John Preskill, CalTech
The quantum laws governing atoms and other tiny objects seem to defy common sense, and information encoded in quantum systems has weird properties that baffle our feeble human minds. John Preskill will explain why he loves quantum entanglement, the elusive feature making quantum information fundamentally different from information in the macroscopic world. By exploiting quantum entanglement, quantum computers should be able to solve otherwise intractable problems, with far-reaching applications to cryptology, materials, and fundamental physical science. Preskill is less weird than a quantum computer, and easier to understand.
Nikesh S. Dattani, Nathaniel Bryans
The largest number factored on a quantum device reported until now was 143.
That quantum computation, which used only 4 qubits, actually also factored much larger numbers such as 3599, 13081, and 44929, without the awareness of the authors of that work.
Furthermore, unlike the implementations of Shor’s algorithm performed thus far, these 4-qubit factorizations do not need to use prior knowledge of the answer. However, because they only use 4 qubits, these factorizations can also be performed trivially on classical computers. We discover a class of numbers for which the power of quantum information actually comes into play.
We then demonstrate a 3-qubit factorization of 175, which would be the first quantum factorization of a triprime.
Read more at http://arxiv.org/pdf/1411.6758v2.pdf
Li Zhaokai, Liu Xiaomei, Xu Nanyang, Du jiangfeng
Machines are possible to have some artificial intelligence like human beings owing to particular algorithms or software.
Such machines could learn knowledge from what people taught them and do works according to the knowledge.
In practical learning cases, the data is often extremely complicated and large, thus classical learning machines often need huge computational resources. Quantum machine learning algorithm, on the other hand, could be exponentially faster than classical machines using quantum parallelism.
Here, we demonstrate a quantum machine learning algorithm on a four-qubit NMR test bench to solve an optical character recognition problem, also known as the handwriting recognition.
The quantum machine learns standard character fonts and then recognize handwritten characters from a set with two candidates.
To our best knowledge, this is the first artificial intelligence realized on a quantum processor.
Due to the widespreading importance of artificial intelligence and its tremendous consuming of computational resources, quantum speedup would be extremely attractive against the challenges from the Big Data…..
Read more at http://arxiv.org/pdf/1410.1054v1.pdf
A fragile quantum memory state has been held stable at room temperature for a “world record” 39 minutes – overcoming a key barrier to ultrafast computers.
“Qubits” of information encoded in a silicon system persisted for almost 100 times longer than ever before.
Quantum systems are notoriously fickle to measure and manipulate, but if harnessed could transform computing.
The new benchmark was set by an international team led by Mike Thewalt of Simon Fraser University, Canada.
“This opens the possibility of truly long-term storage of quantum information at room temperature,” said Prof Thewalt, whose achievement is detailed in the journal Science.
In conventional computers, “bits” of data are stored as a string of 1s and 0s.
But in a quantum system, “qubits” are stored in a so-called “superposition state” in which they can be both 1s and 0 at the same time – enabling them to perform multiple calculations simultaneously.
The trouble with qubits is their instability – typical devices “forget” their memories in less than a second.
There is no Guinness Book of quantum records. But unofficially, the previous best for a solid state system was 25 seconds at room temperature, or three minutes under cryogenic conditions.
In this new experiment, scientists encoded information into the nuclei of phosphorus atoms held in a sliver of purified silicon.
Magnetic field pulses were used to tilt the spin of the nuclei and create superposition states – the qubits of memory.
The team prepared the sample at -269C, close to absolute zero – the lowest temperature possible…..
……….. Read more at http://www.bbc.co.uk/news/science-environment-24934786
Entanglement is a key resource for upcoming quantum computers and simulators. Now, physicists in Innsbruck and Geneva realized a new, reliable method to verify entanglement in the laboratory using a minimal number of assumptions about the system and measuring devices. Hence, this method witnesses the presence of useful entanglement.
Quantum computation, quantum communication and quantum cryptography often require entanglement. For many of these upcoming quantum technologies, entanglement – this hard to grasp, counter-intuitive aspect in the quantum world – is a key ingredient. Therefore, experimental physicists often need to verify entanglement in their systems. “Two years ago, we managed to verify entanglement between up to 14 ions”, explains Thomas Monz. He works in the group of Rainer Blatt at the Institute for Experimental Physics, University Innsbruck. This team is still holding the world-record for the largest number of entangled particles. “In order to verify the entanglement, we had to make some, experimentally calibrated, assumptions. However, assumptions, for instance about the number of dimensions of the system or a decent calibration, make any subsequently derived statements vulnerable”, explains Monz. Together with Julio Barreiro, who recently moved on the Max Planck Institute of Quantum Optics in Garching, and Jean-Daniel Bancal from the group of Nicolas Gisin at the University of Geneva, now at the Center for Quantum Technologies in Singapore, the physicists derived and implemented a new method to verify entanglement between several objects.
The presented device-independent method is based on a single assumption: “We only have to make sure that we always apply the same set of operations on the quantum objects, and that the operations are independent of each other”, explains Julio Barreiro. “However, which operations we apply in detail – this is something we do not need to know.” This approach – called Device Independent – allows them to get around several potential sources of error, and subsequently wrong interpretations of the results. “In the end, we investigate the correlations between the settings and the obtained results. Once the correlations exceed a certain threshold, we know that the objects are entangled.” For the experimentally hardly avoidable crosstalk of operations applied to levitating calcium ions in the vacuum chamber in Innsbruck, the Swiss theorist Jean-Daniel Bancal managed to adapt the threshold according to a worst-case scenario. “When this higher threshold is breached, we can claim entanglement in the system with high confidence”, states Bancal.
Assumptions as Achilles heel
For physicists, such procedures that are based on very few assumptions are highly interesting. By being basically independent of the system, they provide high confidence and strengthen the results of experimentalists. “Assumptions are always the Achilles heel – be that for lab data or theory work”, stresses Thomas Monz. “We managed to reduce the number of assumption to verify entanglement to a minimum. Our method thus allows for reliable statements about the entanglement in a system.” In the actual implementation, the physicists in Innsbruck could verify entanglement of up to 6 ions. This new method can also be applied for larger systems. The technical demands, however, also increase accordingly.
Read more at: http://phys.org/news/2013-08-infallible-quantum.html#jCp
Experimental Quantum Computing to Solve Systems of Linear Equations
X.-D. Cai et al
Solving linear systems of equations is ubiquitous in all areas of science and engineering. With rapidly growing data sets, such a task can be intractable for classical computers, as the best known classical algorithms require a time proportional to the number of variables N. A recently proposed quantum algorithm shows that quantum computers could solve linear systems in a time scale of order log(N), giving an exponential speedup over classical computers. Here we realize the simplest instance of this algorithm, solving 2×2 linear equations for various input vectors on a quantum computer. We use four quantum bits and four controlled logic gates to implement every subroutine required, demonstrating the working principle of this algorithm.
Read more: http://prl.aps.org/abstract/PRL/v110/i23/e230501
Read also: http://physicsworld.com/cws/article/news/2013/jun/12/quantum-computer-solves-simple-linear-equations
Stefanie Barz, Ivan Kassal, Martin Ringbauer, Yannick Ole Lipp, Borivoje Dakic, Alán Aspuru-Guzik, Philip Walther
Systems of linear equations are used to model a wide array of problems in all fields of science and engineering. Recently, it has been shown that quantum computers could solve linear systems exponentially faster than classical computers, making for one of the most promising applications of quantum computation. Here, we demonstrate this quantum algorithm by implementing various instances on a photonic quantum computing architecture. Our implementation involves the application of two consecutive entangling gates on the same pair of polarisation-encoded qubits. We realize two separate controlled-NOT gates where the successful operation of the first gate is heralded by a measurement of two ancillary photons. Our work thus demonstrates the implementation of a quantum algorithm with high practical significance as well as an important technological advance which brings us closer to a comprehensive control of photonic quantum information.
Read more: http://arxiv.org/pdf/1302.1210v1.pdf