Talks

Thomas Ayral & Simon Martiel

Investigating quantum software opportunities and challenges with high performance numerical simulation

Should a quantum computer be available by tomorrow, very few end users would be able to take advantage of it. Quantum programming requires indeed a strong shift as compared to classical software, as it carries some of the weirdnesses of quantum physics. Another reason is that software developers have yet not been able to play (and torture) a quantum computing device.We will introduce the Atos Quantum Learning Machine, a high performance compact supercomputer which emulates a quantum device of 40 fully entangled qubits, with a portable quantum programming framework. We will then show some of the open problems quantum software is facing in the field of quantum resource optimization and fault tolerance, and show how high performance computing can bring elements of answers and potentially provide valuable inputs to the design of qubit technologies.  

 
Stefan Filipp

Quantum computing with superconducting qubits using short-depth algorithms

Quantum computers hold the promise for outperforming conventional computers in certain types of problems such as the computation of energy spectra or the dynamics of molecular or condensed matter systems. An architecture based on fixed-frequency superconducting qubits is a particularly promising candidate to build a scalable quantum computing architecture because of its stability and the relatively long coherence times. In this talk I will present recent results on the computation of ground state energies of simple molecules using a variational algorithm on a six-qubit superconducting quantum processor. Short-depth algorithms are employed, which finish within the coherence time of the system.
To further improve the accuracy of these results, we explore different methods to control qubit-qubit interactions based on parametrically driven flux-tunable couplers or two-tone microwave drives. These methods may enhance the efficiency of variational methods for quantum chemistry or optimization tasks with the prospect to carry out scientifically and commercially relevant computations soon.

Aram Harrow

Small quantum computers and big classical data

Can a quantum computer help us analyze a large classical data set?  Data stored classically cannot be queried in superposition, which rules out direct Grover searches, and it can often be classically accessed with some level of parallelism, which would negate the advantage of Grover even if it were possible. In this talk I will explore how to use quantum computers for data analysis tasks, such as maximum likelihood estimation, in the setting where the data set is too large to fit on the quantum computer, and at the same time, large classical computers are available.

Pérola Milman

Some aspects of quantum error correction for continuous variables

I'll review the problem of  quantum error correction for continuous variables (as position, momentum, the electromagnetic field's quadratures, the quantum phase...)  and its applications using the stabilizer formalism. Starting from the discrete case, we will extend it to the continuous one, as proposed by Gottesman, Kitaev and Preskill. We will show how this formalism is associated to the modular variables one, introduced by Aharanov in the 60's. We then  briefly describe an application of CV QEC involving the proof of quantum supremacy,  in the continuous variables realm, of a sub-universal model of quantum computing, instantaneous quantum computing.

Marc Sanquer

Spin qubits in silicon

The spin of electrons in silicon nanostructures is the base of several recent demonstrations of  decent qubits. I will compare our CMOS qubit [1]- the first qubit realized in a CMOS pre-industrial foundry and the first spin qubit using holes- with other silicon qubit realizations. Holes are interesting because their spin can be controlled by electric field thanks to their intrinsic  spin-orbit coupling [2]. I will also present the observation of Electric dipole  spin resonance (EDSR)  for electrons in the same silicon nanowires [3].

[1] R. Maurand et al. Nature Communications 7, Article number: 13575 (2016) doi:10.1038/ncomms13575

[2] A. Crippa et al., Electrical spin driving by g-matrix modulation in spin-orbit qubits, arXiv1710.08690v1

[3] A. Corna  et al.  Electrically driven electron spin resonance mediated by spin-orbit coupling in a silicon quantum dot arXiv:1708.02903

Philipp Schindler

Small scale quantum error correction in a trapped ion quantum computer

Within this talk, I will review the techniques for quantum information processing with trapped ions. I will present our recent experiments on topological quantum error correction. Based on this result I will present our upcoming system that has the potential to demonstrate beneficial quantum error correction.

Tim Taminiau

Diamond quantum networks for distributed quantum computation

Quantum networks provide a promising way to realize large-scale quantum computations and simulations. Such networks consist of nodes that contain multiple qubits to store and process quantum states, and that are connected together by distributing entangled states through optical links using photons. Crucially, imperfections and errors can be overcome by distributing logical qubits, computations and error correction over the network [1]. This approach is scalable to large sizes by connecting many independent modules, thus avoiding the challenges of a single large structure of ever increasing complexity.

The nitrogen vacancy (NV) center in diamond is a promising candidate to realize such quantum networks, as it combines optical entanglement links [2] with long-lived multiqubit nodes that can store and process quantum information [3-5]. In this talk I willdiscuss the recent progress of my group towards quantum networks for distributed quantum computations.

[1] N. H. Nickerson, Y. Li, S. C. Benjamin, Nature Commun. 4, 1756 (2013)

[2] B. Hensen et al., Nature 526, 682 (2015)

[2] J. Cramer et al., Nature Commun. 7:11526 (2016)

[3] N. Kalb et al., Nature Commun. 7:13111 (2016)

[4] A. Reiserer et al., Phys. Rev. X 6, 021040 (2016)

Jean-Pierre Tillich

A survey on some recent advances on decoding quantum LDPC codes

Quantum low-density parity-check (LDPC) codes are stabilizer codes in which each stabilizer generator acts on O(1) qubits and each qubit participates in O(1) generators. Some famous classes of codes such as the toric code are quantum LDPC codes. There are several reasons that explain why this subclass of stabilizer codes is particularly interesting. For instance, it was shown by Gottesman a few years ago that such codes would in principle allow fault-tolerant quantum computing with constant overhead. However, for this application we need quantum LDPC codes that are able to encode a linear fraction of qubits (they are called constant rate codes) with good minimum distance properties and that are equipped with an efficient decoding algorithm. The fact that their classical counterpart enjoys all these nice properties could lead to think that it will be easy to obtain the same result in the quantum setting. However it has turned out that decoding quantum LDPC codes is a much more involved issue than decoding classical LDPC codes. The latter class of codes is decoded successfully by low complexity iterative decoding algorithms. In principle, such algorithms could also be used to decode quantum LDPC codes. However, this approach fails for quantum LDPC codes due to the fact that such codes are inherently highly degenerate. I will survey and discuss in this talk some recent advances that have been obtained on this issue such as constant rate LDPC codes equipped with a decoding algorithm that allows to correct all possible errors of weight which is a square root of the total length of the code or that allow to correct with probability 1-o(1) a constant fraction of errors.

Xavier Waintal

A physicist introduction to Quantum Error Correction

Quantum computers hold the promise of an exponential speed up of certain tasks with respect to classical computers. In this talk, I will review how such speed up is achieved and what it implies on the specifications that are required for the manipulation of the quantum states. I will stress that a quantum computer is inherently an analogue machine --- its internal state is described by continuous variables --- and that its computing power is not only set by its size but chiefly by its precision. Precision deteriorates with the number of operations; it is the chief resource of a quantum computer that sets the complexity of possible calculations. In the second part, I will discuss quantum error correction schemes that propose, to some extend, to trade precision with size: using several qubits, one can build a better, more precise, logical qubit. In theory, quantum error correction solves the problem of the extreme precision that is required by even the simplest realistic applications of quantum computing. In practice, quantum error correction implies massive overheads that make its applicability doubtful.