Efficient mapping of quantum circuits to the IBM QX architectures

Zulehner, Alwin; Paler, Alexandru; Wille, Robert

doi:10.23919/date.2018.8342181

Cited by 77 publications

(86 citation statements)

References 13 publications

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“…cx q[0], q [1]; is not permitted. 19 [1]; such that the Hadamard gates reverse the direction of the CNOT gate. 19 More broadly, the only qubit connected to all other qubits on the ibmqx4 system, which was used in this study, is q 2 .…”

Section: Quantum Circuit Constructionmentioning

confidence: 99%

“…19 [1]; such that the Hadamard gates reverse the direction of the CNOT gate. 19 More broadly, the only qubit connected to all other qubits on the ibmqx4 system, which was used in this study, is q 2 . As such, to minimize the depth of each quantum circuit and thus the risk of errors, q 2 may be used to represent the origin node in each circuit.…”

Section: Quantum Circuit Constructionmentioning

confidence: 99%

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Modeling viral diffusion using quantum computational network simulation

Britt

2020

Quantum Engineering

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Summary Processes of viral diffusion are important in biological, technological, and social systems alike. Several mathematical models of infection have been developed to predict diffusion through networks, such as nonlinear dynamical systems (NLDSs). Such models generally offer accurate representations of real‐world diffusion, particularly for networks with static topologies. However, simulations of viral diffusion are computationally expensive, rendering them infeasible for large‐scale networks. Here, a new approach is shown that leverages quantum computing to make viral diffusion simulations feasible for large networks, independent of network topology. Simulations of an error‐free quantum circuit accurately modeled viral diffusion, with multivariate Euclidean distances from predicted infection probabilities capped near 8% for a network with N = 5 nodes and t = 20 time‐steps. This is sufficient accuracy to distinguish the relative susceptibility of nodes and to identify significant changes, such as periods of especially high susceptibility. The results illustrate the potential for quantum computational network simulation to provide accurate models of diffusion through large networks, an important real‐world application of quantum computing. The ability to simulate viral diffusion is invaluable for researchers across disciplines who aim to understand, anticipate, prepare for, and intervene in ongoing diffusion processes.

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Section: Quantum Circuit Constructionmentioning

confidence: 99%

Section: Quantum Circuit Constructionmentioning

confidence: 99%

Modeling viral diffusion using quantum computational network simulation

Britt

2020

Quantum Engineering

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“…Previous works (e.g. [2] or [6]) did not specifically address the initial placement problem. Nevertheless, one of the latest works where the importance of the problem is mentioned but not tackled is [7].…”

Section: Previous Workmentioning

confidence: 99%

“…1. Two architectures of IBM quantum machines where qubits are drawn as vertices and supported CNOT gates as arrows between qubit pairs: a) QX3 cannot interact through CNOT qubit pairs (5,6) and (2,15); b) QX5 has a grid like structure. In both diagrams the arrow indicate the direction of the supported CNOT: the target qubit is indicated by the tip of the arrow.…”

Section: Introductionmentioning

confidence: 99%

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On the Influence of Initial Qubit Placement During NISQ Circuit Compilation

Paler

2019

Quantum Technology and Optimization Problems

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Noisy Intermediate-Scale Quantum (NISQ) machines are not fault-tolerant, operate few qubits (currently, less than hundred), but are capable of executing interesting computations. Above the quantum supremacy threshold (approx. 60 qubits), NISQ machines are expected to be more powerful than existing classical computers. One of the most stringent problems is that computations (expressed as quantum circuits) have to be adapted (compiled) to the NISQ hardware, because the hardware does not support arbitrary interactions between the qubits. This procedure introduces additional gates (e.g. SWAP gates) into the circuits while leaving the implemented computations unchanged. Each additional gate increases the failure rate of the adapted (compiled) circuits, because the hardware and the circuits are not fault-tolerant. It is reasonable to expect that the placement influences the number of additionally introduced gates. Therefore, a combinatorial problem arises: how are circuit qubits allocated (placed) initially to the hardware qubits? The novelty of this work relies on the methodology used to investigate the influence of the initial placement. To this end, we introduce a novel heuristic and cost model to estimate the number of gates necessary to adapt a circuit to a given NISQ architecture. We implement the heuristic (source code available on github) and benchmark it using a standard compiler (e.g. from IBM Qiskit) treated as a black box. Preliminary results indicate that cost reductions of up to 10% can be achieved for practical circuit instances on realistic NISQ architectures only by placing qubits differently than default (trivial placement).

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