Integrated photonic circuits play a crucial role in implementing quantum information processing in the noisy intermediate-scale quantum (NISQ) era. Variational learning is a promising avenue that leverages classical optimization techniques to enhance quantum advantages on NISQ devices. However, most variational algorithms are circuit-model-based and encounter challenges when implemented on integrated photonic circuits, because they involve explicit decomposition of large quantum circuits into sequences of basic entangled gates, leading to an exponential decay of success probability due to the non-deterministic nature of photonic entangling gates. Here, we present a variational learning approach for designing quantum photonic circuits, which directly incorporates post-selection and elementary photonic elements into the training process. The complicated circuit is treated as a single nonlinear logical operator, and a unified design is discovered for it through variational learning. Engineering an integrated photonic chip with automated control, we adjust and optimize the internal parameters of the chip in real time for task-specific cost functions. We utilize a simple case of designing photonic circuits for a single ancilla CNOT gate with improved success rate to illustrate how our proposed approach works, and then apply the approach in the first demonstration of quantum stochastic simulation using integrated photonics.
Integrated photonic circuits play a crucial role in implementing quantum information processing in the noisy intermediate-scale quantum (NISQ) era. Variational learning is a promising avenue that leverages classical optimization techniques to enhance quantum advantages on NISQ devices. However, most variational algorithms are circuit-model-based and encounter challenges when implemented on integrated photonic circuits, because they involve explicit decomposition of large quantum circuits into sequences of basic entangled gates, leading to an exponential decay of success probability due to the non-deterministic nature of photonic entangling gates. Here, we present a variational learning approach for designing quantum photonic circuits, which directly incorporates post-selection and elementary photonic elements into the training process. The complicated circuit is treated as a single nonlinear logical operator, and a unified design is discovered for it through variational learning. Engineering an integrated photonic chip with automated control, we adjust and optimize the internal parameters of the chip in real time for task-specific cost functions. We utilize a simple case of designing photonic circuits for a single ancilla CNOT gate with improved success rate to illustrate how our proposed approach works, and then apply the approach in the first demonstration of quantum stochastic simulation using integrated photonics.
Abstract Integrated photonic circuits play a crucial role in implementing quantum information processing in the noisy intermediate‐scale quantum (NISQ) era. Variational learning is a promising avenue that leverages classical optimization techniques to enhance quantum advantages on NISQ devices. However, most variational algorithms are circuit‐model‐based and encounter challenges when implemented on integrated photonic circuits, because they involve explicit decomposition of large quantum circuits into sequences of basic entangled gates, leading to an exponential decay of success probability due to the non‐deterministic nature of photonic entangling gates. Here, a variational learning approach is presented for designing quantum photonic circuits, which directly incorporates post‐selection and elementary photonic components into the training process. The complicated circuit is treated as a single nonlinear logical operator and a unified design is discovered for it through variational learning. Engineering an integrated photonic chip with automated control achieved by genetic algorithm, the internal parameters of the chip are adjusted and optimized in real‐time for task‐specific cost functions. A simple case of designing photonic circuits for a single ancilla CNOT gate with improved success rate is utilized to illustrate how the proposed approach works, and then the approach is applied to the first demonstration of quantum stochastic simulation using integrated photonics.
Quantum embedding is a key technique in quantum machine learning, mapping classical data into a high-dimensional Hilbert space through parameterized quantum circuits to enhance data separability. While effective on general quantum devices, its implementation on photonic platforms faces severe challenges, as entangling gates rely on post-selection and lead to exponentially decreasing success probabilities when cascaded. To address this, we propose a quantum embedding learning approach tailored for photonic quantum circuits, which incorporates multi-photon post-selection into a variational ansatz, enabling the efficient generation of entanglement while maintaining circuit scalability on NISQ devices. By training the transfer matrices, our method achieves the desired photon distribution probabilities and input-output correspondence. We demonstrate its versatility through three tasks: Bell-state projection, quantum support vector machines (qSVM), and quantum clustering of K-means. The experimental results indicate enhanced performance in state discrimination, classification, and clustering, showing that the method provides a practical route to implement quantum embedding in integrated photonic circuits.
In the era of noisy intermediate-scale quantum computers, variational quantum algorithms are promising approaches for solving optimization tasks by training parameterized quantum circuits with the aid of classical routines informed by quantum measurements. In this context, photonic platforms based on reconfigurable integrated optics are an ideal candidate for implementing these algorithms. Among various techniques to train variational circuits, the parameter shift rule enables the exact calculation of cost function derivatives efficiently, facilitating gradient descent-based optimization. In this paper, we derive a formulation of the parameter shift rule for computing derivatives and integrals tailored to reconfigurable optical linear circuits and based on the Boson Sampling paradigm. This allows us to naturally embed common types of experimental noise, such as partial distinguishability and mixedness of the states, thus obtaining a resilient approach. Finally, we employ the developed approach to experimentally test variational algorithms with single-photon states processed in a reconfigurable six-mode universal integrated interferometer. Specifically, we apply the photonic parameter shift rules to the variational implementation, on a photonic platform, of both an eigensolver and a Universal- gate.
Implementing quantum computers using linear photonics is currently constrained by the low success probabilities of CNOT gates. While the KLM protocol has shown efficient scalability, it still incurs cost in terms of the number of modes and the use of error correcting codes. Variational quantum circuits have shown intrinsic error mitigation properties, however, this generally requires post-processing, essentially requiring extra circuit executions, hurting execution times. Therefore, it is now typical to augment variational circuits with error-correcting codes to mitigate error for all quantum implementation technologies. This paper studies the error mitigation effect of variational quantum classifiers on probabilistic quantum photonics CNOT gates with no extra executions. We have observed that the gate failure noise does not affect all prepared states equally. Moreover, the corresponding error-immune states still provide a high level of expressibility and entangling capability, indicating the possibility for training to achieve error mitigation effects. Moreover, we also observed another subspace of the parameter space in which error can be suppressed through parameter corrections without the need for post-processing. The paper also conducts a photonics simulation study for a range of probabilistic CNOT gates for a simple binary classification problem and studies the effect of circuit hyperparameters on the error mitigation. The simulations show acceptable accuracy results on the learning tasks, showing the possible utility of linear photonics in quantum learning classification tasks.
Hybridizing machine learning (ML) with quantum computing has become an attractive strategy to increase computational power on Noisy Intermediate Scale Quantum (NISQ) devices. Although these can be potentially quantum advantage devices, their qubit numbers and noise are bounded and there are challenges when it comes to practical quantum circuit realization. This work concentrates on the creation of optimized quantum circuits via ML-based strategies, to enhance the performance of the circuits, to reduce the error rate and to accommodate the particular noise profile of the hardware of NISQ devices. The resulting suboptimal mappings with little or no consideration of noise and hardware topology in traditional quantum compilers can ruin the fidelity of quantum algorithm. Leveraging machine learning, and specifically reinforcement and supervised learning methods, our method learns to optimize compilation strategy for specific quantum hardware architecture. The paper proposes a framework that uses machine learning models to predict qubit mappings, gate decompositions, and transpilation paths that minimize gate depth and error accumulation. We also investigate transfer learning methods for propagating the learned compilation strategies to novel NISQ machines. Such methods are empirically validated on both simulated and real quantum hardware backends, providing evidence of circuit depth, execution time and error reductions. The results show that ML-assisted compilation can achieve better success probabilities for benchmark quantum algorithms like VQE and QAOA than conventional compilers. Furthermore, the work examines the interpretability of ML models providing hardware-aware optimization recommendations and understanding the noise patterns and methods of error correction. Our work highlights the key role of ML for transferring between theoretical quantum algorithms and their practical NISQ implementations. This integration boosts the immediate applicability of existing quantum devices and provides a basis for adaptive and scalable likelihood-based compilation for the era of fault-tolerant quantum computation. Our results motivate further investigation of hybrid classical-quantum workflows, in which ML iteratively improves compilation in accordance with changing hardware noise and capabilities. By improving quantum circuit compilation onNISQ computers, this work is taking steps toward the realization of quantum advantage in application scenarios such as optimization, cryptography, and quantum simulation without needing to wait for fully fault-tolerant large-scale quantum computers.
The use of Variational Quantum Circuits (VQCs), which are an important type of model in Quantum Machine Learning (QML) that will be used with Basic Quantum Computers during their Noisy intermediate-scale quantum (NISQ) era, is very dependent on the properties of the circuits themselves (architectures) and how they are trained (training settings). In fact, this model is so dependent on the knowledge of the human designer that creating these models takes significant time and energy to develop properly through qualitatively high amounts of quantum knowledge. In this work, we propose a new method for automatically building and training quantum machine learning models using an AutoML-Assisted approach to optimize VQCs. The overall goal of the AutoML-Assisted approach is to treat the design of quantum circuits as a learning problem and utilize Neural Architecture Search ( NAS ) to search end to end on the architecture, variational parameters, and hyperparameters automatically. Our experimental results on classification tasks show that the automatically learned VQCs not only give better accuracy but also give faster convergence and generally are more robust to quantum noise than warehouse VQCs developed by humans. This work makes a direct contribution to increasing the automation and scalability by which quantum machine learning can be run on NISQ computers.