Inferring physical laws by artificial intelligence based causal models

The advances in Artificial Intelligence (AI) and Machine Learning (ML) have opened up many avenues for scientific research, and are adding new dimensions to the process of knowledge creation. However, even the most powerful and versatile of ML applications till date are primarily in the domain of analysis of associations and boil down to complex data fitting. Judea Pearl has pointed out that Artificial General Intelligence must involve interventions involving the acts of doing and imagining. Any machine assisted scientific discovery thus must include casual analysis and interventions. In this context, we propose a causal learning model of physical principles, which not only recognizes correlations but also brings out casual relationships. We use the principles of causal inference and interventions to study the cause-and-effect relationships in the context of some well-known physical phenomena. We show that this technique can not only figure out associations among data, but is also able to correctly ascertain the cause-and-effect relations amongst the variables, thereby strengthening (or weakening) our confidence in the proposed model of the underlying physical process.Comment: Latex 12 pages, 16 figure

Non-classical Correlations in n-Cycle Setting

We explore non-classical correlations in n-cycle setting. In particular, we focus on correlations manifested by Kochen-Specker-Klyachko box (KS box), scenarios involving n-cycle non-contextuality inequalities and Popescu-Rohlrich boxes (PR box). We provide the criteria for optimal classical simulation of a KS box of arbitrary n dimension. The non-contextuality inequalities are analysed for n-cycle setting, and the condition for the quantum violation for odd as well as even n-cycle is discussed. We offer a simple extension of even cycle non-contextuality inequalities to the continuous variable case. Furthermore, we simulate a generalized PR box using KS box and provide some interesting insights. Towards the end, we discuss a few possible interesting open problems for future research.Comment: 12 page

Pseudorandom unitaries are neither real nor sparse nor noise-robust

Pseudorandom quantum states (PRSs) and pseudorandom unitaries (PRUs) possess the dual nature of being efficiently constructible while appearing completely random to any efficient quantum algorithm. In this study, we establish fundamental bounds on pseudorandomness. We show that PRSs and PRUs exist only when the probability that an error occurs is negligible, ruling out their generation on noisy intermediate-scale and early fault-tolerant quantum computers. Additionally, we derive lower bounds on the imaginarity and coherence of PRSs and PRUs, rule out the existence of sparse or real PRUs, and show that PRUs are more difficult to generate than PRSs. Our work also establishes rigorous bounds on the efficiency of property testing, demonstrating the exponential complexity in distinguishing real quantum states from imaginary ones, in contrast to the efficient measurability of unitary imaginarity. Furthermore, we prove lower bounds on the testing of coherence. Lastly, we show that the transformation from a complex to a real model of quantum computation is inefficient, in contrast to the reverse process, which is efficient. Overall, our results establish fundamental limits on property testing and provide valuable insights into quantum pseudorandomness.Comment: 23 pages, 3 figure

NISQ algorithm for the matrix elements of a generic observable

The calculation of off-diagonal matrix elements has various applications in fields such as nuclear physics and quantum chemistry. In this paper, we present a noisy intermediate scale quantum algorithm for estimating the diagonal and off-diagonal matrix elements of a generic observable in the energy eigenbasis of a given Hamiltonian. Several numerical simulations indicate that this approach can find many of the matrix elements even when the trial functions are randomly initialized across a wide range of parameter values without, at the same time, the need to prepare the energy eigenstates