Quantum Artificial Life in an IBM Quantum Computer

Alvarez-Rodriguez, Unai; Sanz, Mikel; Lamata, Lucas; Solano, E.

doi:10.1038/s41598-018-33125-3

Cited by 70 publications

(39 citation statements)

References 42 publications

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“…IBM has developed prototypes of 5-, 16-, 20-and 50-qubit quantum computer 47 , which has attracted the attention of a large number of researchers working in various sub-field of quantum computation and quantum information. A number of experiments have been tested and verified using 5-qubit and 16-qubit quantum computer [48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] . Here, we use IBM's 5 qubit quantum processor 'ibmqx4' to demonstrate the working of a quantum repeater.…”

Section: Introductionmentioning

confidence: 99%

Demonstration of entanglement purification and swapping protocol to design quantum repeater in IBM quantum computer

Behera

Seth

Das

et al. 2019

Quantum Inf Process

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Quantum communication is a secure way to transfer quantum information and to communicate with legitimate parties over distant places in a network. Although communication over a long distance has already been attained, technical problem arises due to unavoidable loss of information through the transmission channel. Quantum repeaters can extend the distance scale using entanglement swapping and purification scheme. Here we demonstrate the working of a quantum repeater by the above two processes. We use IBM's real quantum processor 'ibmqx4' to create two pair of entangled qubits and design an equivalent quantum circuit which consequently swaps the entanglement between the two pairs. We then develop a novel purification protocol which enhances the degree of entanglement in a noisy channel that includes combined errors of bit-flip, phase-flip and phase-change error. We perform quantum state tomography to verify the entanglement swapping between the two pairs of qubits and working of the purification protocol. *

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Section: Introductionmentioning

confidence: 99%

Demonstration of entanglement purification and swapping protocol to design quantum repeater in IBM quantum computer

Behera

Seth

Das

et al. 2019

Quantum Inf Process

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“…Superconducting circuits is one of the leading quantum platforms, allowing for controllability, scalability, and coherence. The combination of biosciences with quantum technologies gave rise to a variety of novel fields, as quantum artificial intelligence [12], quantum biology [13,14], quantum artificial life [15,16], and quantum biomimetics [17], among others. From a wide perspective, we are heading towards a general framework for neuromorphic quantum computing, in which braininspired architectures strive to take advantage of quantum features to enhance computational power.…”

Section: Introductionmentioning

confidence: 99%

Quantized Single-Ion-Channel Hodgkin-Huxley Model for Quantum Neurons

et al. 2019

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The Hodgkin-Huxley model describes the behavior of the cell membrane in neurons, treating each part of it as an electric circuit element, namely capacitors, memristors, and voltage sources. We focus on the activation channel of potassium ions, due to its simplicity, while keeping most of the features displayed by the original model. This reduced version is essentially a classical memristor, a resistor whose resistance depends on the history of electric signals that have crossed it, coupled to a voltage source and a capacitor. Here, we will consider a quantized Hodgkin-Huxley model based on a quantum memristor formalism. We compare the behavior of the membrane voltage and the potassium channel conductance, when the circuit is subjected to AC sources, in both classical and quantum realms. Numerical simulations show an expected adaptation of the considered channel conductance depending on the signal history in all regimes. Remarkably, the computation of higher moments of the voltage manifest purely quantum features related to the circuit zero-point energy. This study may allow the construction of quantum neuron networks inspired in the brain function, as well as the design of neuromorphic quantum architectures for quantum machine learning.

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“…We also show how to extend the algorithm to the multiqubit and high-dimensional situations. This protocol could be useful to implement semi-autonomous quantum devices with the capability to decide using the characterization of an interaction, which is an essential ingredient for the implementation of artificial quantum intelligence [4] and artificial quantum life [48,49].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for semi-autonomous approximate quantum eigensolver

Albarrán-Arriagada

Retamal

Solano

et al. 2020

Mach. Learn.: Sci. Technol.

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The characterization of an operator by its eigenvectors and eigenvalues allows us to know its action over any quantum state. Here, we propose a protocol to obtain an approximation of the eigenvectors of an arbitrary Hermitian quantum operator. This protocol is based on measurement and feedback processes, which characterize a reinforcement learning protocol. Our proposal is composed of two systems, a black box named environment and a quantum state named agent. The role of the environment is to change any quantum state by a unitary matrixˆ= tHermitian operator, and τ is a real parameter. The agent is a quantum state which adapts to some eigenvector of  E by repeated interactions with the environment, feedback process, and semi-random rotations. With this proposal, we can obtain an approximation of the eigenvectors of a random qubit operator with average fidelity over 90% in less than 10 iterations, and surpass 98% in less than 300 iterations. Moreover, for the two-qubit cases, the four eigenvectors are obtained with fidelities above 89% in 8000 iterations for a random operator, and fidelities of 99% for an operator with the Bell states as eigenvectors. This protocol can be useful to implement semi-autonomous quantum devices which should be capable of extracting information and deciding with minimal resources and without human intervention.

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Quantum Artificial Life in an IBM Quantum Computer

Cited by 70 publications

References 42 publications

Demonstration of entanglement purification and swapping protocol to design quantum repeater in IBM quantum computer

Demonstration of entanglement purification and swapping protocol to design quantum repeater in IBM quantum computer

Quantized Single-Ion-Channel Hodgkin-Huxley Model for Quantum Neurons

Reinforcement learning for semi-autonomous approximate quantum eigensolver

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