Automatic Uniform Quantum State Preparation Using Decision Diagrams

Mozafari, Fereshte; Soeken, Mathias; Riener, Heinz; Micheli, Giovanni De

doi:10.1109/ismvl49045.2020.00-10

Cited by 8 publications

(11 citation statements)

References 19 publications

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“…Moreover, the results show that we can reduce the number of elementary quantum gates over QisKit by removing redundancies in the BDD. The detailed results are available in [17].…”

Section: B Using Decision Diagrammentioning

confidence: 99%

“…We use truth-table-based, and decision-diagram-based representations for smaller, and larger numbers of qubits, respectively. In [17], we showed how we use decision diagrams to prepare UQSs. Decision diagrams enable a scalable quantum state preparation since many Boolean functions of practical interest have small representations, e.g., in terms of Binary Decision Diagrams (BDDs) [18].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, whereas [17] targets runtime reduction using BDDs as a symbolic representation of Boolean functions, we present here an orthogonal improvement that identifies dependencies among variables of the Boolean functions and determines an order in which the qubits should be prepared. We show that, if a functional dependency can be identified, the decomposition algorithm can be avoided in favor of more efficient quantum circuit constructions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Boolean Methods for Preparing Uniform Quantum States

Mozafari

Riener

Soeken

et al. 2021

IEEE Trans. Quantum Eng.

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“…Moreover, the results show that we can reduce the number of elementary quantum gates over QisKit by removing redundancies in the BDD. The detailed results are available in [17].…”

Section: B Using Decision Diagrammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Boolean Methods for Preparing Uniform Quantum States

Mozafari

Riener

Soeken

et al. 2021

IEEE Trans. Quantum Eng.

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show abstract

“…In contrast to general quantum states, in most quantum computational tasks, the states to prepare are from subfamilies of n-qubit states, such as uniform quantum states [20,21], Dicke states [22], and cyclic quantum states [23]. In these examples, all state subfamilies have classical descriptions with symmetric structure, which hints to the possibility of utilizing structured classical descriptions of quantum states to achieve efficient QSP.…”

Section: Introductionmentioning

confidence: 99%

Efficient Deterministic Preparation of Quantum States Using Decision Diagrams

Mozafari,

De Micheli,

Yang

2022

Preprint

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Loading classical data into quantum registers is one of the most important primitives of quantum computing. While the complexity of preparing a generic quantum state is exponential in the number of qubits, in many practical tasks the state to prepare has a certain structure that allows for faster preparation. In this paper, we consider quantum states that can be efficiently represented by (reduced) decision diagrams, a versatile data structure for the representation and analysis of Boolean functions. We design an algorithm that utilises the structure of decision diagrams to prepare their associated quantum states. Our algorithm has a circuit complexity that is linear in the number of paths in the decision diagram. Numerical experiments show that our algorithm reduces the circuit complexity by up to 31.85% compared to the state-of-the-art algorithm, when preparing generic n-qubit states with different degrees of non-zero amplitudes. Additionally, for states with sparse decision diagrams, including the initial state of the quantum Byzantine agreement protocol, our algorithm reduces the number of CNOTs by 86.61% ∼ 99.9%.

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“…Currently, there are many ways to prepare the superposition of quantum states for the quantum Monte Carlo algorithm. New methods have been created for specific distribution functions [14] [15]. Others involve local measurement [16] and loading vectors first to be used for state preparation [17].…”

mentioning

confidence: 99%