The introduction of quantum computing has led to an immense increase in performance of various search and encryption algorithms. However the practical realization of quantum computers is1a challenging task. Hence quantum algorithms are being modeled on classical platforms. Hardware emulation of quantum computers has been found to be more effective than software simulations. In this paper, Grover's search algorithm has been modeled in the classical platform using both hardware emulation and software simulation approaches. The modified architecture of the field programmable gate arrays (FPGAs) and the unique data path implemented in this paper results in considerable speed-up and lesser utilization of resources as compared to other existing works. This work includes both simulation and emulation of Grover’s search for 2,3 and 5 qubit systems. The novel emulation approach has led to a 104times reduction in time of search than the corresponding simulation.
The introduction of quantum computing has led to an immense increase in performance of various search and encryption algorithms. However the practical realization of quantum computers is1a challenging task. Hence quantum algorithms are being modeled on classical platforms. Hardware emulation of quantum computers has been found to be more effective than software simulations. In this paper, Grover's search algorithm has been modeled in the classical platform using both hardware emulation and software simulation approaches. The modified architecture of the field programmable gate arrays (FPGAs) and the unique data path implemented in this paper results in considerable speed-up and lesser utilization of resources as compared to other existing works. This work includes both simulation and emulation of Grover’s search for 2,3 and 5 qubit systems. The novel emulation approach has led to a 104times reduction in time of search than the corresponding simulation.
Hardware emulation of quantum systems can mimic more efficiently the parallel behaviour of quantum computations, thus allowing higher processing speed-up than software simulations. In this paper, an efficient hardware emulation method that employs a serial-parallel hardware architecture targeted for field programmable gate array (FPGA) is proposed. Quantum Fourier transform and Grover’s search are chosen as case studies in this work since they are the core of many useful quantum algorithms. Experimental work shows that, with the proposed emulation architecture, a linear reduction in resource utilization is attained against the pipeline implementations proposed in prior works. The proposed work contributes to the formulation of a proof-of-concept baseline FPGA emulation framework with optimization on datapath designs that can be extended to emulate practical large-scale quantum circuits.
Quantum algorithms can be efficiently emulated on classical hardware such as field programmable gate arrays (FPGAs), achieving significant speedup over software simulations. However, the increase in the required hardware resources for emulating quantum systems becomes a critical limitation as the number of qubits is increased. In this paper, we propose a scalable emulation framework for modeling quantum algorithms on FPGAs that employs efficient resource scheduling such as space and space-time scheduling. In addition, full floating point precision arithmetic and dataflow non-linear (dynamic) pipelining are also used to achieve higher accuracy and higher throughput. We propose scalable and optimized hardware architectures for Quantum Fourier Transform (QFT) and Grover's search algorithm and demonstrate the scalability of our framework by scaling the system up to 5 fully-entangled qubits. A multi-node (multi-FPGA), state-of-the-art high-performance reconfigurable computer (HPRC) was used for implementation of the proposed architectures. Our experimental results show that by employing efficient resource scheduling techniques, the hardware resource constraints could be mitigated and the proposed emulation framework could be made feasible for emulation of more complex, larger-scale quantum algorithms while maintaining higher accuracy and throughput than existing work.
Quantum computing (QC) leverages superposition, entanglement, and parallelism to solve complex problems that are challenging for classical computing methods. The immense potential of QC has spurred explosive interest and research in both academia and industry. However, the practicality of QC based on large-scale quantum computers remains limited by issues of scalability and error correction. To bridge this gap, QC emulators utilizing classical computing resources have emerged, with modern implementations employing FPGAs for efficiency. Nevertheless, FPGA-based QC emulators face significant limitations, particularly in standalone implementations required for low-power, low-performance devices like IoT end nodes, embedded systems, and wearable devices, due to their substantial resource demands. This paper proposes optimization techniques to reduce resource requirements and enable standalone FPGA implementations of QC emulators. We specifically focused on Grover's algorithm, known for its excellent performance in searching unstructured databases. The proposed resource-saving optimization techniques allow for the emulation of the largest possible Grover's algorithm within the constrained resources of FPGAs. Using these optimization techniques, we developed a hardware accelerator for Grover's algorithm and integrated it with a RISC-V processor architecture. We completed a standalone Grover's algorithm-specific emulator operating on FPGAs, demonstrating significant performance enhancements and resource savings afforded by the proposed techniques.
As current trend of miniaturization in computing technology continues, modern computing devices would start to exhibit the behaviour of nanoscopic quantum objects. Quantum computing, which is based on the principles of quantum mechanics, becomes a promising candidate for future generation computing system. However, modelling quantum computing systems on existing classical computing platforms before the realization of viable large-scale quantum computer remains a major challenge. The exploration on the modelling of quantum computing systems on field programmable gate array (FPGA) platform, which offers the potential of massive parallelism and allows computational optimization at register-transfer level, is crucial. Due to the exponential growth of resource utilization with the increase in the number of quantum bits (qubit), existing works on modelling of quantum systems on FPGA platform are restricted to simple case studies using small qubit sizes. This work explores the modelling of quantum computing for emulation on FPGA platform based on two types of data structure: (a) state vector model and (b) Heisenberg model. For the conventional state vector modelling approach, an efficient datapath design that is based on serial-parallel hardware architecture, which allows resource sharing between unitary transformations, is proposed. Heisenberg model has been proven to be efficient in modelling stabilizer circuits, which are critical in error correction operations. In the effort to include the consideration of vital quantum error correction in practical quantum systems, a novel FPGA emulation framework that is based on the Heisenberg model is proposed. Effective algorithms for accurate global phase maintenance are proposed to facilitate the modelling of quantum systems based on the Heisenberg representation. The feasibility of the proposed state vector and Heisenberg emulation models are demonstrated based on a number of case studies with different characteristics, which include quantum Fourier transform, Grover’s search algorithm, and stabilizer circuits. Based on the state vector approach, this work has demonstrated the advantage of FPGA emulation over software simulation where hardware emulation of 7-qubit Grover’s search is about 3 × 104 times faster than the software simulation performed on Intel Core i7-4790 processor running at 3.6GHz clock rate. In contrast to the 8-qubit implementation based on the state vector model, the proposed FPGA emulation framework based on the Heisenberg model has successfully modelled 120-qubit stabilizer circuits on the Altera Stratix IV FPGA. In summary, the proposed work in this thesis contributes to the formulation of a proof-of-concept of efficient FPGA emulation framework based on the state vector and Heisenberg models.
Quantum hardware emulators using field programmable gate arrays (FPGAs) have been demonstrated to efficiently exploit the inherent parallelism of quantum algorithms and perform better than sequential software simulators. However, one key problem in mapping quantum circuits on FPGAs is the exponential increase in resource requirements with the increase in the number of quantum bits (qubits). As a result, the precision of hardware emulators is often reduced to save resources and accommodate larger quantum circuits which results in lower accuracy and lower throughput. In this paper, we present a full precision FPGA quantum emulator that uses 32-bit floating-point arithmetic and has a fully pipelined architecture. Our emulator is capable of emulating an entangled 4-qubit quantum system performing Quantum Fourier Transform, Grover's Search algorithms on a single FPGA node. Our experimental work was conducted on a highly scalable multi-node (multi-FPGA) state-of-the-art high-performance reconfigurable computer (HPRC). Experimental results show that our emulator is functionally accurate and has a higher operating frequency relative to other emulators. For emulating larger-scale circuits we propose using a multi-node architecture scheme with low latency, high bandwidth inter-node communication.
Quantum computing leverages quantum mechanics to perform complex computations efficiently, yet its practical implementation is hindered by hardware-software interdependence and quantum state instability. This paper advances lightweight FPGA-based quantum emulation by incorporating a universal quantum gate set, including parameterized Phase gates and the CNOT gate, to support comprehensive quantum protocol emulation on FPGA hardware. The FPGA platform used for synthesis is Artix-7. The proposed emulator is validated through simulations of quantum circuits, including Bell States, the Deutsch algorithm, Superdense coding, Grover's search, and BB84 QKD, with results benchmarked against IBM's noiseless quantum simulator. Utilizing FPGA's customizable parallelism and energy efficiency, this framework offers a resource-efficient, reliable platform for developing and testing quantum algorithms.