Experimental semi-autonomous eigensolver using reinforcement learning

AbstractThe characterization of observables, expressed via Hermitian operators, is a crucial task in quantum mechanics. For this reason, an eigensolver is a fundamental algorithm for any quantum technology. In this work, we implement a semi-autonomous algorithm to obtain an approximation of the eigenvectors of an arbitrary Hermitian operator using the IBM quantum computer. To this end, we only use single-shot measurements and pseudo-random changes handled by a feedback loop, reducing the number of measures in the system. Due to the classical feedback loop, this algorithm can be cast into the reinforcement learning paradigm. Using this algorithm, for a single-qubit observable, we obtain both eigenvectors with fidelities over 0.97 with around 200 single-shot measurements. For two-qubits observables, we get fidelities over 0.91 with around 1500 single-shot measurements for the four eigenvectors, which is a comparatively low resource demand, suitable for current devices. This work is useful to the development of quantum devices able to decide with partial information, which helps to implement future technologies in quantum artificial intelligence.

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Variational Quantum Reinforcement Learning via Evolutionary Optimization

Machine Learning: Science and Technology ◽

10.1088/2632-2153/ac4559 ◽

2021 ◽

Author(s):

Samuel Yen-Chi Chen ◽

Chih-Min Huang ◽

Chia-Wei Hsing ◽

Hsi-Sheng Goan ◽

Ying-Jer Kao

Keyword(s):

Reinforcement Learning ◽

Quantum Computer ◽

Quantum Circuit ◽

Quantum Devices ◽

Encoding Scheme ◽

Free Evolution ◽

Tensor Network ◽

Hybrid Framework ◽

Potential Applications ◽

Natural Way

Abstract Recent advance in classical reinforcement learning (RL) and quantum computation (QC) points to a promising direction of performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in modern quantum devices. Here we present two frameworks of deep quantum RL tasks using a gradient-free evolution optimization: First, we apply the amplitude encoding scheme to the Cart-Pole problem, where we demonstrate the quantum advantage of parameter saving using the amplitude encoding; Second, we propose a hybrid framework where the quantum RL agents are equipped with a hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs of dimensions exceeding the number of qubits. This allows us to perform quantum RL on the MiniGrid environment with 147-dimensional inputs. The hybrid TN-VQC architecture provides a natural way to perform efficient compression of the input dimension, enabling further quantum RL applications on noisy intermediate-scale quantum devices.

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Quantum Reinforcement Learning with Quantum Photonics

Photonics ◽

10.3390/photonics8020033 ◽

2021 ◽

Vol 8 (2) ◽

pp. 33

Author(s):

Lucas Lamata

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Quantum Computation ◽

Exchange Information ◽

Quantum Machine Learning ◽

Quantum Technology ◽

Quantum Photonics

Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum computation and communication, as well as machine learning, via the fruitful marriage between these previously unrelated fields.

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A single inverse-designed photonic structure that performs parallel computing

Nature Communications ◽

10.1038/s41467-021-21664-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Miguel Camacho ◽

Brian Edwards ◽

Nader Engheta

Keyword(s):

Wave Equation ◽

Feedback Loop ◽

Photonic Structure ◽

Quantum Devices ◽

Microwave Frequencies ◽

Parallel Solution ◽

Design Build ◽

Mathematical Problems ◽

Computing Structure ◽

Independent Integral

AbstractIn the search for improved computational capabilities, conventional microelectronic computers are facing various problems arising from the miniaturization and concentration of active electronics. Therefore, researchers have explored wave systems, such as photonic or quantum devices, for solving mathematical problems at higher speeds and larger capacities. However, previous devices have not fully exploited the linearity of the wave equation, which as we show here, allows for the simultaneous parallel solution of several independent mathematical problems within the same device. Here we demonstrate that a transmissive cavity filled with a judiciously tailored dielectric distribution and embedded in a multi-frequency feedback loop can calculate the solutions of a number of mathematical problems simultaneously. We design, build, and test a computing structure at microwave frequencies that solves two independent integral equations with any two arbitrary inputs and also provide numerical results for the calculation of the inverse of four 5 x 5 matrices.

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Decoherence dynamics estimation for superconducting gate-model quantum computers

Quantum Information Processing ◽

10.1007/s11128-020-02863-7 ◽

2020 ◽

Vol 19 (10) ◽

Cited By ~ 1

Author(s):

Laszlo Gyongyosi

Keyword(s):

Gaussian Noise ◽

Quantum Computer ◽

Computer Architectures ◽

Quantum Circuits ◽

Optimal Placement ◽

Quantum Computers ◽

Intermediate Scale ◽

Layout Generation ◽

Quantum Technology ◽

Physical Layout

Abstract Superconducting gate-model quantum computer architectures provide an implementable model for practical quantum computations in the NISQ (noisy intermediate scale quantum) technology era. Due to hardware restrictions and decoherence, generating the physical layout of the quantum circuits of a gate-model quantum computer is a challenge. Here, we define a method for layout generation with a decoherence dynamics estimation in superconducting gate-model quantum computers. We propose an algorithm for the optimal placement of the quantum computational blocks of gate-model quantum circuits. We study the effects of capacitance interference on the distribution of the Gaussian noise in the Josephson energy.

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Single Shot Avalanche Characterization of Series and Parallel Connection of SiC Power MOSFETs

2019 IEEE 7th Workshop on Wide Bandgap Power Devices and Applications (WiPDA) ◽

10.1109/wipda46397.2019.8998886 ◽

2019 ◽

Author(s):

Ashish Kumar ◽

Raj Kumar Kokkonda ◽

Subhashish Bhattacharya ◽

Victor Veliadis

Keyword(s):

Single Shot ◽

Parallel Connection ◽

Power Mosfets

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Single-Shot Full-Field Characterization of Short Pulses by Using Temporal Annealing Modified Gerchberg–Saxton Algorithm

Journal of Lightwave Technology ◽

10.1109/jlt.2017.2695722 ◽

2017 ◽

Vol 35 (13) ◽

pp. 2541-2547 ◽

Cited By ~ 2

Author(s):

Zhi Qiao ◽

Yudong Yao ◽

Xiaochao Wang ◽

Wei Fan ◽

Zunqi Lin

Keyword(s):

Single Shot ◽

Short Pulses ◽

Full Field

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Simplified Pre-Characterization of Chirped Pulses in Single-shot Supercontinuum Spectral Interferometry

Conference on Lasers and Electro-Optics ◽

10.1364/cleo_at.2018.jth2a.145 ◽

2018 ◽

Author(s):

Dinhduy Tran Vu ◽

Dogeun Jang ◽

Ki-Yong Kim

Keyword(s):

Single Shot ◽

Spectral Interferometry ◽

Chirped Pulses

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Characterization of linear maps onMnwhose multiplicity maps have maximal norm, with an application in quantum information

Quantum ◽

10.22331/q-2018-02-05-51 ◽

2018 ◽

Vol 2 ◽

pp. 51

Author(s):

Daniel Puzzuoli

Keyword(s):

Quantum Information ◽

Quantum Channel ◽

Operator Algebras ◽

Optimal Performance ◽

Single Shot ◽

Trace Norm ◽

Linear Maps ◽

The Family ◽

Matrix Transposition

Given a linear mapΦ:Mn→Mm, its multiplicity maps are defined as the family of linear mapsΦ⊗idk:Mn⊗Mk→Mm⊗Mk, whereidkdenotes the identity onMk. Let‖⋅‖1denote the trace-norm on matrices, as well as the induced trace-norm on linear maps of matrices, i.e.‖Φ‖1=max{‖Φ(X)‖1:X∈Mn,‖X‖1=1}. A fact of fundamental importance in both operator algebras and quantum information is that‖Φ⊗idk‖1can grow withk. In general, the rate of growth is bounded by‖Φ⊗idk‖1≤k‖Φ‖1, and matrix transposition is the canonical example of a map achieving this bound. We prove that, up to an equivalence, the transpose is the unique map achieving this bound. The equivalence is given in terms of complete trace-norm isometries, and the proof relies on a particular characterization of complete trace-norm isometries regarding preservation of certain multiplication relations.We use this result to characterize the set of single-shot quantum channel discrimination games satisfying a norm relation that, operationally, implies that the game can be won with certainty using entanglement, but is hard to win without entanglement. Specifically, we show that the well-known example of such a game, involving the Werner-Holevo channels, is essentially the unique game satisfying this norm relation. This constitutes a step towards a characterization of single-shot quantum channel discrimination games with maximal gap between optimal performance of entangled and unentangled strategies.

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Quantum Algorithms

Limitations and Future Applications of Quantum Cryptography - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-6677-0.ch005 ◽

2021 ◽

pp. 82-101

Author(s):

Renata Wong ◽

Amandeep Singh Bhatia

Keyword(s):

Quantum Computing ◽

Fault Tolerance ◽

Quantum Computation ◽

Quantum Computer ◽

Quantum Algorithms ◽

Computer Hardware ◽

Quantum Mechanical ◽

Computational Power ◽

Quantum Devices ◽

The Core

In the last two decades, the interest in quantum computation has increased significantly among research communities. Quantum computing is the field that investigates the computational power and other properties of computers on the basis of the underlying quantum-mechanical principles. The main purpose is to find quantum algorithms that are significantly faster than any existing classical algorithms solving the same problem. While the quantum computers currently freely available to wider public count no more than two dozens of qubits, and most recently developed quantum devices offer some 50-60 qubits, quantum computer hardware is expected to grow in terms of qubit counts, fault tolerance, and resistance to decoherence. The main objective of this chapter is to present an introduction to the core quantum computing algorithms developed thus far for the field of cryptography.

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