Fifth Domain, 4 Jan 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.
Google AI blog, 17 Dec 2018
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.
Nature Communications, 29 Nov 2018
Single-spin qubits in semiconductor quantum dots hold promise for universal quantum computation with demonstrations of a high single-qubit gate fidelity above 99.9% and two-qubit 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 measurement-based protocols such as error-correcting codes. In contrast, a singlet-triplet qubit, encoded in a two-spin 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 spin-qubit implementations. A quantum interface between the two codes is realized by electrically tunable inter-qubit exchange coupling. We demonstrate a controlled-phase 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 spin-based quantum computers.
Arxiv.org, 22 Nov 2018
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 one-way 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 single-photon 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.
Arxiv.org, 13 Nov 2018
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): 3384-3395.), 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.
Science magazine, 19 Oct 2018
Quantum computers are expected to be better at solving certain computational problems than classical computers. This expectation is based on (well-founded) 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 so-called quantum advantage stems from the quantum correlations present in quantum circuits that cannot be reproduced in analogous classical circuits.
Multiparameter optimisation of a magneto-optical trap using deep learningQuantum advantage with shallow circuits
Nature Communications, 19 Oct 2018
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, many-body 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 magneto-optic 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.
Arxiv.org, 18 Oct 2018
In the era of noisy-intermediate-scale 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.
Business Wire, 17 July 2018
The global market will grow at a CAGR of 24.6% throughout 2018-2024. During 2017 Quantum Computing technologies performance has increased at an impressive rate; we forecast that 2018-2019 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 D-Wave. Their ambition reflects a broader transition, taking place at start-ups and academic research labs alike: to move from pure science towards engineering.
NQIT, December 2016
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.