Optimizing Quantum Programs Against Decoherence: Delaying Qubits into Quantum Superposition

Zhang, Yu; Deng, H.W.; Li, Quanxi; Song, Haoze; Nie, Leihai

doi:10.1109/tase.2019.000-2

Cited by 19 publications

(18 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Similar to our experiment on simulator we followed the same principle and architecture design we explained in QuClassi design and we trained each epoch through IRIS data set with 8000 shots (number of repetitions of each circuit) to calculate the loss of the circuit. Based on our observation and previous research in this area [54,22] the integrity of physical qubits and T1, T2 errors of IBM-Q machines could vary, however, our design managed to attain an optimal solution after few iterations, comparable to the simulator results we attained. Since Machine learning applications, unlike chemistry or other noise-sensitive applications, can tolerate more noise within the system, running experiment on actual quantum machines generated stable results and accuracy similar to the simulators.…”

Section: Ibm-q Evaluationsupporting

confidence: 52%

Quantum Computing Aided Machine Learning Through Quantum State Fidelity

Stein¹

2021

Preprint

View full text Add to dashboard Cite

Tremendous progress has been witnessed in artificial intelligence within the domain of neural network backed deep learning systems and its applications. As we approach the post Moore’s Law era, the limit of semiconductor fabrication technology along with a rapid increase in data generation rates have lead to an impending growing challenge of tackling newer and more modern machine learning problems. In parallel, quantum computing has exhibited rapid development in recent years. Due to the potential of a quantum speedup, quantum based learning applications have become an area of significant interest, in hopes that we can leverage quantum systems to solve classical problems. In this work, we propose a quantum deep learning architecture; we demonstrate our quantum neural network architecture on tasks ranging from binary and multi-class classification to generative modelling. Powered by a modified quantum differentiation function along with a hybrid quantum-classic design, our architecture encodes the data with a reduced number of qubits and generates a quantum circuit, loading it onto a quantum platform where the model learns the optimal states iteratively. We conduct intensive experiments on both the local computing environment and IBM-Q quantum platform. The evaluation results demonstrate that our architecture is able to outperform Tensorflow-Quantum by up to 12.51% and 11.71% for a comparable classic deep neural network on the task of classification trained with the same network settings. Furthermore, our GAN architecture runs the discriminator and the generator purely on quantum hardware and utilizes the swap test on qubits to calculate the values of loss functions. In comparing our quantum GAN, we note our architecture is able to achieve similar performance with 98.5% reduction on the parameter set when compared to classical GANs. With the same number of parameters, additionally, QuGAN outperforms other quantum based GANs in the literature for up to 125.0% in terms of similarity between generated distributions and original data sets.

show abstract

Section: Ibm-q Evaluationsupporting

confidence: 52%

Quantum Computing Aided Machine Learning Through Quantum State Fidelity

Stein¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…We train each epoch through Iris dataset with 8000 shots (number of repetitions of each circuit) to calculate the loss of the circuit. Based on our observation and previous research in this area [20,46] the integrity of physical qubits and T1, T2 errors of IBM-Q machines could vary [41], however, our design managed to attain a solution after few iterations, comparable to the simulator results we attain. Running experiments on actual quantum computers generated stable results and accuracy similar to the simulators.…”

Section: Experiments On Ibm-qmentioning

confidence: 77%

QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity

Stein¹,

Baheri²,

Chen³

et al. 2021

Preprint

View full text Add to dashboard Cite

In the past decade, remarkable progress has been achieved in deep learning related systems and applications. In the post Moore's Law era, however, the limit of semiconductor fabrication technology along with the increasing data size have slowed down the development of learning algorithms. In parallel, the fast development of quantum computing has pushed it to the new ear. Google illustrates quantum supremacy by completing a specific task (random sampling problem), in 200 seconds, which is impracticable for the largest classical computers. Due to the limitless potential, quantum based learning is an area of interest, in hopes that certain systems might offer a quantum speedup. In this work, we propose a novel architecture QuClassi, a quantum neural network for both binary and multi-class classification. Powered by a quantum differentiation function along with a hybrid quantum-classic design, QuClassi encodes the data with a reduced number of qubits and generates the quantum circuit, pushing it to the quantum platform for the best states, iteratively. We conduct intensive experiments on both the simulator and IBM-Q quantum platform. The evaluation results demonstrate that QuClassi is able to outperform the state-of-the-art quantumbased solutions, Tensorflow-Quantum and QuantumFlow by up to 53.75% and 203.00% for binary and multi-class classifications. When comparing to traditional deep neural networks, QuClassi achieves a comparable performance with 97.37% fewer parameters.

show abstract

“…4.5). Additionally, the qubits must be mapped to available physical qubits, which influences the quantum algorithm execution as well, due to different characteristics of the qubits, such as decoherence time or connectivity [87]. However, the available quantum compilers are mostly vendor-specific [39], and therefore, compile the quantum algorithm implementations defined in the quantum assembler of a certain vendor to the executable for concrete quantum hardware that is provided by this vendor.…”

Section: Quantum Compilermentioning

confidence: 99%

Relevance of Near-Term Quantum Computing in the Cloud: A Humanities Perspective

Barzen

Leymann

Falkenthal³

et al. 2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

As quantum computers are becoming real, they have the inherent potential to significantly impact many application domains. In this paper we outline the fundamentals about programming quantum computers and show that quantum programs are typically hybrid consisting of a mixture of classical parts and quantum parts. With the advent of quantum computers in the cloud, the cloud is a fine environment for performing quantum programs. The tool chain available for creating and running such programs is sketched. As an exemplary problem we discuss efforts to implement quantum programs that are hardware independent. A use case from quantum humanities is discussed, hinting which applications in this domain can already be used in the field of (quantum) machine learning. Finally, a collaborative platform for solving problems with quantum computers-that is currently under construction-is presented.

show abstract

Optimizing Quantum Programs Against Decoherence: Delaying Qubits into Quantum Superposition

Cited by 19 publications

References 15 publications

Quantum Computing Aided Machine Learning Through Quantum State Fidelity

Quantum Computing Aided Machine Learning Through Quantum State Fidelity

QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity

Relevance of Near-Term Quantum Computing in the Cloud: A Humanities Perspective

Contact Info

Product

Resources

About