Publications

@article{vakili2024quantum,

title = {Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS},

author = {Mohammad Ghazi Vakili and Christoph Gorgulla and AkshatKumar Nigam and Dmitry Bezrukov and Daniel Varoli and Alex Aliper and Daniil Polykovsky and Krishna M Padmanabha Das and Jamie Snider and Anna Lyakisheva and others},

doi = {10.48550/arXiv.2402.08210},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {arXiv preprint arXiv:2402.08210},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{alcazar2021geob,

title = {Enhancing combinatorial optimization with classical and quantum generative models},

author = {Javier Alcazar and Mohammad Ghazi Vakili and Can B Kalayci and Alejandro Perdomo-Ortiz},

doi = {10.1038/s41467-024-46959-5},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Nature Communications},

volume = {15},

number = {1},

pages = {2761},

abstract = {Devising an efficient exploration of the search space is one of the key challenges in the design of combinatorial optimization algorithms. Here, we introduce the Generator-Enhanced Optimization (GEO) strategy: a framework that leverages any generative model (classical, quantum, or quantum-inspired) to solve optimization problems. We focus on a quantum-inspired version of GEO relying on tensor-network Born machines, and referred to hereafter as TN-GEO. To illustrate our results, we run these benchmarks in the context of the canonical cardinality-constrained portfolio optimization problem by constructing instances from the S&P 500 and several other financial stock indexes, and demonstrate how the generalization capabilities of these quantum-inspired generative models can provide real value in the context of an industrial application. We also comprehensively compare state-of-the-art algorithms and show that TN-GEO is among the best; a remarkable outcome given the solvers used in the comparison have been fine-tuned for decades in this real-world industrial application. Also, a promising step toward a practical advantage with quantum-inspired models and, subsequently, with quantum generative models},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nakaji2024generative,

title = {The generative quantum eigensolver (GQE) and its application for ground state search},

author = {Nakaji, Kouhei and Kristensen, Lasse Bjørn and Campos-Gonzalez-Angulo, Jorge A and Vakili, Mohammad Ghazi and Huang, Haozhe and Bagherimehrab, Mohsen and Gorgulla, Christoph and Wong, FuTe and McCaskey, Alex and Kim, Jin-Sung and others},

doi = {10.48550/arXiv.2401.09253},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {arXiv preprint arXiv:2401.09253},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sim_chemos_2023b,

title = {ChemOS 2.0: an orchestration architecture for chemical self-driving laboratories},

author = {Malcolm Sim and Mohammad Ghazi Vakili and Felix Strieth-Kalthoff and Han Hao and Riley Hickman and Santiago Miret and Sergio Pablo-García and Alán Aspuru-Guzik},

url = {https://chemrxiv.org/engage/chemrxiv/article-details/64cbe80adfabaf06ffa61204},

doi = {10.26434/chemrxiv-2023-v2khf},

year  = {2023},

date = {2023-08-01},

urldate = {2023-08-08},

publisher = {ChemRxiv},

abstract = {Self-driving laboratories (SDLs), which combine automated experimental hardware with computational experiment planning, have emerged as powerful tools for accelerating materials discovery. The intrinsic complexity created by their multitude of components requires an effective orchestration platform to ensure the correct operation of diverse experimental setups. Existing orchestration frameworks, however, are either tailored to specific setups or have not been implemented for real-world synthesis. To address these issues, we introduce ChemOS 2.0, an orchestration architecture that efficiently coordinates communication, data exchange, and instruction management among modular laboratory components. By treating the laboratory as an "operating system" ChemOS 2.0 combines ab-initio calculations, experimental orchestration and statistical algorithms to guide closed-loop operations. To demonstrate its capabilities, we showcase ChemOS 2.0 in a case study focused on discovering organic laser molecules. The results confirm the ChemOS 2.0's prowess in accelerating materials research and demonstrate its potential as a valuable design for future SDL platforms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cena2023assessing,

title = {Assessing the Effectiveness of Channel Hopping in IEEE 802.15. 4 TSCH Networks},

author = {Gianluca Cena and Stefano Scanzio and Mohammad Ghazi Vakili and Claudio Giovanni Demartini and Adriano Valenzano},

doi = {10.1109/OJIES.2023.3287943},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {IEEE Open Journal of the Industrial Electronics Society},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{marceddu2022air,

title = {Air-To-Ground Transmission and Near Real-Time Visualization of FBG Sensor Data Via Cloud Database},

author = {AC Marceddu and G Quattrocchi and A Aimasso and E Giusto and L Baldo and MG Vakili and MDL Dalla Vedova and B Montrucchio and P Maggiore},

url = {https://ieeexplore.ieee.org/document/9982645},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {IEEE Sensors Journal},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{alcazar2021geo,

title = {GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models},

author = {Javier Alcazar and Mohammad Ghazi Vakili and Can B Kalayci and Alejandro Perdomo-Ortiz},

url = {https://arxiv.org/abs/2101.06250},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {arXiv preprint arXiv:2101.06250},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{collotta2021fuzzy,

title = {A fuzzy control system for energy-efficient wireless devices in the Internet of vehicles},

author = {Mario Collotta and Renato Ferrero and Edoardo Giusto and Mohammad Ghazi Vakili and Jacopo Grecuccio and Xiangjie Kong and Ilsun You},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/int.22353},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {International Journal of Intelligent Systems},

volume = {36},

number = {4},

pages = {1595--1618},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{giusto2020quantum,

title = {Quantum Pliers Cutting the Blockchain},

author = {Edoardo Giusto and Mohammad Ghazi Vakili and Filippo Gandino and Claudio Demartini and Bartolomeo Montrucchio},

year  = {2020},

date = {2020-01-01},

journal = {IT Professional},

volume = {22},

number = {6},

pages = {90--96},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{montrucchio2020densely,

title = {A densely-deployed, high sampling rate, open-source air pollution monitoring WSN},

author = {Bartolomeo Montrucchio and Edoardo Giusto and Mohammad Ghazi Vakili and Stefano Quer and Renato Ferrero and Claudio Fornaro},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Transactions on Vehicular Technology},

volume = {69},

number = {12},

pages = {15786--15799},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{scanzio2020wireless,

title = {Wireless sensor networks and TSCH: A compromise between reliability, power consumption, and latency},

author = {Stefano Scanzio and Mohammad Ghazi Vakili and Gianluca Cena and Claudio Giovanni Demartini and Bartolomeo Montrucchio and Adriano Valenzano and Claudio Zunino},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Access},

volume = {8},

pages = {167042--167058},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cena2020evaluating,

title = {Evaluating and modeling IEEE 802.15. 4 TSCH resilience against Wi-Fi interference in new-generation highly-dependable wireless sensor networks},

author = {Gianluca Cena and Claudio G Demartini and Mohammad Ghazi Vakili and Stefano Scanzio and Adriano Valenzano and Claudio Zunino},

url = {https://www.sciencedirect.com/science/article/abs/pii/S1570870519310509},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Ad Hoc Networks},

volume = {106},

pages = {102199},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{khalilinezhad2013fuzzy,

title = {A Fuzzy Expert System for Assessing the InternetStores Based on Web Site Attributes},

author = {Mahdieh Khalilinezhad and Ahmad Nadali and Mohammad Ghazivakili NiloufarDehghani},

year  = {2013},

date = {2013-01-01},

keywords = {},

pubstate = {published},

tppubtype = {article}

}