1.0 The goal of this research The goal of the PQLG group is to develop complete methodologies, software tools and circuits for quantum logic. Our interests are mainly in logic synthesis for quantum circuits and quantum system design [10]. Emulation of quantum circuits using standard reconfigurable FPGA technology and FPGA-based Evolvable Quantum Hardware, proposed here, are research areas not yet dealt with by other research groups. A parallel software simulator was presented in [13]. 1.1 Quantum Logic and Gates Unlike classical bits that are realized as electrical voltages or currents present on a wire, quantum logic operations manipulate qubits [12]. Qubits are
1.0 The goal of this research The goal of the PQLG group is to develop complete methodologies, software tools and circuits for quantum logic. Our interests are mainly in logic synthesis for quantum circuits and quantum system design [10]. Emulation of quantum circuits using standard reconfigurable FPGA technology and FPGA-based Evolvable Quantum Hardware, proposed here, are research areas not yet dealt with by other research groups. A parallel software simulator was presented in [13]. 1.1 Quantum Logic and Gates Unlike classical bits that are realized as electrical voltages or currents present on a wire, quantum logic operations manipulate qubits [12]. Qubits are
It can be shown that if quantum algorithms run on quantum computers, their processing speeds improve exponentially compared to their classical counterparts. However, due to the lack of quantum computers circuit model of quantum algorithms are currently simulated using classical computers to verify their functionalities. On the other hand, software simulation cannot use the intrinsic parallelism of quantum algorithm efficiently. To address the problem, in this paper hardware emulation of quantum algorithms are discussed. To emulate quantum algorithms using FPGAs, a new representation for quantum bits is emulated that improves the emulation of quantum circuits considerably. This representation could be used in both distinct and entangle qubit states.
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 Atrix 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.
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 Atrix 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.
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.
Current approaches for emulation of quantum algorithms on classical parallel hardware like FPGA suffer with three main issues: exponential usage of logic elements, synthesis takes exponential time and difficulty of implementation. In this paper we propose a processor architecture capable of efficiently emulate quantum circuits, reducing the mishaps of previous similar proposals. We compare our results to previous works with respect to a common benchmark and validate the architecture using FPGA. Our approach has better scalability in logic cell usage, a better management of the need of re-synthesis and easier implementation.
Field Programmable Gate Arrays (FPGAs) have been used in most of the physics sub-fields for various unique purposes. This includes particle physics, quantum optics, and, more recently, quantum computing. FPGAs boast many benefits over previous experimental and computational setups. They are versatile, easy to program, and cost-effective, leading to an understandable desire to incorporate them into the new field of quantum computing. While FPGAs have been used to help control the readout and control of physical qubits, they can also be a good tool for improving algorithm simulations, which is the focus of this paper. Different algorithms have different computational limits because of the varying operations and circuit depth levels. Due to the ease of incorporating parallel processes and the ability to optimize FPGAs on a deeper level, we can speed up computationally expensive operations, increasing the size of the quantum circuits we can simulate. This paper aims to provide an educational walkthrough for quantum computing physicists interested in taking advantage of FPGAs in their research. Our findings indicate that an inexpensive FPGA with a clock rate of 100MHz can perform simple, unoptimized matrix operations on the order of 107 ns faster than a CPU using optimized Python libraries. This can be further improved upon using more expensive hardware and optimized designs. This will provide many researchers and students with the tools needed to study quantum algorithms without the need for costly hardware.