arXiv subjects

Mert Esencan

Publications and source records attributed to Mert Esencan.

4 recordsLinked to original sources

arXiv2026

Time Crystals as Passively Protected Oscillating Qubits

Protecting information against decoherence in open quantum systems remains a central challenge for quantum computing. In particular, passive error correction schemes have so far been limited to static memories rather than dynamical qubits. We demonstrate that a driven-dissipative bosonic system can encode a persistently oscillating qubit within a noiseless subsystem, realized explicitly in the Bose-Hubbard dimer (BHD). The strong parity symmetry of the model leads to degenerate stationary states. This symmetry is further broken into non-stationary states in the thermodynamic limit, which exhibit persistent oscillations. As the driving force increases, the Liouvillian spectrum of these states features a phase transition. Above the transition point, the non-stationary state encodes quantum information, preserving it in a noiseless subsystem. In addition to global loss that affects both bosonic modes identically, we further add global dephasing and show that the oscillating qubit is preserved. Finally, in order to gain additional physical insight, we study the effect of phase perturbation to both modes and observe that likewise they are passively protected, returning approximately to their initial configurations. These results establish dissipative time-crystalline dynamics as a mechanism for passive protection of dynamical quantum information, enabling autonomously stabilized oscillating qubits.

quant-ph↗

arXiv2024

Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization

Recent Large Language Models (LLMs) have demonstrated impressive capabilities at tasks that require human intelligence and are a significant step towards human-like artificial intelligence (AI). Yet the performance of LLMs at reasoning tasks have been subpar and the reasoning capability of LLMs is a matter of significant debate. While it has been shown that the choice of the prompting technique to the LLM can alter its performance on a multitude of tasks, including reasoning, the best performing techniques require human-made prompts with the knowledge of the tasks at hand. We introduce a framework for what we call Combinatorial Reasoning (CR), a fully-automated prompting method, where reasons are sampled from an LLM pipeline and mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem. The framework investigates whether QUBO solutions can be profitably used to select a useful subset of the reasons to construct a Chain-of-Thought style prompt. We explore the acceleration of CR with specialized solvers. We also investigate the performance of simpler zero-shot strategies such as linear majority rule or random selection of reasons. Our preliminary study indicates that coupling a combinatorial solver to generative AI pipelines is an interesting avenue for AI reasoning and elucidates design principles for future CR methods.

cs.AI↗

arXiv2022

Generative Quantum Learning of Joint Probability Distribution Functions

Modeling joint probability distributions is an important task in a wide variety of fields. One popular technique for this employs a family of multivariate distributions with uniform marginals called copulas. While the theory of modeling joint distributions via copulas is well understood, it gets practically challenging to accurately model real data with many variables. In this work, we design quantum machine learning algorithms to model copulas. We show that any copula can be naturally mapped to a multipartite maximally entangled state. A variational ansatz we christen as a `qopula' creates arbitrary correlations between variables while maintaining the copula structure starting from a set of Bell pairs for two variables, or GHZ states for multiple variables. As an application, we train a Quantum Generative Adversarial Network (QGAN) and a Quantum Circuit Born Machine (QCBM) using this variational ansatz to generate samples from joint distributions of two variables for historical data from the stock market. We demonstrate our generative learning algorithms on trapped ion quantum computers from IonQ for up to 8 qubits and show that our results outperform those obtained through equivalent classical generative learning. Further, we present theoretical arguments for exponential advantage in our model's expressivity over classical models based on communication and computational complexity arguments.

quant-ph↗

arXiv2022

Polynomial unconstrained binary optimisation inspired by optical simulation

We propose an algorithm inspired by optical coherent Ising machines to solve the problem of polynomial unconstrained binary optimization (PUBO). We benchmark the proposed algorithm against existing PUBO algorithms on the extended Sherrington-Kirkpatrick model and random third-degree polynomial pseudo-Boolean functions, and observe its superior performance. We also address instances of practically relevant computational problems such as protein folding and electronic structure calculations with problem sizes not accessible to existing quantum annealing devices. The application of our algorithm to protein folding and quantum chemistry problems sheds light on the shortcomings of approximating the electronic structure problem by a PUBO problem, which, in turn, puts into question the applicability of the unconstrained binary optimization formulation, such as that of quantum annealers and coherent Ising machines, in this context.

quant-ph↗