Spiking neuromorphic chip learns entangled quantum states
Stefanie Czischek, Andreas Baumbach, Sebastian Billaudelle, Benjamin Cramer, Lukas Kades, Jan Martin Pawlowski, Markus K. Oberthaler, Johannes Schemmel, Mihai Alexandru Petrovici, Thomas Gasenzer, Martin Gärttner
The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years. Meanwhile, analog neuromorphic chips, inspired by structural and dynamical properties of the biological brain, show a high energy efficiency in running artificial neural-network architectures for the profit of generative applications. This encourages employing such hardware systems as platforms for simulations of quantum systems. Here we report on the realization of a prototype using the latest spike-based BrainScaleS hardware allowing us to represent few-qubit maximally entangled quantum states with high fidelities. Bell correlations of pure and mixed two-qubit states are well captured by the analog hardware, demonstrating an important building block for simulating quantum systems with spiking neuromorphic chips.
The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years. Meanwhile, analog neuromorphic chips, inspired by structural and dynamical properties of the biological brain, show a high energy efficiency in running artificial neural-network architectures for the profit of generative applications. This encourages employing such hardware systems as platforms for simulations of quantum systems. Here we report on the realization of a prototype using the latest spike-based BrainScaleS hardware allowing us to represent few-qubit maximally entangled quantum states with high fidelities. Bell correlations of pure and mixed two-qubit states are well captured by the analog hardware, demonstrating an important building block for simulating quantum systems with spiking neuromorphic chips.
Emergent phenomena in condensed matter physics, such as superconductivity, are rooted in the interaction of many quantum particles. These phenomena remain poorly understood in part due to the computational demands of their simulation. In recent years variational representations based on artificial neural networks, so called neural quantum states (NQS), have been shown to be efficient, ie. sub-exponentially scaling, representations. However, the computational complexity of such representations scales not only with the size of the physical system, but also with the size of the neural network. In this work, we use the analog neuromorphic BrainScaleS-2 platform to implement probabilistic representations of two particular types of quantum states. The physical nature of the neuromorphic system enforces an inherent parallelism of the compuation, rendering the emulation time independent of the used network size. We show the effectiveness of our scheme in two settings: First, we consider a hallmark test for ”quantumness” by representing a quantum state that violates the classical bounds of the Bell inequality. Second, we show that we can represent the large class of stoquastic quantum states with fidelities above 98% for moderate system sizes. This offers a novel application for spike-based neuromorphic hardware which departs from the more traditional neuroscience-inspired use cases.
This paper explores a novel approach to neuromorphic computing by leveraging the unique properties of quantum entanglement. The core idea is to utilize multi-particle quantum entanglement states as the physical connections between artificial neurons, enabling efficient parallel information processing and mimicking biological neural networks. We propose a quantum neuromorphic chip architecture employing multiple superconducting quantum bits (Qubits) engineered to exhibit entanglement. The design focuses on manipulating the coherence time, topological structure, and combined operation of quantum gates to emulate synaptic transmission and learning within biological neural networks. Specifically, we investigate encoding information through entanglement and explore parallel computation facilitated by this quantum phenomenon. Furthermore, we address the challenges of quantum computation by integrating quantum decoherence and quantum error correction techniques. The potential for achieving high-performance, energy-efficient, and biologically inspired computing is discussed, representing a significant departure from traditional approaches and classical neural networks. The proposed framework offers a pathway towards scalable quantum neuromorphic systems with potential applications in areas such as pattern recognition, machine learning, and complex system modeling.
Abstract Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processing. To improve their efficiency, spiking architectures often run on custom-designed neuromorphic hardware, but, despite their attractive properties, these implementations have been limited to digital systems. We describe an artificial quantum spiking neuron that relies on the dynamical evolution of two easy to implement Hamiltonians and subsequent local measurements. The architecture allows exploiting complex amplitudes and back-action from measurements to influence the input. This approach to learning protocols is advantageous in the case where the input and output of the system are both quantum states. We demonstrate this through the classification of Bell pairs which can be seen as a certification protocol. Stacking the introduced elementary building blocks into larger networks combines the spatiotemporal features of a spiking neural network with the non-local quantum correlations across the graph.
Recent research has demonstrated the usefulness of neural networks as variational ansatz functions for quantum many-body states. However, high-dimensional sampling spaces and transient autocorrelations confront these approaches with a challenging computational bottleneck. Compared to conventional neural networks, physical model devices offer a fast, efficient and inherently parallel substrate capable of related forms of Markov chain Monte Carlo sampling. Here, we demonstrate the ability of a neuromorphic chip to represent the ground states of quantum spin models by variational energy minimization. We develop a training algorithm and apply it to the transverse field Ising model, showing good performance at moderate system sizes ( N ≤ 10 ). A systematic hyperparameter study shows that performance depends on sample quality, which is limited by temporal parameter variations on the analog neuromorphic chip. Our work thus provides an important step towards harnessing the capabilities of neuromorphic hardware for tackling the curse of dimensionality in quantum many-body problems.
Digital neuromorphic systems seek to replicate the organizational principles and dynamics of biological neural networks in silicon, offering massive parallelism, event‐driven processing, and orders-of-magnitude lower energy consumption compared with conventional von Neumann computers. Concurrently, quantum artificial intelligence (AI) methods exploit quantum superposition and entanglement to accelerate optimization and pattern-recognition tasks. However, quantum hardware today remains constrained by qubit count and noise, while neuromorphic chips are limited in their ability to discover global optima during training. In this work, we propose and evaluate a hybrid framework—“quantum-optimized neuromorphic inference”—in which a variational quantum circuit (VQC) tunes synaptic weight matrices for a spiking neural network (SNN), and the optimized SNN is deployed on a digital neuromorphic core for inference. We first survey the relevant neuromorphic platforms and quantum machine learning algorithms, then describe our methodology for weight optimization and mapping to neuromorphic hardware. Through extensive simulations on a spoken-digit classification benchmark, our hybrid approach achieves a 3.3% absolute improvement in accuracy over a conventionally trained SNN, while reducing energy per inference by 3.5%. We discuss implications for scalable brain emulation, energy-efficient AI, and future neuromorphic-quantum co-design, and outline next steps toward experimental validation on emerging quantum processors and multi-core neuromorphic systems.
Recent research has demonstrated the usefulness of neural networks as variational ansatz functions for quantum many-body states. However, high-dimensional sampling spaces and transient autocorrelations confront these approaches with a challenging computational bottleneck. Compared to conventional neural networks, physical-model devices offer a fast, efficient and inherently parallel substrate capable of related forms of Markov chain Monte Carlo sampling. Here, we demonstrate the ability of a neuromorphic chip to represent the ground states of quantum spin models by variational energy minimization. We develop a training algorithm and apply it to the transverse field Ising model, showing good performance at moderate system sizes (N≤10). A systematic hyperparameter study shows that scalability to larger system sizes mainly depends on sample quality, which is limited by temporal parameter variations on the analog neuromorphic chip. Our work thus provides an important step towards harnessing the capabilities of neuromorphic hardware for tackling the curse of dimensionality in quantum many-body problems.