Efficient full-state quantum circuit simulations are useful tools for the design of quantum algorithms. Multi-node distributed systems are commonly employed as such simulations require a large amount of computation power and memory space. In distributed systems, communication overhead can be the performance bottleneck. This paper presents a distributed simulation framework called QuanTrans. A quantum circuit is composed of many levels of quantum gates. The simulation is conducted level by level. For circuits with particular structures, it employs a hybrid simulation approach to replace intermediate multi-level communications with one level of final merge operation, whose communication volume is comparable to that of one level of simulation in previous work. A circuit without such structures is sliced to find applicable sub-circuits with a single or multiple consecutive level(s). One level of communication is required for each sub-circuit, so we further propose a polynomial-time optimal circuit slicing algorithm. It can transform any circuit such that the number of sliced sub-circuits is the minimum after transformation. Experimental results show that QuanTrans can effectively reduce communication time and simulation time.
Efficient full-state quantum circuit simulations are useful tools for the design of quantum algorithms. Multi-node distributed systems are commonly employed as such simulations require a large amount of computation power and memory space. In distributed systems, communication overhead can be the performance bottleneck. This paper presents a distributed simulation framework called QuanTrans. A quantum circuit is composed of many levels of quantum gates. The simulation is conducted level by level. For circuits with particular structures, it employs a hybrid simulation approach to replace intermediate multi-level communications with one level of final merge operation, whose communication volume is comparable to that of one level of simulation in previous work. A circuit without such structures is sliced to find applicable sub-circuits with a single or multiple consecutive level(s). One level of communication is required for each sub-circuit, so we further propose a polynomial-time optimal circuit slicing algorithm. It can transform any circuit such that the number of sliced sub-circuits is the minimum after transformation. Experimental results show that QuanTrans can effectively reduce communication time and simulation time.
Executing large quantum circuits is not feasible using the currently available NISQ (noisy intermediate-scale quantum) devices. The high costs of using real quantum devices make it further challenging to research and develop quantum algorithms. As a result, performing classical simulations is usually the preferred method for researching and validating large-scale quantum algorithms. However, these simulations require a huge amount of resources, as each additional qubit exponentially increases the computational space required. Distributed Quantum Computing (DQC) is a promising alternative to reduce the resources required for simulating large quantum algorithms at the cost of increased runtime. This study presents a comparative analysis of two simulation methods: circuit-splitting and full-circuit execution using distributed memory, each having a different type of overhead. The first method, using CutQC, cuts the circuit into smaller subcircuits and allows us to simulate a large quantum circuit on smaller machines. The second method, using Qiskit-Aer-GPU, distributes the computational space across a distributed memory system to simulate the entire quantum circuit. Results indicate that full-circuit executions are faster than circuit-splitting for simulations performed on a single node. However, circuit-splitting simulations show promising results in specific scenarios as the number of qubits is scaled.
Executing large quantum circuits is not feasible using the currently available NISQ (noisy intermediate-scale quantum) devices. The high costs of using real quantum devices make it further challenging to research and develop quantum algorithms. As a result, performing classical simulations is usually the preferred method for researching and validating large-scale quantum algorithms. However, these simulations require a huge amount of resources, as each additional qubit exponentially increases the computational space required. Distributed Quantum Computing (DQC) is a promising alternative to reduce the resources required for simulating large quantum algorithms at the cost of increased runtime. This study presents a comparative analysis of two simulation methods: circuit-splitting and full-circuit execution using distributed memory, each having a different type of overhead. The first method, using CutQC, cuts the circuit into smaller subcircuits and allows us to simulate a large quantum circuit on smaller machines. The second method, using Qiskit-Aer-GPU, distributes the computational space across a distributed memory system to simulate the entire quantum circuit. Results indicate that full-circuit executions are faster than circuit-splitting for simulations performed on a single node. However, circuit-splitting simulations show promising results in specific scenarios as the number of qubits is scaled.
In this paper we describe, implement, and test the performance of distributed memory simulations of quantum circuits on the MSU Laconia Top500 supercomputer. Using OpenMP and MPI hybrid parallelization, we first use a distributed matrix-vector multiplication with one-dimensional partitioning and discuss the shortcomings of this method due to the exponential memory requirements in simulating quantum computers. We then describe a more efficient method that stores only the 2n amplitudes of the n qubit state vector ∣ψ⟩ and optimize its single node performance. In our multi-node implementation, we use a single amplitude communication protocol that maximizes the number of qubits able to be simulated and minimizes the ratio of qubits that require communication to those that do not, and we present an algorithm for efficiently determining communication pairs among processors. We simulate up to 30 qubits on a single node and 33 qubits with the state vector partitioned across 64 nodes. Lastly, we discuss the advantages and disadvantages of our communication scheme, propose potential improvements, and describe other optimizations such as storing the state vector non-sequentially in memory to map communication requirements to idle qubits in the circuit.
Simulating the behavior of quantum circuits on classical computers is one of the widely used approaches for current quantum computing device and quantum algorithm research. State- vector simulators keep all the quantum states in main memory, thus consuming a large amount of memory and resulting in significant memory access and communication overhead far longer than calculation time. Consequently, data communication time often becomes the primary bottleneck in quantum circuit simulation. Many classical simulators, such as QuEST, are designed to execute quantum gates serially without exploiting data locality, and all amplitudes need to be exchanged. Such a gate-unaware full-data communication scheme forces powerful computation such as GPUs to spend substantial time waiting for data transfers. In this paper, we propose a gate-aware on-demand communication quantum simulation framework to optimize communication overhead. We analyze the on-demand communication process and develop a communication cost model to elucidate the relationship between communication costs and circuit partition. Since greedy circuit partition method can only yield locally optimal solutions, we propose a choice-start-node quantum circuit partition method guided by the communication cost model to further reduce communication overhead in quantum circuit simulation. We evaluate on a 4-node cluster with 2 NVIDIA A100 GPUs on each node and our design achieves 59.32× speedup compared to the gate-unaware full-data communication scheme. Furthermore, compared to the greedy circuit partition method, our partition method can achieve simulation acceleration of 1.79×.
Executing quantum algorithms over distributed quantum systems requires quantum circuits to be divided into sub-circuits which communicate via entanglement-based teleportation. Naively mapping circuits to qubits over multiple quantum processing units (QPUs) results in large communication overhead, increasing both execution time and noise. This can be minimised by optimising the assignment of qubits to QPUs and the methods used for covering non-local operations. Formulations that are general enough to capture the spectrum of teleportation possibilities lead to complex problem instances which can be difficult to solve effectively. This highlights a need to exploit the wide range of heuristic techniques used in the graph partitioning literature. This paper formalises and extends existing constructions for graphical quantum circuit partitioning and designs a new objective function that captures further possibilities for non-local operations via nested state teleportation . We adapt the well-known Fiduccia-Mattheyses heuristic to the constraints and problem objective and explore multilevel techniques that coarsen hypergraphs and partition at multiple levels of granularity. We find that this reduces runtime and improves solution quality of standard partitioning. We place these techniques within a larger framework, through which we can extract full distributed quantum circuits including teleportation instructions. We compare the entanglement requirements and runtimes with state-of-the-art methods, finding that we achieve the lowest entanglement costs in most cases. Averaging over a wide range of circuits, we reduce the entanglement requirements by 35% compared with the next best-performing method. We also find that our techniques can scale to much larger circuit sizes than competing methods, provided the number of partitions is not too large.
Large quantum circuits cannot be executed efectively on available NISQ (noisy intermediatescale quantum) devices.Moreover, due to the associated cost, researching and developing quantum algorithms on real quantum devices is not feasible.Classical simulations are, therefore, the best method to research and validate large-scale quantum algorithms.This, however, requires a large amount of resources as each additional qubit doubles the computational space for a quantum algorithm.Distributed Quantum Computing (DQC) is a promising alternative to reduce the resources required to simulate large quantum algorithms at the cost of increased runtime.This study performs a comparative analysis between two simulation methods, i.e. circuitsplitting versus full-circuit execution using distributed memory, with each method having a diferent type of overhead.The frst approach cuts the circuits into smaller subcircuits and allows us to simulate large quantum circuits using smaller machines.The second approach distributes the computational space across a distributed memory space and simulates the entire circuit.Results show that full-circuit executions are faster than circuit-splitting for simulations executed on a single node.However, circuit-splitting simulations show promising results in specifc scenarios as the number of qubits is scaled.v