Overview
Quantum computers promise exponential speedups for certain computational tasks, but realizing this advantage requires sophisticated algorithms, robust learning protocols, and reliable verification tools. Our research develops the software stack that unlocks quantum advantage.
We pioneered quantum neural networks and quantum generative adversarial learning, analyzed barren plateaus in variational circuits, and developed efficient quantum architecture search methods. Our work on circuit verification provides essential tools for validating quantum computations, while our complexity theory research establishes fundamental limits of quantum computation.
Research Topics
Quantum Machine Learning
Developing quantum neural networks, QGANs, and variational quantum algorithms. Our research addresses barren plateaus, trainability, and practical applications of quantum learning.
Learn MoreQuantum Algorithms
Designing quantum algorithms for optimization, search, and simulation. Our work explores quantum speedups and efficient implementations for practical problems.
Learn MoreQuantum Complexity Theory
Establishing computational complexity of quantum problems, hardness results, and separations between classical and quantum computing power.
Learn MoreCircuit Verification
Developing tools for verifying quantum circuits and programs. Our AutoQ toolkit provides automated verification for quantum computing systems.
Learn MoreKey Contributions
- Expressive power of quantum neural networks โ Established theoretical foundations for QNN expressivity and trainability, showing advantages over classical networks (Physical Review Research, 2020)
- Quantum generative adversarial learning โ Pioneered QGAN algorithms with experimental demonstrations, enabling quantum-enhanced generative modeling (Physical Review Applied, 2021)
- Barren plateau analysis and mitigation โ Identified barren plateau phenomenon in deep variational circuits and developed Gaussian initialization strategies to escape them (NeurIPS 2022)
- Quantum architecture search โ Developed efficient methods for discovering optimal quantum circuit architectures, enabling automated quantum algorithm design (npj Quantum Information, 2022)
- AutoQ verification toolkit โ Created automated verification tools for quantum circuits and programs, ensuring correctness of quantum computations (TACAS 2025)
Selected Publications
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Expressive power of parametrized quantum circuitsPhysical Review Research 2 (3), 033125, 2020
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Experimental quantum generative adversarial networks for image generationPhysical Review Applied 16 (2), 024051, 2021
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Learnability of quantum neural networksPRX Quantum 2 (4), 040337, 2021
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Escaping from the barren plateau via gaussian initializationsNeurIPS 2022, 18612-18627
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Quantum circuit architecture search for variational quantum algorithmsnpj Quantum Information 8 (1), 62, 2022
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AutoQ 2.0: From verification of quantum circuits to verification of quantum programsTACAS 2025, Lecture Notes in Computer Science, vol 15698