Qu&Co comments on this publication:

In this paper, Dunjko et al. provide a comprehensive review of the current (Sept 2017) state of quantum machine learning, including quantum providing speed-ups or enhancing classical ML and classical classical ML being used for quantum-control or to design quantum-circuits

Qu&Co comments on this publication:

Topological codes, and the surface code in particular, are popular choices for many quantum computing architectures, because of high error thresholds and local stabilizers. In this paper, Tuckett et al. show that a simple modification of the surface code can exhibit a fourfold  gain in the error correction threshold for a noise model in which Pauli Z errors (dephasing) occur more frequently than X or Y errors (which is common in many quantum architectures, including superconducting qubits). For pure dephasing an improved threshold of 43,7% is found (versus 10.9% for the optimal surface code), while 28,2% applies with a noise-bias-ratio of 10 (more realistic regime).

Qu&Co comments on this publication:

In recent years many academics and corporates have focus on solving combinatorial optimization problems on quantum-annealing devices like those offered by D-Wave. Now that noisy intermediate scale (NISQ) gate-based quantum-processers (like those of Google, IBM, Rigetti and Intel) are nearing the moment of quantum-supremacy, it is interesting to learn what gate-based quantum-computers can bring to combinatorial optimization problems. In this work, In this paper, Zahedinejad et al. provide a survey of the approaches to solving different types of combinatorial optimization problems, in particular quadratic unconstrained binary optimization (QUBO) problems on a gate model quantum computer. They focus on four different approaches including digitizing the adiabatic quantum computing, global quantum optimization algorithms, the quantum algorithms that approximate the ground state of a general QUBO problem, and quantum sampling. 

Qu&Co comments on this publication:

Quantum dot based spin qubits may offer significant advantages due to their potential for high densities, all-electrical operation, and integration onto an industrial platform. However, in quantum-dots, charge and nuclear spin noise are dominant sources of decoherence and gate errors. Silicon naturally has few nuclear spin isotopes, which can be removed through purification. As a host material, Silicon, enables single-qubit gate fidelities above 99%. In this paper, Watson et al. demonstrate a programmable two-qubit quantum processor in silicon by performing both the Deutsch-Josza and the Grover search algorithms.

Qu&Co comments on this publication:

Superpositions of bit strings (many-body spin configurations) have been recently proposed as a key to quantum machine learning applications. Adiabatic protocols may serve as an effective method to prepare such states. If the ground state of the final Hamiltonian in an adiabatic protocol is energetically degenerate, the final state of the protocol is a superposition of the configurations in the degenerate manifold. The challenge is to be able to control the dynamics of the protocol such that the amplitudes of the final state can be deterministically programmed. In this paper, Sieberer et al. present a framework to do precisely that. They apply an adiabatic protocol with controlled diabatic transitions to dynamically prepare programmable superpositions, where the control parameters can, even for large systems, be determined efficiently.

Qu&Co comments on this publication:

This report by Olson et al. summarizes the resuts of an NSF Workshop on Quantum Computational Chemistry held in November 2016. The workshop was attended by a wide range of experts from directly quantum-oriented fields such as algorithms, chemistry, machine learning, optics, simulation, and metrology, as well as experts in related fields such as condensed matter physics, biochemistry, physical chemistry, inorganic and organic chemistry, and spectroscopy. The goal of the workshop was to summarize recent progress in research at the interface of quantum information science and chemistry as well as to discuss the promising research challenges and opportunities in the field.

Qu&Co comments on this publication:

Shor's algorithm  for breaking both RSA and discrete-log public-key cryptography depend on the availability of a relatively large-scale quantum computer (e.g. Kutin et al. showed in 2006 that factoring a 1024-bit number requires 3132 qubits and 5.7x10^9 T gates). However, in quantum hardware developments are progressing while at the same time quantum algorithms are getting more efficient. So the timing when quantum computers will be able to break e.g. RSA is shifting. In this paper, Berstein et al. present a factoring algorithm that, assuming standard heuristics, uses a sublinear number of qubits. The time complexity of their new algorithm is asymptotically worse than Shor's algorithm, but the qubit requirements are asymptotically better, so it may be possible to physically implement the new algorithm sooner than Shor's algorithm.

Qu&Co comments on this publication:

At its core, the detailed understanding and prediction of complex chemical reaction mechanisms, requires highly accurate electronic structure methods. For molecules with many energetically close-lying orbitals, much less than a hundred strongly correlated electrons are already out of reach for classical calculation methods that could achieve the required accuracy. In this paper, Reiher et al. using as an example the open problem of biological nitrogen fixation in nitrogenase, to show how quantum computers can augment classical computer simulations used to probe these reaction mechanisms, to significantly increase their accuracy and enable hitherto intractable simulations. They demonstrate that quantum computers will be able to tackle important problems in chemistry without requiring exorbitant resources (in this case as little as 111 qubits and 1.0x10^14 T gates)

Qu&Co comments on this publication:

In this article, Matthias Möller and Cornelis Vuik of the Institute of Applied Mathematics at Delft University of Technology describe their vision of future developments in scientific computing that would be enabled by the advent of software-programmable quantum computers. In their analysis they assume that quantum computers will form part of a hybrid accelerated computing platform like GPUs and co-processor cards do today. In particular, they address the potential of quantum algorithms to bring major breakthroughs in applied mathematics and its applications. Finally, the authors give several examples that demonstrate the possible impact of quantum-accelerated scientific computing on society.

Qu&Co comments on this publication:

Boson sampling is a rudimentary quantum algorithm tailored to the platform of photons in linear optics. Prior to this paper by Neville et al, it was believed that Boson-sampling was a good candidate to be the first to experimentally show quantum supremacy. However, Neville et al. show that this would require a technological step change, reaching photon numbers of over 50 and ultra-low loss interferometers with thousands of modes. It is therefore highly unlikely that Boson-sampling experiments will win the 'quantum supremacy race' currently believed to be led by semiconductor-qubit platforms.

Qu&Co comments on this publication:

One of the most popular techniques for error-correction is the surface code with logical 2-qubit operations realized via so-called lattice surgery. This popularity is explained a.o. by its high estimated error-correction threshold of 1% and relatively simple correction procedure. In this paper, De Beaudrap et al. demonstrate that lattice surgery is a model for the ZX calculus, an abstract graphical language for tensor networks. ZX calculus therefore provides a ready-made practical 'language' for discussing computations realized using surface codes via lattice surgery.

Qu&Co comments on this publication:

In this paper, Ambainis et al. study quantum algorithms on search trees of unknown structure, in a model where the tree can be discovered by local exploration. They construct a quantum algorithm which, given a search tree of depth at most n, estimates the size of the tree T with an upper-bound of sqrt(nT) steps. They apply their results to improve the time-complexity of a classical backtracking algorithm and to develop a quantum algorithm for evaluating AND-OR formulas in 2-player game type models.

Page 6 of 8

What's Interesting?

How can we help you?

Invalid Input

Invalid Input

Invalid Input

Invalid Input

Invalid Input

Invalid Input

Copyright © Qu & Co BV
close