Quantum computing frameworks // not completely quantum
The hybrid classical-quantum approach is a real trend until more powerful quantum chips become available (if so)
“The more qubits you try to squeeze on a chip, the worse the quality gets, and at the moment, qubit-count scaling is stalling, as chip manufacturers can’t always get a good-enough qubit quality,” QuantaMap CEO Johannes Jobst said [source]
Quantum Computing (QC) is transitioning from theoretical frameworks to an indispensable powerhouse of computational capability, resulting in extensive adoption across both industrial and academic domains. QC presents exceptional advantages, including unparalleled processing speed and the potential to solve complex problems beyond the capabilities of classical computers. Nevertheless, academic researchers and industry practitioners encounter various challenges in harnessing the benefits of this technology. The limited accessibility of QC resources for classical developers, and a general lack of domain knowledge and expertise, represent insurmountable barrier, hence to address these challenges, we introduce a framework- Quantum Computing as a Service for Hybrid Classical-Quantum Software Development (QCSHQD), which leverages service-oriented strategies. Our framework comprises three principal components: an Integrated Development Environment (IDE) for user interaction, an abstraction layer dedicated to orchestrating quantum services, and a service provider responsible for executing services on quantum computer. This study presents a blueprint for QCSHQD, designed to democratize access to QC resources for classical developers who want to seamless harness QC power. The vision of QCSHQD paves the way for groundbreaking innovations by addressing key challenges of hybridization between classical and quantum computers.
This paper presents a vision for QCSHQD (Quantum Computing as a Service for Hybrid Classical-Quantum Software Development), a framework that aims to bridge the gap between classical developers and quantum computing (QC) resources by leveraging service-oriented computing strategies. QCSHQD comprises three main components: a local Integrated Development Environment (IDE) for user interaction, an abstraction layer for orchestrating quantum services, and a service provider for executing services on quantum computers. The proposed workflow allows classical developers to invoke quantum services through the IDE, with the abstraction layer handling the translation, selection of optimal quantum computers, service deployment, and decoding of results. The motivation behind QCSHQD is to make QC resources more accessible to classical developers who lack expertise in quantum mechanics, enabling them to harness the power of QC without delving into its underlying complexities. The paper outlines the background, motivation, vision, and an implementation roadmap for QCSHQD, utilizing tools like PyDev, Eclipse, Git, OpenAPI, and TOSCA-based orchestration.
Achieving high-performance computation on quantum systems presents a formidable challenge that necessitates bridging the capabilities between quantum hardware and classical computing resources. This study introduces an innovative distribution-aware Quantum-Classical-Quantum (QCQ) architecture, which integrates cutting-edge quantum software frameworks with high-performance classical computing resources to address challenges in quantum simulation for materials and condensed matter physics, including the prediction of quantum phase transitions. At the heart of this architecture is the seamless integration of Variational Quantum Eigensolver (VQE) algorithms running on Quantum Processing Units (QPUs) for efficient quantum state preparation, Tensor Network states, and Quantum Convolutional Neural Networks (QCNNs) for classifying quantum states on classical hardware. For benchmarking quantum simulators, the QCQ architecture utilizes the cuQuantum SDK to leverage multi-GPU acceleration, integrated with PennyLane’s Lightning plugin, demonstrating up to tenfold increases in computational speed for complex phase transition classification tasks compared to traditional CPUbased methods. This significant acceleration enables models such as the transverse field Ising and XXZ systems to accurately predict phase transitions with a 99.5% accuracy. The architecture’s ability to distribute computation between QPUs and classical resources addresses critical bottlenecks in quantum High-Performance Computing (HPC), paving the way for scalable quantum simulation. The QCQ framework embodies a synergistic combination of quantum algorithms, machine learning, and Quantum-HPC capabilities, enhancing its potential to provide transformative insights into the behavior of quantum systems across different scales. As quantum hardware continues to improve, this hybrid distribution-aware framework will play a crucial role in realizing the full potential of quantum computing by seamlessly integrating distributed quantum resources with the state-of-the-art classical computing infrastructure
This paper introduces a novel Quantum-Classical-Quantum (QCQ) architecture for distributed quantum computing and quantum high-performance computing (HPC) applications. The key components of the architecture are:
1. Variational Quantum Eigensolver (VQE) for efficient quantum state preparation using tensor network ansatz states.
2. Data augmentation techniques like rotations and spin flips to enhance the training dataset robustness.
3. A hybrid Quantum Convolutional Neural Network (QCNN) classifier that integrates classical convolutional layers with quantum layers for phase transition prediction.
4. GPU acceleration using NVIDIA’s cuQuantum SDK and PennyLane’s Lightning plugin for multi-GPU simulations, demonstrating up to 10x speedup compared to CPUs.
The QCQ framework achieves 99.5% accuracy in classifying quantum phase transitions for models like transverse field Ising and XXZ. It enables scalable simulations by distributing computation across limited high-fidelity QPUs and classical resources. The synergy between quantum algorithms, machine learning, and Quantum-HPC capabilities positions this architecture for transformative applications across domains like materials science, condensed matter physics, and sustainability research as quantum hardware continues improving.
Quantum comb is an essential tool for characterizing complex quantum protocols in quantum information processing. In this work, we introduce PQComb, a framework leveraging parameterized quantum circuits to explore the capabilities of quantum combs for general quantum process transformation tasks and beyond. By optimizing PQComb for time-reversal simulations of unknown unitary evolutions, we develop a simpler protocol for unknown qubit unitary inversion that reduces the ancilla qubit overhead from 6 to 3 compared to the existing method in [Yoshida, Soeda, Murao, PRL 131, 120602, 2023]. This demonstrates the utility of quantum comb structures and showcases PQComb’s potential for solving complex quantum tasks. Our results pave the way for broader PQComb applications in quantum computing and quantum information, emphasizing its versatility for tackling diverse problems in quantum machine learning.
The paper introduces a framework called “PQComb” that utilizes Parameterized Quantum Circuits (PQCs) to explore the capabilities of quantum combs for transforming quantum processes. PQComb aims to overcome challenges in compiling and training quantum combs by leveraging machine learning strategies. The key contributions include:
1. Developing PQComb as a general framework for quantum process transformation tasks, with advantages over traditional semidefinite programming (SDP) methods, such as more flexible loss functions and practical circuit implementation.
2. Applying PQComb to the task of reversing unknown qubit-unitary operations, obtaining an optimal solution that reduces the number of required ancilla qubits from 6 to 3 compared to the existing method.
3. Proposing a deterministic and exact protocol for reversing arbitrary single-qubit unitary operations using 3 ancilla qubits and 4 queries of the unitary, advancing the state-of-the-art.
4. Demonstrating the potential of PQComb in solving complex quantum tasks and its versatility for applications in quantum computing, quantum information processing, and quantum machine learning.
The paper highlights the utility of quantum comb structures and showcases PQComb’s ability to generate cutting-edge quantum protocols and algorithms, paving the way for broader applications of parameterized quantum combs across various domains.
Graph states are used to represent mathematical graphs as quantum states on quantum computers. They can be formulated through stabilizer codes or directly quantum gates and quantum states. In this paper we show that a quantum graph neural network model can be understood and realized based on graph states. We show that they can be used either as a parameterized quantum circuits to represent neural networks or as an underlying structure to construct graph neural networks on quantum computers.
