In this paper, the authors propose a method for transforming an unknown three-qubit state into its canonical form up to phases. This transformation can be achieved variationally and may be used to estimate the tangle. Simulations on this method have been performed.

The tangle is a three-qubit entanglement invariant that quantifies genuine tripartite entanglement. The state with maximum tangle is the GHZ state (and its local transformations). The distribution of tangle for random three-qubit states is depicted below.

The canonical form up to phases can be achieved when the number of measurements of three possible elements in the computational basis are zero. In this form, the tangle is easily measured. The canonical form can be cast by applying local unitaries on each qubit.

The tangle is affected by errors in the circuit. To mitigate these errors, a post-selection scheme can be applied after measuring the output state. This post-selection consists of discarding those results that should not appear in the canonical form.

First, the authors have analyzed the GHZ state. The estimation of the tangle with this method provides lower values as the errors (“t”) in the circuit increase. Post-selection mitigates the errors considerably.

This behavior has found to be a common tendency for many different random states. In the figure depicted below, each dot corresponds to a random three-qubit state.

With errors comparable to those in the state-of-the-art quantum hardware, the tangle presents a mean underestimation of ~30%. Post-selection lowers this result to ~17%.

Although the method herein presented does not provide any speed up respect to quantum tomography, it can be used as a module for other algorithms, such as a classifier.

*Dear colleagues,*

* Applications are invited for one or two postdoctoral positions in the field of Quantum Computation.*

* The successful candidate will work with Dr. Sofyan Iblisdir and Dr. Luca Tagliacozzo, at the*

* University of Barcelona (Spain) in quantum computation and related topics, from developing and*

* characterizing new quantum algorithms, to their classical simulation, to the characterization of noise*

* and imperfections in specific experimental implementations, to quantum machine learning. Beside*

* quantum computation, the group has strong expertise in the theory of many-body quantum systems*

* at and out of equilibrium, and in tensor networks techniques.*

* The positions are for a period of 1 year that will be possibly extended to 2 years depending*

* on performances and availability of funding. The positions should be filled as soon as possible.*

* Applicants are expected to have a doctoral degree in Physics, Computer Science, Mathematics, or a*

* related discipline before the starting date of the position, and have previous expertise in one (or*

* more) of the following areas: quantum computation, quantum information, tensor networks,*

* machine learning, quantum many-body systems, condensed matter physics, quantum field theory.*

* Applicants should have a strong interest in solving challenging problems, as well as a proven record*

* of research, including publication of original work in at least one of the above areas. Excellent*

* scientific writing ability and good communication skills are essential.*

* Applications should be sent to Dr. Sofyan Iblisdir and should include: 1) a motivation letter;*

* 2) a curriculum vitae including a list of publications; 3) a research statement; 4) the name and email*

* of two references. All qualified applicants will receive equal consideration without regard to*

* appearance, beliefs, sex, sexual orientation, gender identity, national origin, disability or age. For*

* full consideration, applications should be submitted by the 10th of March 2020.*

Please direct informal enquiries to:

Dr Sofyan Iblisdir: sofyan.iblisdir(at)fqa.ub.edu

Dr Luca Tagliacozzo: luca.tagliacozzo(at)fqa.ub.edu

In this paper, the authors have analyzed the performances of a finite depth quantum circuit in order to encode the ground state of local Hamiltonians. They have shown that as expected, the precision improves exponentially with the depth of the circuit in gapped phases.

In conformally invariant gapless phases, the precision improves very slowly up to a number of layers that increase linearly with the system size. They identify that regime with a finite-depth regime, where the depth of the circuit dictates the appearance of an effective correlation length. Beyond that number of layers, the precision improves again exponentially (finite-size regime).

They have provided an explanation of this phenomenon in terms of Lieb-Robinson bounds, and also compared the power of different variational ansatz in representing the ground state of critical systems. Finally, they believe that, in the context of critical systems, the actual entanglement entropy of the wave-function provides a proper measurement of the effective number of free parameters in the ansatz, the number that is ultimately responsible for the accuracy of the results.

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Here is a public summary of the achievement:

*For over 30 years, spontaneous parametric down-conversion (SPDC) has been a workhorse for quantum optics. By splitting one “pump photon” into two daughter photons, SPDC has had a crucial role in fundamental tests of quantum theory as well as many applications in quantum information processing. From the early days, researchers have explored splitting the pump photon into three photons (as a possible resource in quantum computation, for example), but it has proven extremely difficult to realize experimentally—until now. Here, we report on an implementation of three-photon SPDC in the microwave domain.*

*To split one microwave photon into three daughter photons, we use a flux-pumped, superconducting parametric resonator. Our triplet source is bright, producing a propagating photon flux comparable to ordinary two-photon SPDC. We clearly see strong three-photon correlations in the output photons, even in the absence of normal two-photon correlations. The symmetry properties of these correlations allow us to “fingerprint” how the photons were created, clearly demonstrating little contamination from typical SPDC processes.*

*These results form the basis of an exciting new paradigm of three-photon quantum optics. One can only hope that this new paradigm will be as successful as two-photon quantum optics.*

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This manuscript introduces a novel algorithm for European option pricing that uses the unary representation of the asset price. That is, each qubit maps into a single price of the underlying asset.

The amplitude distributor that uploads the expected price evolution to the qubit register and the circuit that encodes the estimated payoff into an ancilla are remarkably simple in the unary representation.

The amplitude distributor only requires first-neighbor interactions between qubits, therefore a simplified chip architecture is enough to run the algorithm.

This scheme allows for a post-processing procedure that results in error mitigation for Noisy Intermediate-Scale Quantum devices. This algorithm is more robust to noise than their usual binary

counterparts.

Option pricing in this unary representation can therefore be beneficial for near-term quantum computers.

]]>The article presents a classical optimization strategy for the Quantum Approximation Optimization Algorithm (QAOA) using Reinforcement Learning (RL). The algorithm is tested for several instances of the MAXCUT problem.

In general, RL approaches consist of discrete-time agent-environment interactions. The agent is provided with partial/total observation of the environment and maximizes the reward by acting into it.

The QAOA is implemented such that, at each step of an episode of arbitrary but fixed length *p*, a pair of parameter-dependent unitary transformations are applied to a quantum state.

The values of the parameters are selected by the Deep RL agent using as inputs to the Neural Network a set of measurements of the quantum state, which include the expected values of X and Z operators for each qubit as well as the clauses of the objective Hamiltonian individually.

At the end of each episode, the agent is rewarded with an amount equal to the expected value of the objective Hamiltonian in the final quantum state of the environment. Results for an instance of a 3-regular graph with 13 vertices are shown in the following graph:

Moreover, an incremental training strategy that allows the agent to reach larger *p’>p* episode lengths is successfully used for graphs with 21 qubits and p up to 25:

Welcome, Fabian!

]]>We hope you enjoy it as much as we did!

People in this video: Pol Forn, Artur García, David López, Adrián Pérez

]]>The title of the talks was: “Bricocuántica, ¿Cómo hacer un ordenador cuántico?” (Quantum DIY: how to make a quantum computer?)

It is always nice to get closer to new generations! Good job!

]]>Bachelor:

Gabriel Fernández: *Quantum Autoencoders*; 8,6

Elies Gil: Variational *Quantum Classifier*; 9,7

Josep Lumbreras: *Scaling of the energy and entropy errors in quantum circuits*; 9,1

Santi Vallés: *Design of infrared filters to improve the quality of a superconducting qubit*; 9,3

Master:

Sergi Ramos: *Maximal Entanglement in One-Loop Z Boson Decay*; 9,1

Rafael Luque: *Coherent control of a superconducting quantum bit*; 9

Everyone did a great job at Quantic. Some of them will continue within the team, other people will spread their wings. The best of luck for you all!

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