Evaluating the Flexibility of A* for Mapping Quantum Circuits

Zulehner, Alwin; Bauer, Hartwig; Wille, Robert

doi:10.1007/978-3-030-21500-2_11

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…The conventional methods do not perform two process in a single step, so we combined the best method in each field, the work by Miller et al [5] and one by Zulehner et al [20], for comparison. It decomposes the MPMCT gates using the best decomposition forms [5] from Fig.…”

Section: Resultsmentioning

confidence: 99%

“…However, unlike our proposed method, it randomly selects an ancillary bit and randomly divides the control bits of an MPMCT gate into two groups. For the SWAP gates, we implemented a method of inserting SWAP gates into a quantum circuit from left to right by using the A* algorithm based on the work by Zulehner et al [20]. We think comparison with this combined method can demonstrate the effectiveness of our method.…”

Section: Resultsmentioning

confidence: 99%

“…We use the A* algorithm to efficiently insert SWAP gates for determining the qubit placement that satisfies the desired adjacent relation graph for a template. The A* algorithm is a search algorithm that has been widely utilized for finding an efficient way to insert SWAP gates [19,20].…”

Section: Inserting Swap Gates By the A* Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Method to Decompose and Map MPMCT Gates That Accounts for Qubit Placement

Matsuo

Hattori

Yamashita

2023

IEICE Trans. Fundamentals

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Mixed-Polarity Multiple-Control Toffoli (MPMCT) gates are generally used to implement large control logic functions for quantum computation. A logic circuit consisting of MPMCT gates needs to be mapped to a quantum computing device that invariably has a physical limitation, which means we need to (1) decompose the MPMCT gates into one-or two-qubit gates, and then (2) insert SWAP gates so that all the gates can be performed on Nearest Neighbor Architectures (NNAs). Up to date, the above two processes have only been studied independently. In this work, we investigate that the total number of gates in a circuit can be decreased if the above two processes are considered simultaneously as a single step. We developed a method that inserts SWAP gates while decomposing MPMCT gates unlike most of the existing methods. Also, we consider the effect on the latter part of a circuit carefully by considering the qubit placement when decomposing an MPMCT gate. Experimental results demonstrate the effectiveness of our method.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Method to Decompose and Map MPMCT Gates That Accounts for Qubit Placement

Matsuo

Hattori

Yamashita

2023

IEICE Trans. Fundamentals

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“…Machine learning techniques have started being applied to quantum circuit compilation and optimisation (e.g. [3][4][5]). The general approach is to invest large amounts of computational power into the training of models that can then be used for fast and efficient quantum circuit compilation.…”

Section: A Motivationmentioning

confidence: 99%

Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation

Moflic¹,

Garg²

2023

Preprint

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Reinforcement learning (RL) is a promising method for quantum circuit optimisation. However, the state space that has to be explored by an RL agent is extremely large when considering all the possibilities in which a quantum circuit can be transformed through local rewrite operations. This state space explosion slows down the learning of RL-based optimisation strategies. We present for the first time how to use graph neural network (GNN) autoencoders for the optimisation of quantum circuits. We construct directed acyclic graphs from the quantum circuits, encode the graphs and use the encodings to represent RL states. We illustrate our proof of concept implementation on Bernstein-Vazirani circuits and, from preliminary results, we conclude that our autoencoder approach: a) maintains the optimality of the original RL method; b) reduces by 20 % the size of the table that encodes the learned optimisation strategy. Our method is the first realistic first step towards very large scale RL quantum circuit optimisation.

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“…The nearest neighbor constraints have been considered in proposals for a range of potential technological realizations of quantum computers such as ion traps [4,32,38], nitrogen-vacancy centers in diamonds [38,55], quantum dots emitting linear cluster states linked by linear optics [10,21], laser manipulated quantum dots in a cavity [26] and superconducting qubits [13,40,34]. They are also considered in realizations of specific types of circuits and architectures, such as surface codes [49], Shor's algorithm [15], the Quantum Fourier Transform (QFT) [48], circuits for modular multiplication and exponentiation [35], quantum adders on the 2D NTC architecture [8], factoring [42], fault-tolerant circuits [33], error correction [16], and more recently, IBM QX architectures [50,56,57,14].…”

Section: Introductionmentioning

confidence: 99%

A polynomial size model with implicit SWAP gate counting for exact qubit reordering

Mulderij¹,

Aardal²,

Chiscop³

et al. 2020

Preprint

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Due to the physics behind quantum computing, quantum circuit designers must adhere to the constraints posed by the limited interaction distance of qubits. Existing circuits need therefore to be modified via the insertion of SWAP gates, which alter the qubit order by interchanging the location of two qubits' quantum states. We consider the Nearest Neighbor Compliance problem on a linear array, where the number of required SWAP gates is to be minimized. We introduce an Integer Linear Programming model of the problem of which the size scales polynomially in the number of qubits and gates. Furthermore, we solve 131 benchmark instances to optimality using the commercial solver CPLEX. The benchmark instances are substantially larger in comparison to those evaluated with exact methods before. The largest circuits contain up to 18 qubits or over 100 quantum gates. This formulation also seems to be suitable for developing heuristic methods since (near) optimal solutions are discovered quickly in the search process.

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Evaluating the Flexibility of A* for Mapping Quantum Circuits

Cited by 11 publications

References 23 publications

An Efficient Method to Decompose and Map MPMCT Gates That Accounts for Qubit Placement

An Efficient Method to Decompose and Map MPMCT Gates That Accounts for Qubit Placement

Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation

A polynomial size model with implicit SWAP gate counting for exact qubit reordering

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