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arXiv:2407.01010 · Machine Learning Science and Technology

Multi-target quantum compilation algorithm

Vu Tuan Hai, Nguyen Tan Viet, Jesus Urbaneja, Nguyen Vu Linh, Lan Nguyen Tran, Le Bin Ho

Published Nov 25, 2024• 2 citations
arXivHTMLPDF

Abstract

Abstract Quantum compilation is the process of converting a target unitary operation into a trainable unitary represented by a quantum circuit. It has a wide range of applications, including gate optimization, quantum-assisted compiling, quantum state preparation, and quantum dynamic simulation. Traditional quantum compilation usually optimizes circuits for a single target. However, many quantum systems require simultaneous optimization of multiple targets, such as thermal state preparation, time-dependent dynamic simulation, and others. To address this, we develop a multi-target quantum compilation algorithm to improve the performance and flexibility of simulating multiple quantum systems. Our benchmarks and case studies demonstrate the effectiveness of the algorithm, highlighting the importance of multi-target optimization in advancing quantum computing. This work lays the groundwork for further development and evaluation of multi-target quantum compilation algorithms.

DOI
10.1088/2632-2153/ad9705

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Abstract Quantum compilation is the process of converting a target unitary operation into a trainable unitary represented by a quantum circuit. It has a wide range of applications, including gate optimization, quantum-assisted compiling, quantum state preparation, and quantum dynamic simulation. Traditional quantum compilation usually optimizes circuits for a single target. However, many quantum systems require simultaneous optimization of multiple targets, such as thermal state preparation, time-dependent dynamic simulation, and others. To address this, we develop a multi-target quantum compilation algorithm to improve the performance and flexibility of simulating multiple quantum systems. Our benchmarks and case studies demonstrate the effectiveness of the algorithm, highlighting the importance of multi-target optimization in advancing quantum computing. This work lays the groundwork for further development and evaluation of multi-target quantum compilation algorithms.
Abstract In quantum computing, quantum compilation transforms a target unitary into a trainable unitary represented by a quantum circuit. Traditional quantum compilation optimizes circuits for a single target. However, many quantum systems require simultaneous optimization of multiple targets. To address this, we developed a multi-target quantum compilation algorithm to enhance the performance and flexibility of simulating multiple quantum systems. Our benchmarks and case studies demonstrate the algorithm’s effectiveness, highlighting the significance of multi-target optimization in advancing quantum computing. This work establishes the foundation for further development and evaluation of multi-target quantum compilation algorithms.
Quantum circuit compilation is a critical step in realizing quantum algorithms on real quantum hardware. Traditional compilation methods often employ static optimization strategies, neglecting the unique constraints and characteristics of the target quantum device. This paper introduces a novel adaptive quantum circuit compilation framework designed to dynamically adjust circuit designs based on hardware-specific information, aiming to minimize gate count and, consequently, error rates. The core of the framework lies in integrating a hardware simulator directly within the compilation process. This allows the compiler to evaluate the performance of various circuit designs—considering factors like qubit connectivity, gate fidelity, and decoherence models—before committing to a final layout. The proposed approach represents a shift from static to adaptive optimization, providing a more nuanced and effective strategy for translating quantum algorithms into executable code on diverse quantum hardware platforms. This research demonstrates a significant potential improvement in quantum circuit efficiency and fidelity, addressing a key bottleneck in the advancement of quantum computing.
This paper presents a novel compilation optimization technique specifically designed for dynamic variable subcircuits in quantum computing. The core claim is to establish a method for adaptive circuit restructuring based on the inherent dynamic variability of quantum circuits, ultimately improving resource utilization. The proposed mechanism leverages the quantum decoherence effect, predicting circuit evolution and dynamically reconfiguring the circuit based on these predictions. This is achieved through the integration of a quantum decoherence model and optimization algorithms. Traditional quantum circuit compilation methods are static, lacking the ability to adapt to the evolving dynamics of a quantum system. Our approach offers a significant advancement by providing a self-optimizing system, leading to enhanced efficiency in quantum computation. The key formulas and notations used throughout this paper are as follows: Q is the set of qubits; C is the set of quantum gates; |ψ⟩ represents a quantum state vector; H⊗ represents the tensor product operation; Ui represents a unitary operator; Tr(A) denotes the trace of a matrix A; ⟨ψ|Φ⟩ represents the inner product between two quantum states; Δt is the decoherence time; γ is the decoherence rate; η represents the optimization step size; ||f|| represents the norm of a function f. The proposed technique addresses a critical limitation in current quantum compilation strategies and paves the way for more efficient and adaptable quantum algorithms.
Near-term quantum computers have significant error rates and short coherence times, so compilation of circuits to be as short as possible is essential. Two types of compilation problems are typically considered: circuits to prepare a given state from a fixed input state, called “state preparation”; and circuits to implement a given unitary operation, for example by “unitary synthesis”. In this paper we solve a more general problem: the transformation of a set of m states to another set ofmmmstates, which we call “multi-state preparation”. State preparation and unitary synthesis are special cases; for state preparation, m=1, while for unitary synthesis, m is the dimension of the full Hilbert space. We generate and optimize circuits for multi-state preparation numerically. In cases where a top-down approach based on matrix decompositions is also possible, our method finds circuits with substantially (up to 40 %) fewer two-qubit gates. We discuss possible applications, including efficient preparation of macroscopic superposition (“cat”) states and synthesis of quantum channels.
Quantum circuit compilation is a critical step in the process of translating high-level quantum algorithms into executable instructions on quantum hardware. Traditional compilation techniques often rely on deterministic models of gate operations and measurement outcomes, which can lead to suboptimal results and inaccurate performance estimates. This paper introduces a novel dynamic programming approach to quantum circuit compilation that explicitly addresses the inherent uncertainty associated with qubit measurements and gate operations. The algorithm utilizes probabilistic models to represent the likelihood of measurement outcomes and gate errors, optimizing the circuit design for the *expected* performance, considering the statistical nature of quantum computation. The core contribution lies in shifting the focus from worst-case scenarios to expected outcomes, leading to more realistic and potentially more efficient quantum circuit designs. The methodology provides a framework for incorporating uncertainty directly into the compilation process, a crucial step towards achieving robust and reliable quantum computation.
Quantum circuit compilation is a crucial step in transforming a high-level quantum algorithm description into a sequence of gates executable on a specific quantum hardware platform. Traditional compilation methods predominantly focus on minimizing gate count and circuit depth, often overlooking the significant impact of quantum coherence on the overall fidelity of the computation. This paper introduces an adaptive quantum circuit compilation algorithm designed to mitigate this limitation. The algorithm dynamically analyzes the coherence properties of the quantum circuit during the compilation process, aiming to preserve coherence while simultaneously minimizing detrimental decoherence effects. This approach represents a fundamental shift in the compilation paradigm, directly addressing the critical challenge of coherence maintenance in quantum computing. The proposed methodology utilizes a novel metric for coherence analysis and employs an adaptive optimization strategy to achieve superior circuit fidelity compared to conventional compilation techniques. The effectiveness of the algorithm is demonstrated through theoretical analysis and a detailed explanation of its components, paving the way for more robust and accurate quantum circuit implementations.