Quantum simulations of light-matter interactions in arbitrary coupling regimes

Light-matter interactions are an established field that is experiencing a renaissance in recent years due to the introduction of exotic coupling regimes. These include the ultrastrong and deep-strong coupling regimes, where the coupling constant is smaller and of the order of the frequency of the light mode, or larger than this frequency, respectively. In the past few years, quantum simulations of light-matter interactions in all possible coupling regimes have been proposed and experimentally realized, in quantum platforms such as trapped ions, superconducting circuits, cold atoms, and quantum photonics. We review this fledgling field, illustrating the benefits and challenges of the quantum simulations of light-matter interactions with quantum technologies.Ministerio de Ciencia, Innovación y Universidades PGC2018-095113-BI00, PID2019-104002GB-C21, and PID2019-104002GBC22 (MCIU/AEI/FEDER, UE

Reinforcement Learning and Physics

Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the actions taken by an agent in a given environment, can allow one for optimizing scientific discovery in a variety of fields such as physics, chemistry, and biology. Morover, physical systems, in particular quantum systems, may allow one for more efficient reinforcement learning protocols. In this review, we describe recent results in the field of reinforcement learning and physics. We include standard reinforcement learning techniques in the computer science community for enhancing physics research, as well as the more recent and emerging area of quantum reinforcement learning, inside quantum machine learning, for improving reinforcement learning computations.Ministerio de Ciencia e Innovación PGC2018- 095113-B-I00, PID2019-104002GB-C21 and PID2019-104002GB-C2

Quantum Machine Learning: A tutorial

This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technological companies as well as the popularity and success of ML have been responsible of making QML one of the main streams for researchers working on fuzzy borders between Physics, Mathematics and Computer Science. A possible, although arguably coarse, classification of QML methods may be based on those approaches that make use of ML in a quantum experimentation environment and those others that take advantage of QC and QI to find out alternative and enhanced solutions to problems driven by data, oftentimes offering a considerable speedup and improved performances as a result of tackling problems from a complete different standpoint. Several examples will be provided to illustrate both classes of methods.Ministerio de Ciencia, Innovación y Universidades GC2018-095113-B-I00,PID2019-104002GB-C21, and PID2019-104002GB-C22 (MCIU/AEI/FEDER, UE

Digital-analog quantum algorithm for the quantum Fourier transform

Quantum computers will allow calculations beyond existing classical computers. However, current technology is still too noisy and imperfect to construct a universal digital quantum computer with quantum error correction. Inspired by the evolution of classical computation, an alternative paradigm merging the flexibility of digital quantum computation with the robustness of analog quantum simulation has emerged. This universal paradigm is known as digital-analog quantum computing. Here, we introduce an efficient digital-analog quantum algorithm to compute the quantum Fourier transform, a subroutine widely employed in several relevant quantum algorithms. We show that, under reasonable assumptions about noise models, the fidelity of the quantum Fourier transformation improves considerably using this approach when the number of qubits involved grows. This suggests that, in the noisy intermediate-scale quantum era, hybrid protocols combining digital and analog quantum computing could be a sensible approach to reach useful quantum supremacy.Ministerio de Ciencia, Innovación y Universidades PGC2018-095113-B-I00Gobierno Vasco IT986-16EU Flagship on Quantum Technologies: QMiCS (820505) and OpenSuperQ (820363)EU FET Open Grant Quromorphic (828826

Adaptive random quantum eigensolver

We propose an adaptive random quantum algorithm to obtain an optimized eigensolver. Specifically, we introduce a general method to parametrize and optimize the probability density function of a random number generator, which is the core of stochastic algorithms. We follow a bioinspired evolutionary mutation method to introduce changes in the involved matrices. Our optimization is based on two figures of merit: learning speed and learning accuracy. This method provides high fidelities for the searched eigenvectors and faster convergence on the way to quantum advantage with current noisy intermediate-scaled quantum computers.Junta de Andalucía (Grants No. P20-00617 and No. US-1380840)Science and Technology Commission of Shanghai Municipality (Grant No. 2019SHZDZX01-ZX04

Quantum Advantage in Cryptography with a Low-Connectivity Quantum Annealer

The application in cryptography of quantum algorithms for prime factorization fostered the interest in quantum computing. However, quantum computers, and particularly quantum annealers, can also be helpful to construct secure cryptographic keys. Indeed, finding robust Boolean functions for cryptography is an important problem in sequence ciphers, block ciphers, and hash functions, among others. Due to the superexponential size O(22n) of the associated space, finding n-variable Boolean functions with global cryptographic constraints is computationally hard. This problem has already been addressed employing generic low-connected incoherent D-Wave quantum annealers. However, the limited connectivity of the Chimera graph, together with the exponential growth in the complexity of the Boolean-function design problem, limit the problem scalability. Here, we propose a special-purpose coherent quantum-annealing architecture with three couplers per qubit, designed to optimally encode the bent-function design problem. A coherent quantum annealer with this tree-type architecture has the potential to solve the eight-variable bent-function design problem, which is classically unsolved, with only 127 physical qubits and 126 couplers. This paves the way to reach useful quantum supremacy within the framework of quantum annealing for cryptographic purposes

Multiqubit and multilevel quantum reinforcement learning with quantum technologies

We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations.We acknowledge support from CEDENNA basal grant No. FB0807 and Direccion de Postgrado USACH (FAC-L), FONDECYT under grant No. 1140194 (JCR), Spanish MINECO/FEDER FIS2015-69983-P and Basque Government IT986-16 (LL and ES), and Ramon y Cajal Grant RYC-2012-11391 (LL)

Probabilistic eigensolver with a trapped-ion quantum processor

Preparing the eigenstate, especially the ground state, of a complex Hamiltonian is of great importance in quantum simulations. Many proposals have been introduced and experimentally realized, among which are quantum variational eigensolver and heat-bath algorithmic cooling, with the former hindered by local minima and the latter lacking of complex system Hamiltonians. Here we introduce a dissipative quantum-classical hybrid scheme, the probabilistic eigensolver. The scheme repeatedly uses an ancilla qubit to acquire information on the system, based on which it postselectively lowers the average energy of the system. The optimal reduction is achieved through classical optimization with a single variational parameter. We describe the implementation of the probabilistic eigensolver with trapped-ion systems and demonstrate the performance by numerically simulating the ground-state preparation of several paradigmatic models, including the Rabi and the Hubbard models. We believe the scheme would enrich the functionalities of universal quantum simulators and be useful as a module for various quantum-computation tasks

Quantum Artificial Life in an IBM Quantum Computer

We present the first experimental realization of a quantum artificial life algorithm in a quantum computer. The quantum biomimetic protocol encodes tailored quantum behaviors belonging to living systems, namely, self-replication, mutation, interaction between individuals, and death, into the cloud quantum computer IBM ibmqx4. In this experiment, entanglement spreads throughout generations of individuals, where genuine quantum information features are inherited through genealogical networks. As a pioneering proof-of-principle, experimental data fits the ideal model with accuracy. Thereafter, these and other models of quantum artificial life, for which no classical device may predict its quantum supremacy evolution, can be further explored in novel generations of quantum computers. Quantum biomimetics, quantum machine learning, and quantum artificial intelligence will move forward hand in hand through more elaborate levels of quantum complexity. © 2018, The Author(s).We acknowledge support from Spanish MINECO/FEDER FIS2015-69983-P, UPV/EHU new PhD program, Basque Government Programa Posdoctoral de Perfeccionamiento de Personal Investigador Doctor, Basque Government IT986-16, and Ramón y Cajal Grant RYC-2012-11391