We report our work on the Alibaba Cloud Quantum Development Platform (AC-QDP). The capability of AC-QDP's computational engine was already reported in \cite{CZH+18, ZHN+19}.In this follow-up article, we demonstrate with figures how AC-QDP helps in testing large-scale quantum algorithms (currently within the QAOA framework). We give new benchmark results on regular graphs. AC-QDP's QAOA framework can simulate thousands of qubits for up to $4$ layers. Then we discuss two interesting use cases we have implemented on the platform: 1. Optimal QAOA sequences for small-cycle free graphs; 2. Graph structure discovery.
We report our work on the Alibaba Cloud Quantum Development Platform (AC-QDP). The capability of AC-QDP's computational engine was already reported in \cite{CZH+18, ZHN+19}.In this follow-up article, we demonstrate with figures how AC-QDP helps in testing large-scale quantum algorithms (currently within the QAOA framework). We give new benchmark results on regular graphs. AC-QDP's QAOA framework can simulate thousands of qubits for up to 4 layers. Then we discuss two interesting use cases we have implemented on the platform: 1. Optimal QAOA sequences for small-cycle free graphs; 2. Graph structure discovery.
We report, in a sequence of notes, our work on the Alibaba Cloud Quantum Development Platform(AC-QDP). AC-QDP provides a set of tools for aiding the development of both quantum computing algorithms and quantum processors, and is powered by a large-scale classical simulator deployed on Alibaba Cloud. In this note, we report the computational experiments demonstrating the classical simulation capability of AC-QDP. We use as a benchmark the random quantum circuits designed for Google's Bristlecone QPU {\cite{GRCS}}. We simulate Bristlecone-70 circuits with depth 1+32+1 in 0.43 second per amplitude, using 1449 Alibaba Cloud Elastic Computing Service (ECS) instances, each with 88 Intel Xeon(Skylake) Platinum 8163 vCPU cores @ 2.5 GHz and 160 gigabytes of memory. By comparison, the previously best reported results for the same tasks are 104 and 135 seconds, using NASA's HPC Pleiades and Electra systems, respectively ({arXiv:1811.09599}). Furthermore, we report simulations of Bristlecone-70 with depth 1+36+1 and depth 1+40+1 in 5.6 and 580.7 seconds per amplitude, respectively. To the best of our knowledge, these are the first successful simulations of instances at these depths.
We report, in a sequence of notes, our work on the Alibaba Cloud Quantum Development Platform(AC-QDP). AC-QDP provides a set of tools for aiding the development of both quantum computing algorithms and quantum processors, and is powered by a large-scale classical simulator deployed on Alibaba Cloud. In this note, we report the computational experiments demonstrating the classical simulation capability of AC-QDP. We use as a benchmark the random quantum circuits designed for Google's Bristlecone QPU {\cite{GRCS}}. We simulate Bristlecone-70 circuits with depth 1+32+1 in 0.43 second per amplitude, using 1449 Alibaba Cloud Elastic Computing Service (ECS) instances, each with 88 Intel Xeon(Skylake) Platinum 8163 vCPU cores @ 2.5 GHz and 160 gigabytes of memory. By comparison, the previously best reported results for the same tasks are 104 and 135 seconds, using NASA's HPC Pleiades and Electra systems, respectively ({arXiv:1811.09599}). Furthermore, we report simulations of Bristlecone-70 with depth 1+36+1 and depth 1+40+1 in 5.6 and 580.7 seconds per amplitude, respectively. To the best of our knowledge, these are the first successful simulations of instances at these depths.
We report, in a sequence of notes, our work on the Alibaba Cloud Quantum Development Platform (AC-QDP). AC-QDP provides a set of tools for aiding the development of both quantum computing algorithms and quantum processors, and is powered by a large-scale classical simulator deployed on Alibaba Cloud. In this note, we simulate a distance-3 logical qubit encoded in the 17-qubit surface code using experimental noise parameters for transmon qubits in a planar circuit QED architecture. Our simulation features crosstalk induced by ZZ-interactions. We show that at the current-stage noise levels, crosstalk contributes significantly to the dephasing of the logical qubit. This results in a total phase-flip probability of ∼0.6%, about 60% higher than expected without considering crosstalk. This indicates that for the code considered, the current noise parameters approach, but do not yet meet, the break-even fault-tolerance regime.
Quantum Artificial Intelligence has been considered as one of transformative interdisciplinary areas that have joined quantum calculation with machine learning and optimization. The paper is a survey of the recent development in QAI and a detailed case study of the Quantum Approximate Optimization Algorithm, its mathematical formulation, operator-based modeling, and utility in practical application to combinatorial optimization. Based on the background principles of the Hilbert space theory, Hermitian operators and variational quantum circuits, the paper examines the process of encoding a classical optimization problem in a quantum Hamiltonian and the effects of circuit depth, parameterization, and graph structure on the algorithms. An example of Max-Cut case study shows how the algorithm performs on regular graph, irregular graph and weighted graph cases, showing good approximation even at small depths, and showing convergence behavior as outlined in theory. The comparative perspectives in regards to existing models of QAI further outline the advantages of QAOA with regard to interpretability and operator design, and also outline the constraints associated with noise sensitivity, measurement load, and hardware scalability. The way QAOA is related to spectral methods, variational theory, and discrete optimization are discussed as giving theoretical implications of QAOA to applied mathematics. The research finds that QAOA is a bright future of practical, yet mathematically rich, quantum computation in the near-term. The development in the future depends on the interdisciplinary cooperation, noise-resistant circuit engineering, and the development of quantum hardware to achieve the full potential of QAI in both science and engineering uses.
Quantum computing has emerged as a transformative paradigm capable of solving complex computational problems that are intractable for classical computers. Central to the advancement of this technology is the development of efficient quantum algorithms and high fidelity simulations that enable the design, testing, and optimization of quantum solutions before deployment on physical hardware. This study focuses on the design and implementation of quantum algorithms tailored for solving a variety of quantum problems, including quantum chemistry, optimization, and cryptography. Using state of the art simulation platforms such as Qiskit, Cirq, and Pennylane, algorithm performance was evaluated in terms of accuracy, execution time, scalability, and resilience to noise. Benchmarks demonstrate that algorithms such as the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) achieve significant speedups in problem specific domains, while hybrid quantum–classical methods provide robust pathways for near term quantum advantage. Simulation results reveal that algorithmic efficiency can be significantly improved through optimized circuit depth, qubit connectivity mapping, and advanced error mitigation techniques. The findings highlight the potential of simulation driven quantum algorithm development in accelerating the practical realization of quantum computing applications across disciplines such as materials discovery, molecular modeling, secure communications, and complex optimization tasks.
We implement Quantum Approximate Optimization Algorithm (QAOA) on NP-Hard problem MaxCut (for 3-chain and 4-node chain graphs, and 4-node and 6node Mobius Ladder graphs) on a quantum simulator without noise, a simulator with experimentally bench-marked noise (fake back-end), and 5-qubit and 7-qubit processors. We use the following modes of operation: QAOA parameters updated through forward pass on the quantum circuit, modelled by the noiseless simulator as well as fake back-end, and the quantum circuit for final run with updated parameters implemented on the noiseless simulator, fake back-end, and real quantum processors. While QAOA yields higher approximation ratio compared to random guess for almost all graph instances, we also conclude that given the noise in existing quantum hardware, a quantum circuit with more than two stages is not suitable for experimental implementation of QAOA currently.