Quantum Innovators 2026

Antonio Anna Mele, Freie Universität Berlin

Quantum process tomography asks how many uses of an unknown quantum channel are needed to learn a classical description that is accurate in diamond distance. While the analogous problem of quantum state tomography has been largely settled over the past decades, the corresponding question for general quantum channels remained open beyond special cases.

In this talk, I will show that a generic d-dimensional channel can be learned using O(d⁴/ε²) channel uses, and more generally that O(d_in d_out k/ε²) uses suffice for channels of Kraus rank k, with optimal dependence on the dimension parameters. Perhaps surprisingly, the natural strategy of performing tomography on the Choi state already achieves this scaling. The main challenge is to avoid the dimension loss arising from a naive conversion between trace distance on Choi states and diamond distance on channels. I will explain how random purification and a direct diamond-distance analysis overcome this obstruction. Finally, I will briefly discuss recent work showing that quantum memory can provide a provable advantage for quantum process tomography.

Based on:
- Optimal learning of quantum channels in diamond distance: https://arxiv.org/abs/2512.10214
- Quantum memory advantage for quantum process tomography: https://arxiv.org/abs/2607.13476

Antonio Anna Mele is a PhD researcher in quantum information at FU Berlin and a 2025 Google PhD Fellow in Quantum Computing. His research has focused on quantum learning theory--how can we efficiently extract useful information, or learn a compact classical model, from a complex quantum system?--as well as on the impact of noise on quantum circuits and their classical simulability. His work has also uncovered mathematical structures underlying quantum systems, from Gaussian and non-Gaussian bosonic and fermionic systems to Clifford symmetries and random unitaries. Looking ahead, he is increasingly interested in quantum algorithms and quantum error correction, with the goal of pushing quantum computing toward genuinely useful applications, particularly as increasingly capable AI models enable previously unimaginable ways of doing research and accelerate the search for such applications.

Francesco Anna Mele, Caltech

The talk will be based on our recent works at the intersection of two important fields of quantum information: quantum learning theory and continuous-variable (CV) systems. Quantum learning theory addresses the question of how to extract classical information from quantum systems as efficiently as possible. CV systems are ubiquitous in nature and in quantum technologies, as they model bosonic systems and quantum optical platforms. The intersection of these two fields raises many interesting questions, some of which are addressed in our recent works. The first natural question is: what is the ultimate achievable performance of tomography for CV systems? We answer this question by establishing the optimal sample complexity of tomography of Gaussian states (an efficient task) and non-Gaussian states (an extremely inefficient task). Other natural questions explored in our recent works include: How does the sample complexity of CV tomography grow with the degree of non-Gaussianity of the unknown state? How can we efficiently learn Gaussian processes? And how can we efficiently test whether an unknown CV state is Gaussian or far from the set of Gaussian states? As a by-product of our analysis, we establish mathematical tools that may be of independent interest, including (i) bounds on the trace distance between CV states in terms of their covariance matrices, and (ii) a Gaussian version of the recently introduced random purification channel.

Francesco Anna Mele was born in Italy in 1997. He received the B.Sc. and M.Sc. degrees in Physics from the University of Pisa, Italy, and an additional degree in Physics from Scuola Normale Superiore (SNS), a special-status university for advanced studies in Pisa, Italy, in 2021. He is about to defend his Ph.D. in Nanoscience at SNS, advised by Vittorio Giovannetti and Ludovico Lami. He was a Student Researcher at Google Quantum AI during the summer of 2026, working with Tom O'Brien. He will join the California Institute of Technology in September 2026 as an IQIM Postdoctoral Scholar. His research interests include all aspects of quantum information and computation.

Linnea Grans-Samuelsson, Université de Sherbrooke

Distributed architectures have been proposed as a pathway to large-scale quantum computers. Combined with the need for fault-tolerance, such architectures require distributed quantum error correction and distributed logical gates. We consider a setting where QPUs are connected using shared Bell pairs that are significantly noisier than on-chip operations. We extend the work in arXiv:2506.17181 on fault tolerance by construction to this setting, deriving different strategies for handling the additional noise. We recover conventional entanglement distillation, but also find more dynamical protocols that allow for space-time trade-offs. Through fault equivalence we show that with integrated decoding, significantly fewer Bell pairs are needed compared to entanglement distillation with separate decoding, and also that fewer Bell pairs are needed when compared to postselected entanglement distillation (repeat-until-success). As a main focus of the work, we derive efficient circuits for an important primitive in distributed fault tolerance: distributed stabilizer measurement. This primitive appears both in the context of distributed quantum memories and in the context of lattice surgery between logical qubits hosted on separate QPUs, and we showcase its usage in both settings. (We also note that the general strategies are applicable beyond stabilizer measurements, and would also apply to e.g. transversal CNOT gates.) The circuits can be adapted to resource constraints, e.g. on the Bell pair generation rate or the space available for on-chip auxiliary qubits. Noting that full local fault-tolerance is not always needed to preserve the correct scaling of logical error rates, we further optimize the circuits depending on the surrounding context. We consider in particular the surface code and the color code, both as distributed memories and in the case of lattice surgery across separate QPUs. Here, robustness to certain hook and readout errors reduces the number of Bell pairs required even further, compared to the context-free setting. We numerically benchmark the resulting implementations under circuit level noise with additional interconnect noise.

I am an incoming tenure-track assistant professor at the physics department of Université de Sherbrooke. The main focus of my research is on quantum error correction and fault-tolerant quantum computing. I am also interested in the connections between quantum error correction and statistical mechanics. Prior to obtaining my position at Sherbrooke I was (and still am, as of writing) a research fellow in theoretical physics at University of Oxford, before which I was a postdoctoral researcher at Microsoft Quantum (Station Q). I did my PhD at Institut de Physique Théorique, CEA Saclay, under the supervision of Hubert Saleur and Jesper Jacobsen, focusing on critical integrable lattice models and non-unitary conformal field theories. I am originally from Sweden.

Miryam Mi-Ying Huang, Carnegie Mellon University

Program obfuscation studies whether a program can be converted into a protected version that **retains** its functionality while **hiding** how it is implemented. In the quantum setting, obtaining such a notion for general quantum circuits has remained a longstanding challenge, and existing results have largely focused on restricted families of quantum programs.

In this talk, I will describe new constructions that extend quantum program obfuscation beyond these restricted settings to general quantum computation. I will first present an obfuscation scheme for unitary quantum programs that supports quantum inputs and outputs, thereby extending **existing** results for pseudo-deterministic computation. Using this construction together with a new notion of subspace-preserving pseudorandom unitaries, we further obtain a quantum ideal obfuscation scheme for arbitrary quantum circuits implementing general completely positive trace-preserving (CPTP) maps. The security of these constructions follows from the existence of post-quantum one-way functions in the classical oracle model.

Miryam Mi-Ying Huang is a postdoc research fellow at Carnegie Mellon University. Previously, she graduated from University of Southern California advised by Dr. Jiapeng Zhang. Her research primarily focuses on cryptography and complexity theory.

Bobak Kiani, Bowdoin College

Many questions in quantum information center around the expressive power of a set of quantum states: Given a Hamiltonian H, how closely can states in that class approach the ground energy of H? I will describe general methods for controlling the energy achievable by restricted classes of quantum states, thereby proving lower bounds on the resources required for state preparation. I will apply these methods in several settings including (1) no-go results for stabilizer states, (2) no-go results for states in the first level of the magic hierarchy, (3) depth lower bounds for SYK with arbitrary ancillas, and (4) tensor network bond-dimension lower bounds for spin models. This is based on joint work with Omar Al-Ghattas and David Gamarnik.

I am an assistant professor at Bowdoin College in Maine. I completed my PhD in electrical engineering and computer science at MIT. My research interests span quantum algorithms, Hamiltonian complexity, and theory of machine learning. I'm always open to meeting new people, so please come find me if you're interested in chatting with me!

Laura Lewis, UC Berkeley

We design an algorithm for learning the coefficients of an n-qubit constant-local Lindbladian to epsilon error with O(g d^2 log(n)/epsilon^2) total evolution time, where g is the single-site energy and d is the (approximate) degree of the interaction graph. Though Lindbladians present new challenges not present in the special case of Hamiltonians, our algorithm achieves the suite of desiderata attained by state-of-the-art Hamiltonian learning algorithms: (1) it uses non-adaptive, ancilla-free randomized Pauli measurement circuits with a time resolution of only Theta(1/g); (2) it works without knowledge of the structure of the unknown Lindbladian; (3) it depends on a smooth form of degree, thereby supporting the learning of quasi-local and power-law Lindbladians.

Our algorithm is a simple iterative method, where the objective function consists of Fourier coefficients of the Lindbladian restricted to few-site regions. Its analysis identifies the difficulty unique to open systems, which we call “confusing” terms. For settings where the “confusion” is limited, the performance of the algorithm improves. We demonstrate this for the case of structure learning of Hamiltonians from access to real-time evolution, where we obtain a new algorithm that is significantly simpler than previous work. In addition, using the same iterative method, we design the first efficient algorithm for structure learning Hamiltonians from high-temperature Gibbs states.

Laura is a second-year PhD student at UC Berkeley, where she is advised by Umesh Vazirani and John Wright. Previously, she was a master’s student at the University of Cambridge and the University of Edinburgh, funded by a Marshall Scholarship. Before that, she completed her bachelor’s degree at Caltech, advised by Thomas Vidick and John Preskill. Laura’s main research interests lie in quantum learning theory and quantum algorithms. 

Chaithanya Rayudu, University of Cambridge

Spectral gaps of local Hamiltonians are fundamental to dictating their physical and computational properties. A uniform lower bound on the spectral gap implies decay of correlations, stability of phases, and efficient algorithms for ground state preparation. Yet obtaining rigorous lower bounds on the spectral gap is notoriously hard, exemplified by open problems like the Haldane conjecture. In this talk, I will present a new method to obtain such bounds from extensions of the Lee-Yang theorem to quantum spin systems.

These extensions, due to Asano and Suzuki-Fisher, state that for a broad class of spin Hamiltonians on any graph, the partition function’s zeros in the complex magnetic field plane lie only on the imaginary axis. For these Hamiltonians, we prove that under a uniform Z-field of any strength h, the ground state has a spectral gap of at least h/2, independent of the system size and of the coupling strengths. The proof uses the zero-freeness of the partition function to show exponential decay of the imaginary-time correlations for any product of Z-operators. Our result gives a polynomial time quantum algorithm for computing the ground state energy of any Lee-Yang Hamiltonian.

Based on joint work (arXiv:2607.10765) with Jun Takahashi.

Chaithanya Rayudu is joining the University of Cambridge as a Postdoctoral Research Associate in fall 2026. He completed his PhD in Physics at the University of New Mexico, advised by Ojas Parekh, and holds a dual degree in Electrical Engineering from IIT Madras. His research lies at the intersection of Hamiltonian complexity, quantum algorithms, and classical optimization. His recent works are on understanding which quantum many-body systems have computationally hard ground states and thermal states, and which admit efficient algorithms.

Francisca Vasconcelos, UC Berkeley

Constant-depth quantum circuits offer a clean playground for studying the origins of quantum advantage. They are simple enough that one can hope to characterize their power, yet already exhibit phenomena with no clear classical analogue. In this talk, I will explore this question through the lens of QAC^0—the class of polynomial-size, constant-depth quantum circuits composed of arbitrary single-qubit gates and unbounded CZ/Toffoli gates. I will center the discussion around a long-standing open problem in quantum circuit complexity: is Parity computable in QAC^0? Despite its apparent simplicity, this question captures a fundamental gap in our understanding of shallow quantum computation. Resolving it would clarify how QAC^0 compares with classical AC^0 and, more broadly, how quantum circuits can use global operations such as fan-out to parallelize computation in fundamentally non-classical ways. I will survey recent upper and lower bounds on this problem and highlight how the resulting techniques have led to surprising applications, including efficient learning of QAC^0 as well as constant-depth pseudorandomness and Dicke state preparation.

Francisca Vasconcelos is a fifth-year PhD student in Computer Science at UC Berkeley, where she is advised by Michael I. Jordan and Umesh Vazirani. Her research broadly focuses on quantum algorithms and quantum complexity. Prior to Berkeley, she received her BS in Electrical Engineering, Computer Science, and Physics from MIT in 2020. She then studied at the University of Oxford as a Rhodes Scholar, completing masters degrees in Statistical Science and Philosophy of Physics. Her research experience also includes internships at Rigetti Computing, Microsoft Research Quantum, and Amazon Quantum. Francisca is a recipient of the NSF Graduate Research Fellowship and the Paul & Daisy Soros Fellowship for New Americans. She is also the Founding Academic Director of Qubit x Qubit, a global quantum computing education initiative.

Alexander Zlokapa, MIT

At large constant temperature, we show that local quantum systems with bounded interaction strength are in a separable, stabilizer thermal state within which polynomial-time classical algorithms can sample and estimate local observables. We find that the aforementioned notions of classicality break down at different temperature scales. Notably, it remains easy to classically estimate local thermal expectations at asymptotically colder temperatures than the sudden deaths of entanglement and magic. Indeed, these classical algorithms remain efficient even in the presence of large quantum circuit complexity, which we prove for the SYK model. Finally, using the non-rigorous replica trick, we conjecture that disordered all-to-all quantum systems generically experience a sharp temperature transition from classically easy to quantumly hard. Our techniques are based on constructing and sampling new cluster expansions (including for mean-field systems), zero-freeness analysis of restricted partition functions, and quantum optimal transport.

Alexander Zlokapa is currently a PhD student at MIT co-advised by Isaac Chuang and Aram Harrow, and his research interests include average-case quantum complexity, spin glass theory and quantum algorithms.