
Are defense contractors investing enough in quantum computing?
Fifth Domain ● January 19, 2019
Quantum computing is expected to make existing forms of cybersecurity obsolete, but the coming revolution has not jolted researchers and defense firms to fully invest in the technology, according to the intelligence community, experts and industry officials.
Quantum computing needs strong collaboration between theory and practice, said Christopher Monroe, professor of physics at the University of Maryland and the head of IonQ, a quantum computer manufacturer.
Exploring Quantum Neural NetworksGoogle AI blog ● January 19, 2019
Since its inception, the Google AI Quantum team has pushed to understand the role of quantum computing in machine learning. The existence of algorithms with provable advantages for global optimization suggest that quantum computers may be useful for training existing models within machine learning more quickly, and we are building experimental quantum computers to investigate how intricate quantum systems can carry out these computations. While this may prove invaluable, it does not yet touch on the tantalizing idea that quantum computers might be able to provide a way to learn more about complex patterns in physical systems that conventional computers cannot in any reasonable amount of time.
A fast quantum interface between different spin qubit encodingsNature Communications ● January 19, 2019
Singlespin qubits in semiconductor quantum dots hold promise for universal quantum computation with demonstrations of a high singlequbit gate fidelity above 99.9% and twoqubit gates in conjunction with a long coherence time. However, initialization and readout of a qubit is orders of magnitude slower than control, which is detrimental for implementing measurementbased protocols such as errorcorrecting codes. In contrast, a singlettriplet qubit, encoded in a twospin subspace, has the virtue of fast readout with high fidelity. Here, we present a hybrid system which benefits from the different advantages of these two distinct spinqubit implementations. A quantum interface between the two codes is realized by electrically tunable interqubit exchange coupling. We demonstrate a controlledphase gate that acts within 5.5 ns, much faster than the measured dephasing time of 211 ns. The presented hybrid architecture will be useful to settle remaining key problems with building scalable spinbased quantum computers.
Experimental demonstration of quantum advantage for oneway communication complexityNiraj Kumar, Iordanis Kerenidis, Eleni Diamanti (via Arxiv) ● January 19, 2019
The goal of demonstrating a quantum advantage with currently available experimental systems is of utmost importance in quantum information science. While this remains elusive for quantum computation, the field of communication complexity offers the possibility to already explore and showcase this advantage for useful tasks. Here, we define such a task, the Sampling Matching problem, which is inspired by the Hidden Matching problem and features an exponential gap between quantum and classical protocols in the oneway communication model. Our problem allows by its conception a photonic implementation based on encoding in the phase of coherent states of light, the use of a fixed size linear optic circuit, and singlephoton detection. This enables us to demonstrate experimentally an advantage in the transmitted information resource beyond a threshold input size, which would have been impossible to reach for the original Hidden Matching problem. Our demonstration has implications in various communication and cryptographic settings, for example for quantum retrieval games and quantum money.
Solving Quantum Chemistry Problems with a DWave Quantum AnnealerMichael Streif, Florian Neukart, Martin Leib (via Arxiv) ● January 19, 2019
Quantum annealing devices have been subject to various analyses in order to classify their usefulness for practical applications. While it has been successfully proven that such systems can in general be used for solving combinatorial optimization problems, they have not been used to solve chemistry applications. In this paper we apply a mapping, put forward by Xia et al. (The Journal of Physical Chemistry B 122.13 (2017): 33843395.), from a quantum chemistry Hamiltonian to an Ising spin glass formulation and find the ground state energy with a quantum annealer. Additionally we investigate the scaling in terms of needed physical qubits on a quantum annealer with limited connectivity. To the best of our knowledge, this is the first experimental study of quantum chemistry problems on quantum annealing devices.
Quantum advantage with shallow circuitsSergey Bravyi, David Gosset1, Robert König (via Science magazine) ● January 19, 2019
Quantum computers are expected to be better at solving certain computational problems than classical computers. This expectation is based on (wellfounded) conjectures in computational complexity theory, but rigorous comparisons between the capabilities of quantum and classical algorithms are difficult to perform. Bravyi et al. proved theoretically that whereas the number of “steps” needed by parallel quantum circuits to solve certain linear algebra problems was independent of the problem size, this number grew logarithmically with size for analogous classical circuits (see the Perspective by Montanaro). This socalled quantum advantage stems from the quantum correlations present in quantum circuits that cannot be reproduced in analogous classical circuits.
Multiparameter optimisation of a magnetooptical trap using deep learningQuantum advantage with shallow circuitsA. D. Tranter, H. J. Slatyer, M. R. Hush, A. C. Leung, J. L. Everett, K. V. Paul, P. VernazGris, P. K. Lam, B. C. Buchler & G. T. Campbell (via Nature communications) ● January 19, 2019
Machine learning based on artificial neural networks has emerged as an efficient means to develop empirical models of complex systems. Cold atomic ensembles have become commonplace in laboratories around the world, however, manybody interactions give rise to complex dynamics that preclude precise analytic optimisation of the cooling and trapping process. Here, we implement a deep artificial neural network to optimise the magnetooptic cooling and trapping of neutral atomic ensembles. The solution identified by machine learning is radically different to the smoothly varying adiabatic solutions currently used. Despite this, the solutions outperform best known solutions producing higher optical densities.
Qubit Allocation for Noisy IntermediateScale Quantum ComputersWill Finigan, Michael Cubeddu, Thomas Lively, Johannes Flick, Prineha Narang (via Arxiv) ● January 19, 2019
In the era of noisyintermediatescale quantum computers, we expect to see quantum devices with increasing numbers of qubits emerge in the foreseeable future. To practically run quantum programs, logical qubits have to be mapped to the physical qubits by a qubit allocation algorithm. However, on present day devices, qubits differ by their error rate and connectivity. Here, we establish and demonstrate on current experimental devices a new allocation algorithm that combines the simulated annealing method with local search of the solution space using Dijkstra's algorithm. Our algorithm takes into account the weighted connectivity constraints of both the quantum hardware and the quantum program being compiled. New quantum programs will enable unprecedented developments in physics, chemistry, and materials science and our work offers an important new pathway toward optimizing compilers for quantum programs.
Quantum Computing Market & Technologies 20182024ResearchAndMarkets.com (via Business Wire) ● January 19, 2019
The global market will grow at a CAGR of 24.6% throughout 20182024. During 2017 Quantum Computing technologies performance has increased at an impressive rate; we forecast that 20182019 will experience a surge of breakthroughs. Realizing quantum computing capability demands that hardware efforts would be augmented by the development of quantum software to obtain optimized quantum algorithms able to solve application problems of interest. Due to economic interest and the decline of Moore’s law of computational scaling, eighteen of the world’s biggest corporations (see image below) and dozens of government agencies are working on quantum processor technologies and/or quantum software or partnering with the quantum industry startups like DWave. Their ambition reflects a broader transition, taking place at startups and academic research labs alike: to move from pure science towards engineering.
The Commercial Prospects for Quantum ComputingNQIT ● January 19, 2019
The NQIT User Engagement Team is pleased to present a new market report, “The Commercial Prospects for Quantum Computing”, December 2016, which reviews current commercial activity in quantum computing around the world.
In this report, we cover commercial investment in quantum computing, the current market and research status and public perceptions of this new technology.
We go into more depth on potential market segments and provide a detailed timeline of commercial investment in quantum computing.
The report is based entirely on publicly available data and full references are provided in the Appendix.