Simulation of Quantum Computers: Review and Acceleration Opportunities
arXiv:2410.12660 · doi:10.1145/3762672
Abstract
Quantum computing has the potential to revolutionize multiple fields by solving complex problems that can not be solved in reasonable time with current classical computers. Nevertheless, the development of quantum computers is still in its early stages and the available systems have still very limited resources. As such, currently, the most practical way to develop and test quantum algorithms is to use classical simulators of quantum computers. In addition, the development of new quantum computers and their components also depends on simulations. Given the characteristics of a quantum computer, their simulation is a very demanding application in terms of both computation and memory. As such, simulations do not scale well in current classical systems. Thus different optimization and approximation techniques need to be applied at different levels. This review provides an overview of the components of a quantum computer, the levels at which these components and the whole quantum computer can be simulated, and an in-depth analysis of different state-of-the-art acceleration approaches. Besides the optimizations that can be performed at the algorithmic level, this review presents the most promising hardware-aware optimizations and future directions that can be explored for improving the performance and scalability of the simulations.
References in corpus (64)
- Array Programming with NumPy
- Quantum Computing in the NISQ era and beyond
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- The density-matrix renormalization group in the age of matrix product states
- Variational Quantum Algorithms
- Quantum Simulation
- QuTiP 2: A Python framework for the dynamics of open quantum systems
- Quantum computational advantage using photons
- QuTiP: An open-source Python framework for the dynamics of open quantum systems
- A Quantum Engineer's Guide to Superconducting Qubits
- Noisy intermediate-scale quantum (NISQ) algorithms
- Quantum computational chemistry
- Improved Simulation of Stabilizer Circuits
- Superconducting Qubits: Current State of Play
- Microwave photonics with superconducting quantum circuits
- Characterizing Quantum Supremacy in Near-Term Devices
- Logical quantum processor based on reconfigurable atom arrays
- Quantum information processing with superconducting circuits: a review
- Matrix Product States and Projected Entangled Pair States: Concepts, Symmetries, and Theorems
- Quantum algorithms for quantum chemistry and quantum materials science
- Challenges and Opportunities in Quantum Machine Learning
- Quantum computing with neutral atoms
- Random Quantum Circuits
- Simulating quantum computation by contracting tensor networks
- ProjectQ: An Open Source Software Framework for Quantum Computing
- tket : A Retargetable Compiler for NISQ Devices
- Stim: a fast stabilizer circuit simulator
- Decoherence benchmarking of superconducting qubits
- Qulacs: a fast and versatile quantum circuit simulator for research purpose
- Simulation of quantum circuits by low-rank stabilizer decompositions
- QuEST and High Performance Simulation of Quantum Computers
- Massive Parallel Quantum Computer Simulator
- QuantumOptics.jl: A Julia framework for simulating open quantum systems
- Hyper-optimized tensor network contraction
- 0.5 Petabyte Simulation of a 45-Qubit Quantum Circuit
- CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations
- 64-Qubit Quantum Circuit Simulation
- Tensor Network Algorithms: a Route Map
- Full-State Quantum Circuit Simulation by Using Data Compression
- Qibo: a framework for quantum simulation with hardware acceleration
- Massively parallel quantum computer simulator, eleven years later
- Establishing the Quantum Supremacy Frontier with a 281 Pflop/s Simulation
- Neutral Atom Quantum Computing Hardware: Performance and End-User Perspective
- Intel Quantum Simulator: A cloud-ready high-performance simulator of quantum circuits
- Transmon qubit readout fidelity at the threshold for quantum error correction without a quantum-limited amplifier
- Scqubits: a Python package for superconducting qubits
- Integrated tool-set for Control, Calibration and Characterization of quantum devices applied to superconducting qubits
- Universal fidelity reduction of quantum operations from weak dissipation
- Unbiased Simulation of Near-Clifford Quantum Circuits
- Jet: Fast quantum circuit simulations with parallel task-based tensor-network contraction
- GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm
- Stabilizer Tensor Networks: universal quantum simulator on a basis of stabilizer states
- All-photonic architectural roadmap for scalable quantum computing using Greenberger-Horne-Zeilinger states
- An architecture for quantum networking of neutral atom processors
- Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures
- Quantum simulation with just-in-time compilation
- Cache Blocking Technique to Large Scale Quantum Computing Simulation on Supercomputers
- Performance Evaluation and Acceleration of the QTensor Quantum Circuit Simulator on GPUs
- Photonic Quantum Information Processing
- High-speed calibration and characterization of superconducting quantum processors without qubit reset
- Simulation of Quantum Computing on Classical Supercomputers
- Photonic Quantum Computing
- Fast simulation of planar Clifford circuits
- TensorLy-Quantum: Quantum Machine Learning with Tensor Methods