Realising and Compressing Quantum Circuits With Quantum Reservoir Computing

Abstract Quantum computers require precise control over parameters and careful engineering of the underlying physical system. In contrast, neural networks have evolved to tolerate imprecision and inhomogeneity. Here, using a reservoir computing architecture we show how a random network of quantum nodes can be used as a robust hardware for quantum computing. It induces quantum operations by optimising only a single layer of quantum nodes, a key advantage over the traditional neural networks where many layers of neurons have to be optimised. We demonstrate how a single network can induce different quantum gates, including a universal gate set. Moreover, we show that sequences of multiple quantum gates in quantum circuits can be compressed with a single operation, greatly reducing the operation time and complexity. As the key resource is a random network of nodes, with no specific topology or structure, this architecture is a hardware friendly alternative paradigm for quantum computation.

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Realising and compressing quantum circuits with quantum reservoir computing

Communications Physics ◽

10.1038/s42005-021-00606-3 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Sanjib Ghosh ◽

Tanjung Krisnanda ◽

Tomasz Paterek ◽

Timothy C. H. Liew

Keyword(s):

Neural Networks ◽

Network Architecture ◽

Random Network ◽

Single Layer ◽

Operation Time ◽

Quantum Circuits ◽

Quantum Gates ◽

Multiple Quantum ◽

Reservoir Computing ◽

Precise Control

AbstractQuantum computers require precise control over parameters and careful engineering of the underlying physical system. In contrast, neural networks have evolved to tolerate imprecision and inhomogeneity. Here, using a reservoir computing architecture we show how a random network of quantum nodes can be used as a robust hardware for quantum computing. Our network architecture induces quantum operations by optimising only a single layer of quantum nodes, a key advantage over the traditional neural networks where many layers of neurons have to be optimised. We demonstrate how a single network can induce different quantum gates, including a universal gate set. Moreover, in the few-qubit regime, we show that sequences of multiple quantum gates in quantum circuits can be compressed with a single operation, potentially reducing the operation time and complexity. As the key resource is a random network of nodes, with no specific topology or structure, this architecture is a hardware friendly alternative paradigm for quantum computation.

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Quantum Computing in the NISQ era and beyond

Quantum ◽

10.22331/q-2018-08-06-79 ◽

2018 ◽

Vol 2 ◽

pp. 79 ◽

Cited By ~ 691

Author(s):

John Preskill

Keyword(s):

Quantum Computing ◽

Quantum Physics ◽

Quantum Computer ◽

Fault Tolerant ◽

Quantum Circuits ◽

Quantum Gates ◽

Quantum Computers ◽

Many Body ◽

Significant Step ◽

Near Future

Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably. NISQ devices will be useful tools for exploring many-body quantum physics, and may have other useful applications, but the 100-qubit quantum computer will not change the world right away - we should regard it as a significant step toward the more powerful quantum technologies of the future. Quantum technologists should continue to strive for more accurate quantum gates and, eventually, fully fault-tolerant quantum computing.

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Nonlocal implementation of multi-qubit controlled unitary quantum gates with quantum channel

Modern Physics Letters A ◽

10.1142/s0217732321502448 ◽

2021 ◽

Author(s):

Engin Şahin ◽

İhsan Yilmaz

Keyword(s):

Quantum Channel ◽

Hybrid Methods ◽

Quantum Computer ◽

Communication Channels ◽

Quantum Measurements ◽

Quantum Gates ◽

Quantum Computers ◽

Multiple Quantum ◽

Classical Communication ◽

Target State

Quantum computers are very efficient in terms of speed and security. Decoherence and architectural complexity restrict the control of sensitive quantum information as the number of qubits in a quantum computer increases. Therefore, it is more convenient to make a device with multiple quantum processors with small number of qubits instead of making a device with a large number of qubits quantum processors. The implementation of controlled unitary gates is a problem in such nonlocal systems. The methods in the literature for this problem use entangled qubit pairs, classical communication channels and classical bits. The existing methods perform some unitary operations on the target state for reconstruction after sending information through classical communication channels and applying quantum measurements on control states. In this study, a generalized method for nonlocal implementation of multi-qubit controlled unitary quantum gates with quantum channel is proposed. The proposed method can implement any controlled gate on the control and target qubits that are far from each other in terms of location. The method does not require classical channels and classical bits, any extra unitary operation for reconstruction. The proposed method is both more secure and uses less resources for operations than the other hybrid methods in the literature. Comparisons with existing studies are given in terms of required entangled qubit pairs, classical channels and bits, extra unitary operations for reconstruction the target state, and the advantages of the proposed method are revealed.

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Deep neural networks for quantum circuit mapping

Neural Computing and Applications ◽

10.1007/s00521-021-06009-3 ◽

2021 ◽

Author(s):

Giovanni Acampora ◽

Roberto Schiattarella

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Quantum Algorithms ◽

Quantum Algorithm ◽

Quantum Circuit ◽

Machine Learning Techniques ◽

Quantum Computers ◽

Physical Constraints ◽

Proper Mapping ◽

Correct Execution

AbstractQuantum computers have become reality thanks to the effort of some majors in developing innovative technologies that enable the usage of quantum effects in computation, so as to pave the way towards the design of efficient quantum algorithms to use in different applications domains, from finance and chemistry to artificial and computational intelligence. However, there are still some technological limitations that do not allow a correct design of quantum algorithms, compromising the achievement of the so-called quantum advantage. Specifically, a major limitation in the design of a quantum algorithm is related to its proper mapping to a specific quantum processor so that the underlying physical constraints are satisfied. This hard problem, known as circuit mapping, is a critical task to face in quantum world, and it needs to be efficiently addressed to allow quantum computers to work correctly and productively. In order to bridge above gap, this paper introduces a very first circuit mapping approach based on deep neural networks, which opens a completely new scenario in which the correct execution of quantum algorithms is supported by classical machine learning techniques. As shown in experimental section, the proposed approach speeds up current state-of-the-art mapping algorithms when used on 5-qubits IBM Q processors, maintaining suitable mapping accuracy.

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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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Identify PMSM's Parameters by Single-Layer Neural Networks with Gradient Descent

2010 International Conference on Electrical and Control Engineering ◽

10.1109/icece.2010.930 ◽

2010 ◽

Cited By ~ 2

Author(s):

Wang Shaowei ◽

Wan Shanming

Keyword(s):

Neural Networks ◽

Gradient Descent ◽

Single Layer

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Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Foods ◽

10.3390/foods10040879 ◽

2021 ◽

Vol 10 (4) ◽

pp. 879

Author(s):

Clíssia Barboza da Silva ◽

Alysson Alexander Naves Silva ◽

Geovanny Barroso ◽

Pedro Takao Yamamoto ◽

Valter Arthur ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Prediction Models ◽

Sitophilus Zeamais ◽

Human Consumption ◽

Storage Pests ◽

Maize Weevil ◽

X Ray ◽

Precise Control ◽

X Ray Imaging

The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of Sitophilus zeamais in maize grains. In addition, we proposed an approach based on convolutional neural networks (CNNs) to identity non-infested and infested classes using three different architectures; (i) Inception-ResNet-v2, (ii) Xception and (iii) MobileNetV2. In general, the prediction models developed based on the MobileNetV2 and Xception architectures achieved higher accuracy (≥0.88) in identifying non-infested grains and grains infested by maize weevil, with a correct classification from 0.78 to 1.00 for validation and test sets. Hence, the proposed approach using enhanced radiographs has the potential to provide precise control of Sitophilus zeamais for safe human consumption of maize grains. The proposed method can automatically recognize food contaminated with hidden storage pests without manual features, which makes it more reliable for grain inspection.

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Towards Compact Modeling of Noisy Quantum Computers: A Molecular-Spin-Qubit Case of Study

ACM Journal on Emerging Technologies in Computing Systems ◽

10.1145/3474223 ◽

2022 ◽

Vol 18 (1) ◽

pp. 1-26

Author(s):

Mario Simoni ◽

Giovanni Amedeo Cirillo ◽

Giovanna Turvani ◽

Mariagrazia Graziano ◽

Maurizio Zamboni

Keyword(s):

Physical Simulation ◽

Quantum Circuits ◽

Compact Modeling ◽

Quantum Computers ◽

Expected Performance ◽

Classical Simulation ◽

Spin Qubit ◽

Compact Models ◽

Standard Formalism ◽

Definition Of

Classical simulation of Noisy Intermediate Scale Quantum computers is a crucial task for testing the expected performance of real hardware. The standard approach, based on solving Schrödinger and Lindblad equations, is demanding when scaling the number of qubits in terms of both execution time and memory. In this article, attempts in defining compact models for the simulation of quantum hardware are proposed, ensuring results close to those obtained with standard formalism. Molecular Nuclear Magnetic Resonance quantum hardware is the target technology, where three non-ideality phenomena—common to other quantum technologies—are taken into account: decoherence, off-resonance qubit evolution, and undesired qubit-qubit residual interaction. A model for each non-ideality phenomenon is embedded into a MATLAB simulation infrastructure of noisy quantum computers. The accuracy of the models is tested on a benchmark of quantum circuits, in the expected operating ranges of quantum hardware. The corresponding outcomes are compared with those obtained via numeric integration of the Schrödinger equation and the Qiskit’s QASMSimulator. The achieved results give evidence that this work is a step forward towards the definition of compact models able to provide fast results close to those obtained with the traditional physical simulation strategies, thus paving the way for their integration into a classical simulator of quantum computers.

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