A quantum algorithm emulator (QAE) consisting of a photonic metastructure is investigated in the terahertz regime to show the implementation of the Deutsch-Josza algorithm. The QAE design consists of silicon as the oracle subblock and a polyimide gradient-index (GRIN) lens as the Fourier Transform subblock. Through a machine learning approach using inverse design the geometry of the GRIN lens was optimized to enhance the interaction of incident light with the metamaterial.
A quantum algorithm emulator (QAE) consisting of a photonic metastructure is investigated in the terahertz regime to show the implementation of the Deutsch-Josza algorithm. The QAE design consists of silicon as the oracle subblock and a polyimide gradient-index (GRIN) lens as the Fourier Transform subblock. Through a machine learning approach using inverse design the geometry of the GRIN lens was optimized to enhance the interaction of incident light with the metamaterial.
An all-dielectric photonic metastructure is investigated for application as a quantum algorithm emulator (QAE) in the terahertz frequency regime; specifically, we show implementation of the Deustsh-Josza algorithm. The design for the QAE consists of a gradient-index (GRIN) lens as the Fourier transform subblock and patterned silicon as the oracle subblock. First, we detail optimization of the GRIN lens through numerical analysis. Then, we employed inverse design through a machine learning approach to further optimize the structural geometry. Through this optimization, we enhance the interaction of the incident light with the metamaterial via spectral improvements of the outgoing wave.
Photonic systems utilized as components for optical computing promise the potential of enhanced computational ability over current computer architectures. Here, an all-dielectric photonic metastructure is investigated for application as a quantum algorithm emulator (QAE) in the terahertz frequency regime; specifically, we show implementation of the Deustsh-Josza algorithm. The design for the QAE consists of a gradient-index (GRIN) lens as the Fourier transform subblock and silicon as the oracle subblock. First, we detail optimization of the metastructure through numerical analysis. Then, we employed inverse design through a machine learning approach to further optimize the structural geometry. In particular, we improved the lens thickness, in order to enhance the resulting output signal for both balanced and constant functions. We show that by optimizing the thickness of the gradient-index lens through ML, we enhance the interaction of the incident light with the metamaterial leading to a stronger focus of the outgoing wave resulting in more accurate implementation of the desired quantum algorithm in the terahertz.
During the past few years, a lot of efforts have been devoted in studying optical analog computing with artificial structures. Up to now, much of them are primarily focused on classical mathematical operations. How to use artificial structures to simulate quantum algorithm is still to be explored. In this work, an all-dielectric metamaterial-based model is proposed and realized to demonstrate the quantum Deutsch-Jozsa algorithm. The model is comprised of two cascaded functional metamaterial subblocks. The oracle subblock encodes the detecting functions (constant or balanced), onto the phase distribution of the incident wave. Then, the original Hadamard transformation is performed with a graded-index subblock. Both the numerical and experimental results indicate that the proposed metamaterials are able to simulate the Deutsch-Jozsa problem with one round operation and a single measurement of the output eletric field, where the zero (maximum) intensity at the central position results from the destructive (constructive) interference accompanying with the balance (constant) function marked by the oracle subblock. The proposed computational metamaterial is miniaturized and easy-integration for potential applications in communication, wave-based analog computing, and signal processing systems.
One of the main challenges in quantum computing is implementing algorithms within quantum optical free-space and integrated circuits. This research focuses on the quantum addition algorithm, derived from the Deutsch-Jozsa algorithm, to advance quantum computing using optical technologies. The developed quantum optical integrated circuit is built on a hybrid substrate with interconnected components on a silicon dioxide base. Qubits and their states are arranged according to the requirements of each quantum algorithm. In the Fourier transform section, qubits act as propagation modes within silicon waveguides, with emitted photons representing the Fourier transform after traveling a distance of π/2. The phase-shifting section consists of two parts: one shifts the phase of incoming photons by π, while the other modulates this shift based on the quantum full adder algorithm. The first employs a photonic crystal for short phase shifts, which are adjusted via impurities in the silicon substrate and controlled by an electro-optic circuit. Additionally, single-mode waveguides made of silicon dioxide and air serve as qubits. By coupling photon amplitudes, a comparator circuit is triggered based on the generated output. Through these integrations, the sum of two quantum numbers can be represented at the output. This underscores the importance of designing and simulating a full adder within quantum optical integrated circuits to address the complexities inherent in quantum computing.
We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum–classical hybrid simulator, where a 'quantum student' is being taught by a 'classical teacher'. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch–Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method.
Optical computing harnesses the speed of light to perform vector-matrix operations efficiently. It leverages interference, a cornerstone of quantum computing algorithms, to enable parallel computations. In this work, we interweave quantum computing with classical structured light by formulating the process of photonic matrix multiplication using quantum mechanical principles such as state superposition and subsequently demonstrate a well-known algorithm, namely, Deutsch–Jozsa’s algorithm. This is accomplished by elucidating the inherent tensor product structure within the Cartesian transverse degrees of freedom of light, which is the main resource for optical vector-matrix multiplication. To this end, we establish a discrete basis using localized Gaussian modes arranged in a lattice formation and demonstrate the operation of a Hadamard gate. Leveraging the reprogrammable and digital capabilities of spatial light modulators, coupled with Fourier transforms by lenses, our approach proves adaptable to various algorithms. Therefore, our work advances the use of structured light for quantum information processing.