arXiv Machine Learning

Resource-Efficient QUBO Formulation for Anchored Currency Arbitrage

arXiv:2608. 15889v1 Announce Type: cross Abstract: Currency arbitrage (CA) involves trading currencies in cycles to exploit discrepancies in market valuations.

arXiv AI
Jul 7

COMET: Combinatorial Optimization for Multiplex Editing Targets Via Constraint-Preserving QAOA

arXiv:2607. 02622v1 Announce Type: cross Abstract: Multiplex CRISPR-Cas9 gene editing requires selecting one guide RNA per target gene subject to cross-gene interactions: a constrained combinatorial problem that can be formulated as a Quadratic Unconstrained Binary Optimization (QUBO) and solved via the Quantum Approximate Optimization Algorithm (QAOA).

By Priyansh Singhal, Sumit Maheshwari, Piyush Joshi
arXiv AI
Jun 17

PIVOT: Bridging Black-Scholes Implied-Volatility and Price Objectives via Differentiable J\"ackel Operator

arXiv:2606. 17065v1 Announce Type: cross Abstract: Modern option-learning systems operate in two coordinates: price space, where markets quote and no-arbitrage constraints are most naturally enforced, and implied volatility (IV) space, where volatility surfaces are smoothed, regularized, and evaluated.

By Raeid Saqur, Yannick Limmer, Anastasis Kratsios, Blanka Horvath, Hans Buehler
arXiv Machine Learning
Jun 9

Adaptive directional gradients for parameterised quantum circuits

arXiv:2606. 09734v1 Announce Type: cross Abstract: Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale.

By Brian Coyle, Snehal Raj, Virag Umathe, El Amine Cherrat, Elham Kashefi
arXiv AI
6d ago

Error-Aware Reverse Auction Mechanism for Large Language Model Routing

arXiv:2608. 12719v1 Announce Type: cross Abstract: Routing each query to a cost-effective large language model (LLM) is critical for balancing quality and cost, yet most routers rely on a centralized task center to predict model performance, creating an information-risk mismatch and a scalability bottleneck as the model pool grows.

By Haolong Chen, Zhengyuan Xin, Liang Zhang, Lei Xue, Guangxu Zhu