Optimization of Circuits for IBM's Five-Qubit Quantum Computers

Dueck, Gerhard W.; Pathak, Anirban; Rahman, Mazder; Shukla, Abhishek; Banerjee, Anindita

doi:10.1109/dsd.2018.00005

Cited by 30 publications

(29 citation statements)

References 18 publications

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“…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

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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.

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Section: Ibm-q Evaluationsupporting

confidence: 52%

Quantum Computing Aided Machine Learning Through Quantum State Fidelity

Stein¹

2021

Preprint

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“…Figure 1(a) shows a representation of an input circuit. The target architecture to which we intend to compile our quantum algorithm is represented using an undirected graph called a coupling graph, G = (V, E), with vertices V and edges E. Each physical qubit is represented as a vertex v ∈ V and the two-qubit gates available between physical qubits are represented by edges (u, v) ∈ E between vertices u and v. We do not consider direction for the edges in this paper; however, our algorithm can be adapted to the case of devices with a particular direction for two-qubit gates as in the case of [28]. Figure 1(b) shows the coupling graph of the IBM Q20 Tokyo chip as an example target architecture [29].…”

Section: The Placement and Routing Problemmentioning

confidence: 99%

Nuwa: A Quantum Circuit Transpiler Based on a Finite-Horizon Heuristic for Placement and Routing

Ren¹,

Chen²,

Ghadermarzy³

et al. 2021

Preprint

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We introduce a novel transpiler for the placement and routing of quantum circuits on arbitrary target hardware architectures. We use finite-horizon, and optionally discounted, reward functions to heuristically find a suitable placement and routing policy. We employ a finite lookahead to refine the reward functions when breaking a tie between multiple policies. We benchmark our transpiler against multiple alternative solutions and on various test sets of quantum algorithms to demonstrate the benefits of our approach.

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“…2 (b). Further, the qubits are chosen such that the circuit after transpilation has a minimal circuit cost [10]. According to the measurement results announced by Alice, Bob 1 and Bob 2 apply corresponding unitaries to obtain the teleported states.…”

Section: Multi-output Quantum Teleportation Using Two Bell Statesmentioning

confidence: 99%

Comment on "Multi-output quantum teleportation of different quantum information with an IBM quantum experience"

Kumar¹

2021

Preprint

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Recently, Yu et al., (Commun. Theor. Phys. 73 (2021) 085103) has proposed a scheme for "multi-output quantum teleportation" and has implemented the scheme using an IBM quantum computer. In their so called multicast-based quantum teleportation scheme, a sender (Alice) teleported two different quantum states (one of which is a m-qubit GHZ class state and the other is a (m + 1)-qubit GHZ class state) to the two receivers. To perform the task, a five-qubit cluster state was used as a quantum channel, and the scheme was realized using IBM quantum computer for m = 1. In this comment, it is shown that the quantum resources used by Yu et al., was extensively high. One can perform the same task of two-party quantum teleportation using two Bell states only. The modified scheme for multi-output teleportation using optimal resources has also been implemented using IBM quantum computer for m = 1 and the obtained result is compared with the result of Yu et al.

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Optimization of Circuits for IBM's Five-Qubit Quantum Computers

Cited by 30 publications

References 18 publications

Quantum Computing Aided Machine Learning Through Quantum State Fidelity

Quantum Computing Aided Machine Learning Through Quantum State Fidelity

Nuwa: A Quantum Circuit Transpiler Based on a Finite-Horizon Heuristic for Placement and Routing

Comment on "Multi-output quantum teleportation of different quantum information with an IBM quantum experience"

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