Quantum Algorithm Implementations for Beginners. - Patrick J. Coles; Stephan J. Eidenbenz; Scott Pakin; Adetokunbo Adedoyin; John Ambrosiano; Petr M. Anisimov; William Casper; Gopinath Chennupati; Carleton Coffrin; Hristo N. Djidjev; David Gunter; Satish Karra; et al. (CoRR, 2018)

Reliable numerical key rates for quantum key distribution. - Adam Winick; Norbert Lütkenhaus; Patrick J. Coles (Quantum, 2018)

Quantum-assisted quantum compiling. - Sumeet Khatri; Ryan LaRose; Alexander Poremba; Lukasz Cincio; Andrew T. Sornborger; Patrick J. Coles (Quantum, 2019)

Cost-Function-Dependent Barren Plateaus in Shallow Quantum Neural Networks. - Marco Cerezo; Akira Sone; Tyler J. Volkoff; Lukasz Cincio; Patrick J. Coles (CoRR, 2020)

Trainability of Dissipative Perceptron-Based Quantum Neural Networks. - Kunal Sharma; Marco Cerezo; Lukasz Cincio; Patrick J. Coles (CoRR, 2020)

Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets. - Kunal Sharma; Marco Cerezo; Zoë Holmes; Lukasz Cincio; Andrew T. Sornborger; Patrick J. Coles (CoRR, 2020)

Noise-Induced Barren Plateaus in Variational Quantum Algorithms. - Samson Wang; Enrico Fontana; Marco Cerezo; Kunal Sharma; Akira Sone; Lukasz Cincio; Patrick J. Coles (CoRR, 2020)

Absence of Barren Plateaus in Quantum Convolutional Neural Networks. - Arthur Pesah; Marco Cerezo; Samson Wang; Tyler J. Volkoff; Andrew T. Sornborger; Patrick J. Coles (CoRR, 2020)

Optimizing parametrized quantum circuits via noise-induced breaking of symmetries. - Enrico Fontana; Marco Cerezo; Andrew Arrasmith; Ivan Rungger; Patrick J. Coles (CoRR, 2020)

Variational Quantum Algorithms. - Marco Cerezo; Andrew Arrasmith; Ryan Babbush; Simon C. Benjamin; Suguru Endo; Keisuke Fujii 004; Jarrod R. McClean; Kosuke Mitarai; Xiao Yuan 002; Lukasz Cincio; Patrick J. Coles (CoRR, 2020)

Variational Quantum Fidelity Estimation. - Marco Cerezo; Alexander Poremba; Lukasz Cincio; Patrick J. Coles (Quantum, 2020)

An Adaptive Optimizer for Measurement-Frugal Variational Algorithms. - Jonas M. Kübler; Andrew Arrasmith; Lukasz Cincio; Patrick J. Coles (Quantum, 2020)

Connecting ansatz expressibility to gradient magnitudes and barren plateaus. - Zoë Holmes; Kunal Sharma; Marco Cerezo; Patrick J. Coles (CoRR, 2021)

Long-time simulations with high fidelity on quantum hardware. - Joe Gibbs; Kaitlin Gili; Zoë Holmes; Benjamin Commeau; Andrew Arrasmith; Lukasz Cincio; Patrick J. Coles; Andrew T. Sornborger (CoRR, 2021)

A semi-agnostic ansatz with variable structure for quantum machine learning. - Matías Bilkis; Marco Cerezo; Guillaume Verdon; Patrick J. Coles; Lukasz Cincio (CoRR, 2021)

Equivalence of quantum barren plateaus to cost concentration and narrow gorges. - Andrew Arrasmith; Zoë Holmes; Marco Cerezo; Patrick J. Coles (CoRR, 2021)

Adaptive shot allocation for fast convergence in variational quantum algorithms. - Andi Gu; Angus Lowe; Pavel A. Dub; Patrick J. Coles; Andrew Arrasmith (CoRR, 2021)

Entangled Datasets for Quantum Machine Learning. - Louis Schatzki; Andrew Arrasmith; Patrick J. Coles; Marco Cerezo (CoRR, 2021)

Generalization in quantum machine learning from few training data. - Matthias C. Caro; Hsin-Yuan Huang; Marco Cerezo; Kunal Sharma; Andrew T. Sornborger; Lukasz Cincio; Patrick J. Coles (CoRR, 2021)

Seeking quantum advantage for neural networks. - Patrick J. Coles (Nat. Comput. Sci., 2021)

Effect of barren plateaus on gradient-free optimization. - Andrew Arrasmith; Marco Cerezo; Piotr Czarnik; Lukasz Cincio; Patrick J. Coles (Quantum, 2021)

Error mitigation with Clifford quantum-circuit data. - Piotr Czarnik; Andrew Arrasmith; Patrick J. Coles; Lukasz Cincio (Quantum, 2021)

The quantum low-rank approximation problem. - Nic Ezzell; Zoë Holmes; Patrick J. Coles (CoRR, 2022)

Covariance matrix preparation for quantum principal component analysis. - Max Hunter Gordon; Marco Cerezo; Lukasz Cincio; Patrick J. Coles (CoRR, 2022)

Out-of-distribution generalization for learning quantum dynamics. - Matthias C. Caro; Hsin-Yuan Huang; Nicholas Ezzell; Joe Gibbs; Andrew T. Sornborger; Lukasz Cincio; Patrick J. Coles; Zoë Holmes (CoRR, 2022)

Dynamical simulation via quantum machine learning with provable generalization. - Joe Gibbs; Zoë Holmes; Matthias C. Caro; Nicholas Ezzell; Hsin-Yuan Huang; Lukasz Cincio; Andrew T. Sornborger; Patrick J. Coles (CoRR, 2022)

Group-Invariant Quantum Machine Learning. - Martin Larocca; Frederic Sauvage; Faris M. Sbahi; Guillaume Verdon; Patrick J. Coles; Marco Cerezo (CoRR, 2022)

Inference-Based Quantum Sensing. - Cinthia Huerta Alderete; Max Hunter Gordon; Frederic Sauvage; Akira Sone; Andrew T. Sornborger; Patrick J. Coles; Marco Cerezo (CoRR, 2022)

Practical Black Box Hamiltonian Learning. - Andi Gu; Lukasz Cincio; Patrick J. Coles (CoRR, 2022)

Representation Theory for Geometric Quantum Machine Learning. - Michael Ragone; Paolo Braccia; Quynh T. Nguyen; Louis Schatzki; Patrick J. Coles; Frederic Sauvage; Martin Larocca; Marco Cerezo (CoRR, 2022)

Theory for Equivariant Quantum Neural Networks. - Quynh T. Nguyen; Louis Schatzki; Paolo Braccia; Michael Ragone; Patrick J. Coles; Frederic Sauvage; Martin Larocca; Marco Cerezo (CoRR, 2022)

Resource frugal optimizer for quantum machine learning. - Charles Moussa; Max Hunter Gordon; Michal Baczyk; Marco Cerezo; Lukasz Cincio; Patrick J. Coles (CoRR, 2022)

Challenges and opportunities in quantum machine learning. - Marco Cerezo; Guillaume Verdon; Hsin-Yuan Huang; Lukasz Cincio; Patrick J. Coles (Nat. Comput. Sci., 2022)

Non-trivial symmetries in quantum landscapes and their resilience to quantum noise. - Enrico Fontana; Marco Cerezo; Andrew Arrasmith; Ivan Rungger; Patrick J. Coles (Quantum, 2022)

Diagnosing Barren Plateaus with Tools from Quantum Optimal Control. - Martin Larocca; Piotr Czarnik; Kunal Sharma; Gopikrishnan Muraleedharan; Patrick J. Coles; Marco Cerezo (Quantum, 2022)

Thermodynamic AI and the Fluctuation Frontier. - Patrick J. Coles; Collin Szczepanski; Denis Melanson; Kaelan Donatella; Antonio J. Martinez; Faris M. Sbahi (ICRC, 2023)

Thermodynamic AI and the fluctuation frontier. - Patrick J. Coles (CoRR, 2023)

The power and limitations of learning quantum dynamics incoherently. - Sofiène Jerbi; Joe Gibbs; Manuel S. Rudolph; Matthias C. Caro; Patrick J. Coles; Hsin-Yuan Huang; Zoë Holmes (CoRR, 2023)

Thermodynamic Linear Algebra. - Maxwell Aifer; Kaelan Donatella; Max Hunter Gordon; Thomas Ahle; Daniel Simpson; Gavin E. Crooks; Patrick J. Coles (CoRR, 2023)

Thermodynamic Matrix Exponentials and Thermodynamic Parallelism. - Samuel Duffield; Maxwell Aifer; Gavin E. Crooks; Thomas Ahle; Patrick J. Coles (CoRR, 2023)

Thermodynamic Computing System for AI Applications. - Denis Melanson; Mohammad Abu Khater; Maxwell Aifer; Kaelan Donatella; Max Hunter Gordon; Thomas Ahle; Gavin E. Crooks; Antonio J. Martinez; Faris M. Sbahi; Patrick J. Coles (CoRR, 2023)

Theory of overparametrization in quantum neural networks. - Martin Larocca; Nathan Ju; Diego García-Martín; Patrick J. Coles; Marco Cerezo (Nat. Comput. Sci., 2023)

Subtleties in the trainability of quantum machine learning models. - Supanut Thanasilp; Samson Wang; Nhat A. Nghiem; Patrick J. Coles; Marco Cerezo (Quantum Mach. Intell., 2023)

Unifying and benchmarking state-of-the-art quantum error mitigation techniques. - Daniel Bultrini; Max Hunter Gordon; Piotr Czarnik; Andrew Arrasmith; Marco Cerezo; Patrick J. Coles; Lukasz Cincio (Quantum, 2023)

The battle of clean and dirty qubits in the era of partial error correction. - Daniel Bultrini; Samson Wang; Piotr Czarnik; Max Hunter Gordon; Marco Cerezo; Patrick J. Coles; Lukasz Cincio (Quantum, 2023)

Variational Quantum Linear Solver. - Carlos Bravo-Prieto; Ryan LaRose; Marco Cerezo; Yigit Subasi; Lukasz Cincio; Patrick J. Coles (Quantum, 2023)

A semi-agnostic ansatz with variable structure for variational quantum algorithms. - Matías Bilkis; Marco Cerezo; Guillaume Verdon; Patrick J. Coles; Lukasz Cincio (Quantum Mach. Intell., 2023)

Thermodynamic Bayesian Inference. - Maxwell Aifer; Samuel Duffield; Kaelan Donatella; Denis Melanson; Phoebe Klett; Zach Belateche; Gavin E. Crooks; Antonio J. Martinez; Patrick J. Coles (ICRC, 2024)

Thermodynamic Algorithms for Quadratic Programming. - Patryk Lipka-Bartosik; Kaelan Donatella; Maxwell Aifer; Denis Melanson; Martí Perarnau-Llobet; Nicolas Brunner; Patrick J. Coles (ICRC, 2024)

Error Mitigation for Thermodynamic Computing. - Maxwell Aifer; Denis Melanson; Kaelan Donatella; Gavin E. Crooks; Thomas Ahle; Patrick J. Coles (CoRR, 2024)

A Review of Barren Plateaus in Variational Quantum Computing. - Martin Larocca; Supanut Thanasilp; Samson Wang; Kunal Sharma; Jacob D. Biamonte; Patrick J. Coles; Lukasz Cincio; Jarrod R. McClean; Zoë Holmes; Marco Cerezo (CoRR, 2024)

Thermodynamic Natural Gradient Descent. - Kaelan Donatella; Samuel Duffield; Maxwell Aifer; Denis Melanson; Gavin E. Crooks; Patrick J. Coles (CoRR, 2024)

Variational Quantum Algorithms for Semidefinite Programming. - Dhrumil Patel; Patrick J. Coles; Mark M. Wilde (Quantum, 2024)

Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms? - Samson Wang; Piotr Czarnik; Andrew Arrasmith; Marco Cerezo; Lukasz Cincio; Patrick J. Coles (Quantum, 2024)

Scalable Thermodynamic Second-order Optimization. - Kaelan Donatella; Samuel Duffield; Denis Melanson; Maxwell Aifer; Phoebe Klett; Rajath Salegame; Zach Belateche; Gavin E. Crooks; Antonio J. Martinez; Patrick J. Coles (CoRR, 2025)

Solving the compute crisis with physics-based ASICs. - Maxwell Aifer; Zach Belateche; Suraj Bramhavar; Kerem Yunus Çamsari; Patrick J. Coles; Gavin E. Crooks; Douglas J. Durian; Andrea J. Liu; Anastasia Marchenkova; Antonio J. Martinez; Peter L. McMahon; Faris M. Sbahi; et al. (CoRR, 2025)