By modeling quantum chaotic dynamics with ensembles of random operators, we explore how machine learning algorithms can be used to detect pseudorandom behavior in qubit systems. We analyze samples consisting of pieces of correlation functions and find that machine learning algorithms are capable of determining the degree of pseudorandomness which a system is subject to in a precise sense. This is done without computing any correlators explicitly. Interestingly, even samples drawn from two-point functions are found to be sufficient to solve this classification problem. This presents the possibility of using deep learning algorithms to explore late time behavior in chaotic quantum systems which have been inaccessible to simulation.
By modeling quantum chaotic dynamics with ensembles of random operators, we explore how machine learning algorithms can be used to detect pseudorandom behavior in qubit systems. We analyze samples consisting of pieces of correlation functions and find that machine learning algorithms are capable of determining the degree of pseudorandomness which a system is subject to in a precise sense. This is done without computing any correlators explicitly. Interestingly, even samples drawn from two-point functions are found to be sufficient to solve this classification problem. This presents the possibility of using deep learning algorithms to explore late time behavior in chaotic quantum systems which have been inaccessible to simulation.
By modeling quantum chaotic dynamics with ensembles of random operators, we explore how a deep learning architecture known as a convolutional neural network (CNN) can be used to detect pseudorandom behavior in qubit systems. We analyze samples consisting of pieces of correlation functions and find that a CNN is capable of determining the degree of pseudorandomness which a system is subject to. This is done without computing any correlators explicitly. Interestingly, even samples drawn from two-point functions are found to be sufficient to solve this classification problem. This presents the possibility of using deep learning algorithms to explore late time behavior in chaotic quantum systems which have been inaccessible to simulation.
Understanding properties of quantum matter is an outstanding challenge in science. In this paper, we demonstrate how machine-learning methods can be successfully applied for the classification of various regimes in single-particle and many-body systems. We realize neural network algorithms that perform a classification between regular and chaotic behavior in quantum billiard models with remarkably high accuracy. We use the variational autoencoder for autosupervised classification of regular/chaotic wave functions, as well as demonstrating that autoencoders could be used as a tool for detection of anomalous quantum states, such as quantum scars. By taking this method further, we show that machine-learning techniques allow us to pin down the transition from integrability to many-body quantum chaos in Heisenberg XXZ spin chains. For both cases, we confirm the existence of universal W shapes that characterize the transition. Our results pave the way for exploring the power of machine-learning tools for revealing exotic phenomena in quantum many-body systems.
Quantum chaos is central to understanding quantum dynamics and is crucial for generating random quantum states, a key resource for quantum information tasks. In this work, we introduce a new class of quantum many-body dynamics, termed pseudochaotic dynamics. Although distinct from chaotic dynamics, out-of-time-ordered correlators, the key indicators of quantum chaos, fail to distinguish them. Moreover, pseudochaotic dynamics generates pseudorandom states that are computationally indistinguishable from Haar-random states. We construct pseudochaotic dynamics by embedding a smaller k-qubit subsystem into a larger n-qubit system. We demonstrate that a subsystem of size k=ω(logn) is sufficient to induce pseudochaotic behavior in the entire n-qubit system. Furthermore, we construct a quantum circuit exhibiting pseudochaotic dynamics and demonstrate that it generates pseudorandom states within polylog(n) depth. In summary, our results constitute the discovery of new quantum dynamics that are computationally indistinguishable from genuine quantum chaos, which provides efficient routes to generate useful pseudorandom states. Recent work introduced pseudorandom quantum states, indistinguishable from truly random states by any polynomial-time algorithm. Here the authors propose pseudochaotic many-body dynamics, which is indistinguishable from chaotic dynamics and can generate pseudorandom states using polylogarithmic-depth circuits.
Using quantum algorithms to improve deep learning models' capabilities is becoming increasingly popular as quantum computing develops. In this work, we investigate how quantum algorithms using quantum neural networks (QNNs) might enhance the effectiveness and performance of deep learning models. We examine the effects of quantum-inspired methods on tasks, including regression, sorting, and optimization, by thoroughly analyzing quantum algorithms and how they integrate with deep learning systems. We experiment with Estimator QNN and Sampler QNN implementations using Qiskit machine-learning, analyzing their forward and backward pass outcomes to assess the effectiveness of quantum algorithms in improving deep learning models. Our research clarifies the scope, intricacy, and scalability issues surrounding QNNs and offers insights into the possible advantages and difficulties of quantum-enhanced deep learning. This work adds to the continuing investigation of quantum computing's potential to advance machine learning and artificial intelligence paradigms by clarifying the interaction between quantum algorithms and deep learning systems.
Randomness is a scarce and precious resource: cryptography, scientific simulation, gambling regulation, and even foundational physics all depend on being able to produce numbers that no one — not even the generator itself — could have predicted in advance. Classical computers cannot create such numbers on their own, because they are deterministic machines; every pseudorandom number generator (PRNG) is, in principle, a fixed algorithm whose entire output is fixed the moment a seed is chosen. Quantum random number generators (QRNGs) promise something categorically different: unpredictability guaranteed by the indeterminism of quantum measurement itself. This paper examines whether “true” randomness is achievable at all, surveys the physical arguments (rooted in Bell's theorem) that quantum measurement outcomes are not merely unknown but undetermined, and then asks a narrower, more computational question: can a machine-learning model tell the difference between the output of a well-designed PRNG and genuine entropy? We built and tested a small experimental pipeline comparing a deliberately flawed linear-congruential generator, a mildly weak variant, two industry-standard PRNGs (Mersenne Twister and PCG64), and operating-system entropy (as an accessible proxy for physically-sourced randomness). Gradient-boosted and logistic-regression classifiers detected the flawed generator with 100% accuracy but could not beat chance (49–51%) against modern PRNGs, mirroring published results in the cryptographic and quantum-optics literature. We conclude that while true randomness is very plausibly achievable through quantum processes certified by Bell-inequality violation, no black-box statistical or machine-learning test — however sophisticated — can, on principle, certify the difference between a good PRNG and true randomness from output alone; the distinction is one of provenance and physical mechanism, not detectable pattern.
This work focuses on quantum machine learning and analyzes the power of one single qubit to solve classical machine learning problems. We explore possible strategies to address traditional supervised, unsupervised, and reinforcement learning tasks. In particular, we study binary and multinomial classification, regression problems, time series forecasting, clustering, and quantum reinforcement learning. Our results suggest that the same methodology could be used to address all three types of learning with different measurement strategies and, despite the strong limitation of reduced data dimensionality of the candidate problems, a single qubit can achieve similar or even improved performance with respect to state-of-the-art classic machine learning methods in many cases. As simulating the evolution of one qubit state is computationally efficient, our study enables the possibility to use the qubit model as a candidate solution to implement simple decision-making machine learning models in hardware with extremely low memory resources such as embedded systems or edge devices. The outcomes provided could be also of interest in academia and for the construction of demonstrators using low-scale contemporary quantum hardware.