scholarly journals Universal discriminative quantum neural networks

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
H. Chen ◽  
L. Wossnig ◽  
S. Severini ◽  
H. Neven ◽  
M. Mohseni
Keyword(s):  
Machine Learning ◽  
Hybrid Methods ◽  
Quantum Circuit ◽  
Learning Task ◽  
Error Rates ◽  
Quantum Circuits ◽  
Circuit Optimization ◽  
Learning Tasks ◽  
Classical Analogue ◽  
Optimal Values

AbstractRecent results have demonstrated the successful applications of quantum-classical hybrid methods to train quantum circuits for a variety of machine learning tasks. A natural question to ask is consequentially whether we can also train such quantum circuits to discriminate quantum data, i.e., perform classification on data stored in form of quantum states. Although quantum mechanics fundamentally forbids deterministic discrimination of non-orthogonal states, we show in this work that it is possible to train a quantum circuit to discriminate such data with a trade-off between minimizing error rates and inconclusiveness rates of the classification tasks. Our approach achieves at the same time a performance which is close to the theoretically optimal values and a generalization ability to previously unseen quantum data. This generalization power hence distinguishes our work from previous circuit optimization results and furthermore provides an example of a quantum machine learning task that has inherently no classical analogue.

scholarly journals Connectivity matrix model of quantum circuits and its application to distributed quantum circuit optimization

2021 ◽  
Vol 20 (7) ◽  
Author(s):  
Ismail Ghodsollahee ◽  
Zohreh Davarzani ◽  
Mariam Zomorodi ◽  
Paweł Pławiak ◽  
Monireh Houshmand ◽  
...  
Keyword(s):  
Quantum Computation ◽  
Quantum Computer ◽  
Quantum Circuit ◽  
Quantum Circuits ◽  
Connectivity Matrix ◽  
Circuit Optimization ◽  
Novel Approach ◽  
The Matrix ◽  
Minimum Number ◽  
Two Phases

AbstractAs quantum computation grows, the number of qubits involved in a given quantum computer increases. But due to the physical limitations in the number of qubits of a single quantum device, the computation should be performed in a distributed system. In this paper, a new model of quantum computation based on the matrix representation of quantum circuits is proposed. Then, using this model, we propose a novel approach for reducing the number of teleportations in a distributed quantum circuit. The proposed method consists of two phases: the pre-processing phase and the optimization phase. In the pre-processing phase, it considers the bi-partitioning of quantum circuits by Non-Dominated Sorting Genetic Algorithm (NSGA-III) to minimize the number of global gates and to distribute the quantum circuit into two balanced parts with equal number of qubits and minimum number of global gates. In the optimization phase, two heuristics named Heuristic I and Heuristic II are proposed to optimize the number of teleportations according to the partitioning obtained from the pre-processing phase. Finally, the proposed approach is evaluated on many benchmark quantum circuits. The results of these evaluations show an average of 22.16% improvement in the teleportation cost of the proposed approach compared to the existing works in the literature.

scholarly journals Quantum generative adversarial learning in a superconducting quantum circuit

2019 ◽  
Vol 5 (1) ◽  
pp. eaav2761 ◽  
Author(s):  
Ling Hu ◽  
Shu-Hao Wu ◽  
Weizhou Cai ◽  
Yuwei Ma ◽  
Xianghao Mu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Circuit ◽  
High Fidelity ◽  
Adversarial Learning ◽  
Classical Counterpart ◽  
Learning Tasks ◽  
Challenging Tasks ◽  
Data Output ◽  
Quantum Version ◽  
Channel Simulator

Generative adversarial learning is one of the most exciting recent breakthroughs in machine learning. It has shown splendid performance in a variety of challenging tasks such as image and video generation. More recently, a quantum version of generative adversarial learning has been theoretically proposed and shown to have the potential of exhibiting an exponential advantage over its classical counterpart. Here, we report the first proof-of-principle experimental demonstration of quantum generative adversarial learning in a superconducting quantum circuit. We demonstrate that, after several rounds of adversarial learning, a quantum-state generator can be trained to replicate the statistics of the quantum data output from a quantum channel simulator, with a high fidelity (98.8% on average) so that the discriminator cannot distinguish between the true and the generated data. Our results pave the way for experimentally exploring the intriguing long-sought-after quantum advantages in machine learning tasks with noisy intermediate–scale quantum devices.

A Quantum Circuit Optimization Framework Based on Pattern Matching

SPIN ◽  
2021 ◽  
Author(s):  
Mingyu Chen ◽  
Yu Zhang ◽  
Yongshang Li
Keyword(s):  
Pattern Matching ◽  
Quantum Algorithms ◽  
General Pattern ◽  
Quantum Circuit ◽  
Quantum Circuits ◽  
Circuit Optimization ◽  
Coherent Noise ◽  
Pattern Description ◽  
Algorithm Framework ◽  
Machine Readable

In the NISQ era, quantum computers have insufficient qubits to support quantum error correction, which can only perform shallow quantum algorithms under noisy conditions. Aiming to improve the fidelity of quantum circuits, it is necessary to reduce the circuit depth as much as possible to mitigate the coherent noise. To address the issue, we propose PaF , a Pattern matching-based quantum circuit rewriting algorithm Framework to optimize quantum circuits. The algorithm framework finds all sub-circuits satisfied in the input quantum circuit according to the given external pattern description, then replaces them with better circuit implementations. To extend the capabilities of PaF , a general pattern description format is proposed to make rewriting patterns in existing work become machine-readable. In order to evaluate the effectiveness of PaF , we employ the BIGD benchmarks in QUEKO benchmark suite to test the performance and the result shows that PaF provides a maximal speedup of [Formula: see text] by using few patterns.

Ensemble Learning for Regression

2011 ◽  
pp. 777-782
Author(s):  
Niall Rooney
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Neural Networks ◽  
Ensemble Learning ◽  
Learning Task ◽  
General Definition ◽  
Learning Tasks ◽  
Continuous Output ◽  
Input Variables ◽  
The Given

The concept of ensemble learning has its origins in research from the late 1980s/early 1990s into combining a number of artificial neural networks (ANNs) models for regression tasks. Ensemble learning is now a widely deployed and researched topic within the area of machine learning and data mining. Ensemble learning, as a general definition, refers to the concept of being able to apply more than one learning model to a particular machine learning problem using some method of integration. The desired goal of course is that the ensemble as a unit will outperform any of its individual members for the given learning task. Ensemble learning has been extended to cover other learning tasks such as classification (refer to Kuncheva, 2004 for a detailed overview of this area), online learning (Fern & Givan, 2003) and clustering (Strehl & Ghosh, 2003). The focus of this article is to review ensemble learning with respect to regression, where by regression, we refer to the supervised learning task of creating a model that relates a continuous output variable to a vector of input variables.

scholarly journals Efficient and effective quantum compiling for entanglement-based machine learning on IBM Q devices

2018 ◽  
Vol 16 (08) ◽  
pp. 1840006 ◽  
Author(s):  
Davide Ferrari ◽  
Michele Amoretti
Keyword(s):  
Machine Learning ◽  
Quantum Algorithms ◽  
Quantum Circuit ◽  
Quantum Circuits ◽  
Entangled States ◽  
Hard Problem ◽  
Practical Applications ◽  
Ghz States ◽  
Quantum Machine Learning ◽  
Uniform Quantum

Quantum compiling means fast, device-aware implementation of quantum algorithms (i.e. quantum circuits, in the quantum circuit model of computation). In this paper, we present a strategy for compiling IBM Q-aware, low-depth quantum circuits that generate Greenberger–Horne–Zeilinger (GHZ) entangled states. The resulting compiler can replace the QISKit compiler for the specific purpose of obtaining improved GHZ circuits. It is well known that GHZ states have several practical applications, including quantum machine learning. We illustrate our experience in implementing and querying a uniform quantum example oracle based on the GHZ circuit, for solving the classically hard problem of learning parity with noise.

scholarly journals Quantum Circuit Synthesis Using Projective Simulation

2021 ◽  
Vol 24 (67) ◽  
pp. 90-101
Author(s):  
Otto Menegasso Pires ◽  
Eduardo Inacio Duzzioni ◽  
Jerusa Marchi ◽  
Rafael De Santiago
Keyword(s):  
Quantum Computing ◽  
Quantum Algorithms ◽  
Quantum Circuit ◽  
Optimization Techniques ◽  
Error Tolerance ◽  
Quantum Circuits ◽  
Quantum Decoherence ◽  
Circuit Synthesis ◽  
Circuit Optimization ◽  
Learning Technique

Quantum Computing has been evolving in the last years. Although nowadays quantum algorithms performance has shown superior to their classical counterparts, quantum decoherence and additional auxiliary qubits needed for error tolerance routines have been huge barriers for quantum algorithms efficient use.These restrictions lead us to search for ways to minimize algorithms costs, i.e the number of quantum logical gates and the depth of the circuit. For this, quantum circuit synthesis and quantum circuit optimization techniques are explored.We studied the viability of using Projective Simulation, a reinforcement learning technique, to tackle the problem of quantum circuit synthesis. The agent had the task of creating quantum circuits up to 5 qubits. Our simulations demonstrated that the agent had a good performance but its capacity for learning new circuits decreased as the number of qubits increased.

scholarly journals KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response

2020 ◽  
Author(s):  
Justin Reese ◽  
Deepak Unni ◽  
Tiffany J. Callahan ◽  
Luca Cappelletti ◽  
Vida Ravanmehr ◽  
...  
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Learning Task ◽  
Research Community ◽  
Biological Knowledge ◽  
Biomedical Data ◽  
Effective Response ◽  
Learning Tasks ◽  
Flexible Framework ◽  
Knowledge Graphs

SUMMARYIntegrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks - the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.BIGGER PICTUREAn effective response to the COVID-19 pandemic relies on integration of many different types of data available about SARS-CoV-2 and related viruses. KG-COVID-19 is a framework for producing knowledge graphs that can be customized for downstream applications including machine learning tasks, hypothesis-based querying, and browsable user interface to enable researchers to explore COVID-19 data and discover relationships.

Handling Local Patterns in Collaborative Structuring

2008 ◽  
pp. 167-186
Author(s):  
Ingo Mierswa ◽  
Katharina Morik ◽  
Michael Wurst
Keyword(s):  
Machine Learning ◽  
Learning Task ◽  
Media Management ◽  
Cluster Ensembles ◽  
Learning Tasks ◽  
New Learning ◽  
Local Patterns ◽  
Uniform Manner ◽  
Personal Media ◽  
Private Collections

Media collections in the internet have become a commercial success and the structuring of large media collections has thus become an issue. Personal media collections are locally structured in very different ways by different users. The level of detail, the chosen categories, and the extensions can differ completely from user to user. Can machine learning be of help also for structuring personal collections? Since users do not want to have their hand-made structures overwritten, one could deny the benefit of automatic structuring. We argue that what seems to exclude machine learning, actually poses a new learning task. We propose a notation which allows us to describe machine learning tasks in a uniform manner. Keeping the demands of structuring private collections in mind, we define the new learning task of localized alternative cluster ensembles. An algorithm solving the new task is presented together with its application to distributed media management.

scholarly journals Quantum circuit optimization using quantum Karnaugh map

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
J.-H. Bae ◽  
Paul M. Alsing ◽  
Doyeol Ahn ◽  
Warner A. Miller
Keyword(s):  
Quantum Computation ◽  
Quantum Algorithms ◽  
Optimization Technique ◽  
Circuit Complexity ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Quantum Circuits ◽  
Circuit Optimization ◽  
General Technique ◽  
Small Quantum

Abstract Every quantum algorithm is represented by set of quantum circuits. Any optimization scheme for a quantum algorithm and quantum computation is very important especially in the arena of quantum computation with limited number of qubit resources. Major obstacle to this goal is the large number of elemental quantum gates to build even small quantum circuits. Here, we propose and demonstrate a general technique that significantly reduces the number of elemental gates to build quantum circuits. This is impactful for the design of quantum circuits, and we show below this could reduce the number of gates by 60% and 46% for the four- and five-qubit Toffoli gates, two key quantum circuits, respectively, as compared with simplest known decomposition. Reduced circuit complexity often goes hand-in-hand with higher efficiency and bandwidth. The quantum circuit optimization technique proposed in this work would provide a significant step forward in the optimization of quantum circuits and quantum algorithms, and has the potential for wider application in quantum computation.

scholarly journals Evaluation of parameterized quantum circuits: on the relation between classification accuracy, expressibility, and entangling capability

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Thomas Hubregtsen ◽  
Josef Pichlmeier ◽  
Patrick Stecher ◽  
Koen Bertels
Keyword(s):  
Machine Learning ◽  
Hilbert Space ◽  
Classification Accuracy ◽  
Numerical Study ◽  
Feature Space ◽  
Quantum Circuit ◽  
Quantum Circuits ◽  
Prior Art ◽  
Quantum Machine Learning ◽  
Future Work

AbstractAn active area of investigation in the search for quantum advantage is quantum machine learning. Quantum machine learning, and parameterized quantum circuits in a hybrid quantum-classical setup in particular, could bring advancements in accuracy by utilizing the high dimensionality of the Hilbert space as feature space. But is the ability of a quantum circuit to uniformly address the Hilbert space a good indicator of classification accuracy? In our work, we use methods and quantifications from prior art to perform a numerical study in order to evaluate the level of correlation. We find a moderate to strong correlation between the ability of the circuit to uniformly address the Hilbert space and the achieved classification accuracy for circuits that entail a single embedding layer followed by 1 or 2 circuit designs. This is based on our study encompassing 19 circuits in both 1- and 2-layer configurations, evaluated on 9 datasets of increasing difficulty. We also evaluate the correlation between entangling capability and classification accuracy in a similar setup, and find a weak correlation. Future work will focus on evaluating if this holds for different circuit designs.

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