PD on-line measurement system to facilitate automatic PD source identification. Three artificial PD models were used to simulate typical PD sources which may exist within power systems. Wavelet analysis was applied to pre-process the obtained measurement data. This data was then processed using correlation analysis to cluster the discharges into different groups. A machine learning technique, namely the support vector machine (SVM) was then used to identify between the different PD sources. The SVM is trained to differentiate between the inherent features of each discharge source signal. Laboratory experiments indicate that this approach is applicable for use with field measurement data
Power load forecasting
For the electric power factory, the power load forecasting problem, including load forecasting and consumption predicting, is crucial to work planning. According to the predicting time, it can be divided into long-term forecasting, mid-term forecasting, short-term forecasting and ultra-short-term forecasting. The long-term and mid-term forecasting are mainly used for macro control, and their forecasting time arrange are from one year to ten years and from one month to twelve months respectively. The short-term forecasting which prediction time is from one day to seven days is used in generators macroeconomic control, power exchange plan and some other areas. Predicting the situation in next 24 hours is named as the ultra-short-term forecasting which is used for failure prediction, emergency treatment and frequency control. In general, the forecast accuracy is different for different prediction time. The longer is the time, the lower accurate is the prediction.
As the unique power supplier in Huizhou (China), Huizhou Electric Power wants to know the solution to the problems: 1. Prediction of the total electrical consumption and the peak load of the city in 2006 based on the economy development and the feature of the city. 2. Monthly prediction of the consumption and peak load in 2006. 3. Daily prediction of the consumption and peak load from July 10th to 16th in 2006. 4. Prediction of the load every 15 minutes of July 10th. 5. Real-time forecasting which means to amend the existing load prediction for next 15 minute
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