INFORMS said the award recognizes fundamental, sustained contributions that have stood the test of time, using significance, innovation, depth and scientific excellence as its criteria. Ben-Tal is an internationally recognized expert in continuous optimization whose technical work has extended across robust optimization, convexity theory, duality, optimal decision-making under uncertainty, structural design, semidefinite optimization and conic optimization. Ben-Tal helped create robust optimization through a long series of papers and a monograph written with Arkadi Nemirovski and Laurent El Ghaoui.

According to the announcement, his contributions span the field’s core theory, including tractability, and extended the area from static linear problems to multistage decision-making. He has also worked on applications of robust optimization in machine learning. INFORMS Chief Executive Officer and Executive Director Elena Gerstmann said Ben-Tal’s work on robust optimization was among the field’s most consequential contributions because it gave operations research a rigorous framework for making sound decisions when data are uncertain. “He didn’t just advance an area of research.

He helped create one, and the field has been building on it ever since,” Gerstmann said. Beyond robust optimization, Ben-Tal has conducted foundational work in convexity theory, duality and optimal decision-making under uncertainty. He also contributed to structural design by helping develop the theory and computational toolbox for semidefinite and conic optimization.

The announcement describes these achievements as influential, long-running contributions to operations research and the management sciences rather than as results of a single study. This is an award announcement based on the published body of research and the association’s assessment of its significance; it does not report a new experiment, clinical trial or fresh quantitative evaluation. Ben-Tal’s connection to the field has included teaching, student supervision, editorial work and professional service in addition to research.

The prize will be presented at the 2026 INFORMS Annual Meeting in San Francisco, scheduled for November 1–4, 2026. It includes a $5,000 cash prize, a medal and a formal citation. INFORMS, which describes itself as the world’s largest association for professionals and students in operations research, AI, analytics, data science and related disciplines, has more than 12,000 members across academia, industry and government.

Subject of Research: Recognition of Aharon Ben-Tal’s sustained contributions to robust optimization and operations research theory

Article Title: Aharon Ben-Tal awarded 2026 INFORMS John von Neumann Theory Prize

Article References: Aharon Ben-Tal awarded 2026 INFORMS John von Neumann Theory Prize. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: Aharon Ben-Tal, INFORMS, John von Neumann Theory Prize, robust optimization, operations research, continuous optimization, convexity theory, duality, multistage optimization, machine learning, semidefinite optimization, Aharon

The central question the team addressed sounds deceptively simple: given a large quantum system coupled to its environment, what is the maximal rate at which it can decay, and how does that rate grow as the system gets bigger? In a collection of many particles, vacuum fluctuations of the electromagnetic field—or of phonons, magnons, and other collective excitations—mediate long-range dissipative interactions that can never be switched off. When emitters are packed closely together, their decay becomes correlated and collectively enhanced, a phenomenon famously exemplified by Dicke superradiance, in which a dense ensemble of excited atoms emits a burst of light far brighter than the sum of independent emissions. Correlated decay can be a curse, shortening coherence times in quantum processors and degrading the signal-to-noise ratio of atomic clocks, or a blessing, enabling new light sources and the dissipative preparation of entangled many-body states. Knowing the worst-case decay rate is therefore essential for both diagnosing the limits of quantum hardware and exploiting dissipation as a resource.

Computing that maximal decay rate exactly, however, is extraordinarily hard. The researchers showed that the problem is formally equivalent to finding the ground-state energy of a generic two-local spin Hamiltonian, a task known to be quantum Merlin–Arthur complete—believed to be intractable even for a quantum computer. Except in trivial limits, such as non-interacting qubits or fully symmetric all-to-all models, no exact solution exists. Rather than surrender to this complexity, the team turned it to their advantage. By importing tools from Hamiltonian complexity theory into the study of out-of-equilibrium open quantum dynamics, they derived general upper and lower bounds on the maximal decay rate that hold for a broad class of Markovian many-body quantum systems, encompassing arbitrary Hamiltonian interactions, coherent driving, diverse decoherence channels, and even disorder in the dissipative couplings.

The mathematical framework rests on a Lindblad master equation describing N qubits whose collective dissipation is encoded in a Hermitian, positive semidefinite decoherence matrix. The instantaneous decay rate of the many-body system can be written as the expectation value of an auxiliary Hermitian Hamiltonian, which generically takes the form of an XY model on a weighted interaction graph with a local transverse field. Maximizing the decay rate over all possible quantum states then amounts to finding the ground-state energy of the negative of that auxiliary Hamiltonian—the very problem complexity theory tells us is hard. The team’s key insight was to bound this quantity using product states, simple unentangled states in which each qubit is treated independently with a carefully chosen phase. Remarkably, this approach yields bounds that remain valid for any state, including highly entangled ones, without relying on mean-field approximations. The physical message is striking: entanglement is not necessary for a system to dissipate at a rate near the theoretical maximum.

The resulting bounds take a compact form involving the largest eigenvalue of the decoherence matrix, the average single-qubit decay rate, and a parameter that measures how uniformly the brightest collective decay channel is spread across the system. When decay is delocalized—meaning the dominant collective jump operator has approximately uniform spatial support over all the qubits—the bounds become tight up to a constant factor, yielding an elegant scaling law: the maximal decay rate is proportional to the system size times the largest collective transition rate. This simple relation is far from obvious and does not hold for arbitrary systems; the authors even constructed explicit mathematical counterexamples showing that no universal scaling law depending only on system size and the spectrum of the decoherence matrix can exist in general. But for the physically relevant delocalized regime, the law holds, and it remains valid even when local Hamiltonian terms and local dissipation are included, since those contribute only subleading corrections.

The researchers then applied their machinery to the platform of greatest experimental relevance: ordered arrays of atoms in free space, which have become a workhorse for quantum computing, quantum simulation, atomic clocks, and spin squeezing. Here the decoherence matrix is proportional to the electromagnetic Green’s function evaluated at the resonance frequency, a long-ranged function with oscillating sign that makes the problem genuinely non-trivial. By analyzing divergences of the transition rates in reciprocal space—divergences that appear in two- and three-dimensional lattices as constructive interference of photon emission is enhanced—they found that the maximal decay rate scales as N raised to the power of three-halves minus one over twice the array dimensionality. This dimensional scaling is universal in the strictest sense: it is independent of lattice geometry, lattice constant, and atomic polarization, all of which enter only as prefactors. For one-dimensional arrays, the decay effectively behaves as though the atoms were non-interacting, while for two- and three-dimensional arrays the collective enhancement grows with size.

To validate these analytical predictions, the team employed a semidefinite-programming relaxation, a numerical technique borrowed from approximation algorithms that can be solved in polynomial time. They proved rigorously that the semidefinite-programming solution approximates the true maximal decay rate up to a constant factor, establishing a universal relation valid for arbitrary systems regardless of whether decay is delocalized. Numerical simulations for arrays with lattice constants comparable to the resonance wavelength confirmed the predicted dimensional scaling with excellent agreement. The analysis also mapped out the crossover between regimes: for arrays much smaller than the wavelength, Dicke’s quadratic scaling is recovered, while for very widely spaced atoms the ensemble behaves as independent emitters. For intermediate, physically relevant spacings, the crossover to superlinear collective decay requires a number of atoms that grows rapidly with the lattice constant, explaining why current experiments with a few hundred atoms have not yet observed the full dimensional scaling.

The implications ripple across quantum science. The scaling laws set rigorous upper bounds on transient superradiant bursts from extended atomic arrays and on the intensity and optimal pump rate of superradiant lasing, ruling out such lasing for one-dimensional free-space arrays while suggesting that a superradiant lasing phase transition might occur in two and three dimensions. They also determine the threshold drive intensity for driven-dissipative Dicke phase transitions in free space, consistent with recent numerical studies of collective resonance fluorescence. Perhaps most consequentially for technology, the authors estimated the impact of collective decay on Rydberg atom quantum processors, where microwave transitions have wavelengths of roughly ten millimeters and typical experiments already sit within the collective decay regime. Their analysis of a two-dimensional array of tweezer-trapped rubidium atoms performing parallel entangling gates suggests that in a next-generation 40-by-200 array, collectively enhanced leakage could contribute roughly one-third of the total gate error budget—a non-negligible share that grows as arrays scale up.

Quantum error correction faces its own reckoning with these results. In the worst case, the error rate per qubit from correlated decay scales with the maximal decay rate divided by system size, which grows with array size in two dimensions and above, meaning that corrections must be applied on timescales that shorten as processors expand. Yet the news is not uniformly grim: the researchers proved that typical stabilizer states—the random-looking states that populate the code space of many error-correcting schemes—decay at nearly the rate of independent emitters, protected by random phases between qubits. The danger lies in structured states, since even simple product states can be superradiant. Looking forward, the authors point to quantum metrology, where carefully prepared Dicke and squeezed states may hit fundamental limits imposed by correlated decay, and to theoretical extensions of their approximation techniques to higher-order observables, driven-dissipative steady states, interacting fermions and bosons, and even maximal absorption rates relevant to quantum batteries. What began as a question about how fast quantum systems fall apart has yielded a unifying framework for the limits of dissipation itself.

Subject of Research: Universal scaling laws governing the maximal correlated decay rate of many-body open quantum systems

Article Title: Universal scaling laws for correlated decay of many-body quantum systems

Article References: Mok, W.-K., Poddar, A., Sierra, E., Rusconi, C. C., Preskill, J., & Asenjo-Garcia, A. (2026). Universal scaling laws for correlated decay of many-body quantum systems. Nature Physics. https://doi.org/10.1038/s41567-026-03448-4

Image Credits: AI Generated

DOI: 10.1038/s41567-026-03448-4

Keywords: quantum decay, decoherence, superradiance, open quantum systems, atomic arrays, Lindblad master equation, quantum complexity theory, semidefinite programming, Rydberg atoms, quantum error correction, Dicke scaling, quantum optics

The formal setting of the problem involves a positive operator-valued measure, or POVM, a collection of positive semidefinite operators that sum to the identity operator. Each POVM element corresponds to a possible guess: when a measurement is performed on a system prepared in one of the candidate states, the outcome indicates which state the experimenter concludes was present. The goal of minimum-error discrimination is to choose the POVM that maximizes the average probability of guessing correctly, given the a priori probabilities of the states. Mathematically, this optimization is an instance of semidefinite programming, a class of convex optimization problems that can, in principle, be solved to arbitrary precision. Yet semidefinite programs scale poorly as the number of states and the dimension of the underlying Hilbert space grow, which motivates the search for structural shortcuts that bypass the full optimization.

The first structural insight revisited in the work concerns single-qubit pure states. For ensembles of such states, the authors confirm that the matrix of pairwise fidelities, the magnitudes of the inner products between the states weighted by their probabilities, fully determines the optimal discrimination probability. The proof relies on the geometry of the Bloch sphere: each qubit pure state corresponds to a unit vector in three-dimensional real space, and pairwise fidelities fix all inner products between these Bloch vectors. A classical result of matrix analysis then guarantees that the whole configuration of Bloch vectors is fixed up to an orthogonal transformation. In three dimensions, every rotation can be realized by a unitary operator on the qubit Hilbert space, and reflections can be absorbed by an additional transposition trick applied to both states and measurement operators. Consequently, ensembles related by such transformations share identical optimal success probabilities, and pairwise fidelities alone suffice to pin the answer down.

Remarkably, this tidy correspondence collapses in higher dimensions. For Hilbert spaces of dimension greater than two, the generalized Bloch vectors live in spaces of dimension d squared minus one, and not every orthogonal transformation of those vectors corresponds to a physically allowed unitary on the system. Starting from a counterexample with three states in a three-dimensional Hilbert space, the authors show that the failure propagates to all higher dimensions and larger ensembles. In other words, knowing only how similar each pair of states is, in the fidelity sense, is no longer enough to determine how well they can be distinguished; phase information and richer structural detail become essential. This dimensional divide sharpens our understanding of when reduced descriptions of quantum ensembles can legitimately stand in for the full state data.

As an illustration of the single-qubit result, the paper derives a closed-form expression for the optimal discrimination probability of three equiprobable pure qubit states with equal pairwise fidelities, a condition the authors call equal fidelity distance, weaker than the conventional requirement of equidistance in the complex Gram matrix. The ensemble turns out to be geometrically uniform, meaning its states are related by a symmetry operation, a rotation of the Bloch sphere by 120 degrees about a suitable axis. For geometrically uniform states, the optimal measurement is known to be the pretty good measurement, or PGM, a canonical strategy constructed from the square root of the ensemble density matrix. Carrying out the algebra with an explicit formula for the square root of a two-by-two matrix, the authors find that the optimal success probability equals one third plus a term proportional to the square root of one minus the squared pairwise fidelity, tracing an arc of an ellipse as the fidelity parameter varies. The result is a rare example of a fully fidelity-based closed form for a multi-state discrimination problem.

The second major thread of the work concerns sparsity in the optimal measurement. Sometimes the optimal POVM assigns a zero operator to one or more states, effectively giving up on identifying them and devoting the full measurement resources to the remaining, more probable candidates. Mirror-symmetric qubit states provide a classic example: when the outer states are sufficiently probable relative to the middle one, the optimal measurement simply distinguishes the two outer states and ignores the third. Cha and Lee formalize this by defining the set of indices whose optimal POVM elements are nonzero, and they prove a scaling relation: once this set is known, the original discrimination problem reduces to a smaller problem on the surviving states, with the optimal success probability multiplied by the total prior probability of those states. This reduction is the key that unlocks tighter performance bounds.

The practical payoff comes in the form of refined upper bounds on the optimal success probability. A well-known bound due to Renes relates the optimum to the PGM success probability, and the authors show that applying this bound to the reduced ensemble of nonvanishing states often yields a strictly tighter estimate. The improvement is not universal, and the authors are careful to document this: for three mirror-symmetric qubit states there exists a small region of parameters where the refined bound is actually weaker, and numerical experiments over random pure and mixed ensembles on up to five qubits confirm that the refined bound dominates more often as the number of qubits or states grows, approaching universal superiority in the largest configurations tested. For equiprobable states the authors conjecture, supported by extensive numerics and partial analysis, that the refined bound never loses, and they verify this conjecture analytically in the simplest nontrivial setting.

Beyond the PGM-based bound, the paper extends the same sparsity-aware reduction to three other upper bounds drawn from the literature, including a fidelity-sum bound, a bound involving the trace norm of the square root of the summed squared weighted density matrices, and a trace-distance bound. For the latter two, the authors prove that the reduced versions are always at least as tight as the originals, with the trace-norm case following elegantly from the operator monotonicity of the square root function, the Löwner-Heinz inequality. For the fidelity-sum bound with equiprobable states, a direct combinatorial argument shows the reduced bound is always tighter as well. Together these results demonstrate that partial knowledge about which measurement operators vanish is a broadly useful resource for bounding discrimination performance without solving any optimization problem at all.

Perhaps most intriguingly, the authors show that such partial knowledge can often be obtained cheaply. They present a necessary condition certifying that a given state must receive a nonzero optimal operator, based on the overlap of supports of pairwise difference matrices, and a sufficient condition certifying that an operator must vanish, based on expressing one state as dominated by a convex combination of the others. In numerical experiments on ten thousand randomly generated three-state qubit problems, these two tests coincided in roughly half of the instances, uniquely identifying the full set of nonvanishing operators without ever running a semidefinite program. Since unions of certified subsets and intersections of certified supersets remain valid, the framework offers a compositional route to narrowing down the optimal measurement structure. The authors conclude that determining the vanishing pattern of the optimal POVM efficiently, whether analytically or heuristically, remains an open challenge, but one whose solution could substantially reduce the computational cost of optimal quantum measurements, with implications for quantum communication, sensing, and the readout of quantum information in any technology where nonorthogonal states must be told apart at the limits allowed by physics.

Subject of Research: Structural properties of quantum states and measurements in optimal quantum state discrimination

Article Title: Structural perspectives from quantum states and measurements in optimal state discrimination

Article References: Cha, H., & Lee, J. (2026). Structural perspectives from quantum states and measurements in optimal state discrimination. Quantum Information Processing, 25(9), Article 310. https://doi.org/10.1007/s11128-026-05335-6

Image Credits: AI Generated

DOI: 10.1007/s11128-026-05335-6

Keywords: quantum state discrimination, minimum-error discrimination, positive operator-valued measure, pretty good measurement, pairwise fidelity, semidefinite programming, Bloch vector, sparsity, upper bounds, geometrically uniform states, quantum information theory, Structural