arXiv:2304.09253

Towards Advantages of Parameterized Quantum Pulses

Zhiding Liang, Jinglei Cheng, Zhixin Song, Hang Ren, Rui Yang, Kecheng Liu, Peter Michael Kogge, Tongyang Li, Yongshan Ding, Yiyu Shi

Published Apr 18, 2023• 1 citation

arXiv HTML PDF

Abstract

The advantages of quantum pulses over quantum gates have attracted increasing attention from researchers. Quantum pulses offer benefits such as flexibility, high fidelity, scalability, and real-time tuning. However, while there are established workflows and processes to evaluate the performance of quantum gates, there has been limited research on profiling parameterized pulses and providing guidance for pulse circuit design. To address this gap, our study proposes a set of design spaces for parameterized pulses, evaluating these pulses based on metrics such as expressivity, entanglement capability, and effective parameter dimension. Using these design spaces, we demonstrate the advantages of parameterized pulses over gate circuits in the aspect of duration and performance at the same time thus enabling high-performance quantum computing. Our proposed design space for parameterized pulse circuits has shown promising results in quantum chemistry benchmarks.

DOI: 10.48550/arxiv.2304.09253

Related papers

Hybrid Gate-Pulse Model for Variational Quantum Algorithms

Zhiding Liang, Zhixin Song, Jinglei Cheng +8 more

Current quantum programs are mostly synthesized and compiled on the gate-level, where quantum circuits are composed of quantum gates. The gate-level workflow, however, introduces significant redundancy when quantum gates are eventually transformed into control signals and applied on quantum devices. For superconducting quantum computers, the control signals are microwave pulses. Therefore, pulse-level optimization has gained more attention from researchers due to their advantages in terms of circuit duration. Recent works, however, are limited by their poor scalability brought by the large parameter space of control signals. In addition, the lack of gate-level "knowledge" also affects the performance of pure pulse-level frameworks. We present a hybrid gate-pulse model that can mitigate these problems. We propose to use gate-level compilation and optimization for "fixed" part of the quantum circuits and to use pulse-level methods for problem-agnostic parts. Experimental results demonstrate the efficiency of the proposed framework in discrete optimization tasks. We achieve a performance boost at most 8% with 60% shorter pulse duration in the problem-agnostic layer.

arXiv:2212.00661ACM/IEEE Design Automation Conference (DAC)Jul 9, 202320 citations

Unleashing Quantum Neural Networks: Solving Parameterized Quantum Circuit Challenges

Kolla Bhanu Prakash

A possible approach to tackling computationally demanding issues beyond the capabilities of conventional algorithms is the use of quantum circuits, which provide a fundamental foundation for utilizing the power of quantum computation. This abstract examines how quantum circuits are implemented, emphasizing important topics such as circuit optimization, gate operations, and qubit manipulation. We explore different methods of encoding quantum information into topological qubits, trapped ions, and superconducting circuits, among other physical qubit formats. Furthermore, we go over the function of gate operations in quantum circuits, emphasizing crucial gates like phase, Hadamard, and CNOT gates, which serve as the foundation for quantum algorithms. Additionally, we look at methods for maximizing the efficiency and dependability of quantum processing by designing quantum circuits to minimize gate counts, depth, and error rates. To enhance the capabilities of quantum computing, this abstract presents a novel synthesis of quantum neural networks (QNNs) and quantum parameterized circuits. Through the use of parameterized quantum circuits' inherent flexibility, our approach makes it easier to design and optimize quantum circuits with flexible parameters, providing a flexible foundation for a variety of quantum algorithms. Using this amalgamation, we unleash the capacity of quantum computers to address intricate optimization and machine learning assignments with unparalleled effectiveness and expandability. Our solution bridges the gap between traditional machine learning approaches and quantum computing paradigms by enabling the smooth integration of classical data processing techniques with quantum computation. We use quantum states as data representations in our implementation of quantum neural networks, and we optimize trainable parameters to meet predetermined goals. This new paradigm opens up the investigation of the inherent features of quantum states and provides new perspectives on their processing power compared to their classical counterparts. We show, by thorough testing and analysis, how effective quantum parameterized circuits and QNNs are at solving practical problems in a variety of fields. Our work sets the ground for revolutionary breakthroughs in quantum information processing by pushing the boundaries of quantum computation and machine learning integration, offering hitherto unseen insights and solutions to challenging challenges in science, engineering, and beyond.

International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE)May 16, 20240 citations

Title: Dynamically Reconfiguring Quantum Circuit Optimization

Jincheng Zhang

Quantum computing holds immense promise for revolutionizing various fields, but the design and optimization of quantum circuits remain a significant challenge. Current methods often rely on manual tweaking and are limited by the time and expertise required. This paper introduces a novel system for dynamically reconfiguring quantum circuit parameters during runtime, driven by reinforcement learning. Our approach addresses the bottleneck in quantum circuit optimization by automating the process and providing real-time adjustments based on performance metrics. The system is designed to intelligently explore parameter space, maximizing performance while ensuring stability and minimizing resource consumption. This work presents a framework for automated circuit optimization, paving the way for more efficient and accessible quantum computing.

Sep 3, 20260 citations

Evaluation of parameterized quantum circuits: on the relation between classification accuracy, expressibility, and entangling capability

Thomas Hubregtsen, Josef Pichlmeier, Patrick Stecher +1 more

Abstract An active area of investigation in the search for quantum advantage is quantum machine learning. Quantum machine learning, and parameterized quantum circuits in a hybrid quantum-classical setup in particular, could bring advancements in accuracy by utilizing the high dimensionality of the Hilbert space as feature space. But is the ability of a quantum circuit to uniformly address the Hilbert space a good indicator of classification accuracy? In our work, we use methods and quantifications from prior art to perform a numerical study in order to evaluate the level of correlation. We find a moderate to strong correlation between the ability of the circuit to uniformly address the Hilbert space and the achieved classification accuracy for circuits that entail a single embedding layer followed by 1 or 2 circuit designs. This is based on our study encompassing 19 circuits in both 1- and 2-layer configurations, evaluated on 9 datasets of increasing difficulty. We also evaluate the correlation between entangling capability and classification accuracy in a similar setup, and find a weak correlation. Future work will focus on evaluating if this holds for different circuit designs.

arXiv:2003.09887Quantum Machine IntelligenceMar 11, 2021135 citations

Adaptive Parameterized Quantum Circuit Design

Jincheng Zhang

This paper presents a novel approach to quantum circuit design, termed adaptive parameterized quantum circuit design, which addresses the inherent challenges of decoherence and errors within quantum computing systems. The core concept revolves around dynamically adjusting circuit parameters in real-time based on continuous monitoring of the circuit's performance. Traditional quantum circuit design relies on fixed parameters, leading to suboptimal results and limited accuracy. This work introduces a framework for building a quantum circuit monitoring system that continuously assesses qubit decoherence and error rates. The system then utilizes these measurements to intelligently modify circuit parameters, striving to optimize the overall computation. The effectiveness of this approach is demonstrated through theoretical analysis and the formulation of key equations governing the adaptive process. The primary goal is to enhance the precision and efficiency of quantum computations by overcoming the limitations of static parameter settings. The proposed methodology represents a significant advancement in quantum circuit design, moving towards a more robust and adaptable quantum computing paradigm.

Sep 6, 20260 citations

Towards Verification of Dynamic Quantum Circuits.

Lukas Burgholzer, Robert Wille

Quantum computers are reaching a level where interactions between classical and quantum computations can happen in real-time. This marks the advent of a new, broader class of quantum circuits: dynamic quantum circuits. They offer a broader range of available computing primitives that lead to new challenges for design tasks such as simulation, compilation, and verification. Due to the non-unitary nature of dynamic circuit primitives, existing techniques for these tasks are no longer applicable in an out-of-the-box fashion. In this work, we discuss the resulting consequences for quantum circuit verification and present first ideas for corresponding automatic methods. More precisely, we propose two different schemes that eventually allow to treat the involved circuits as if they were not dynamic at all. By this, we provide a basis for applying existing techniques for quantum circuit verification to this broader class of circuits.

Jun 2, 20214 citations