Quantum Technology seminars

Olivia Di Matteo introduces quantum computing and compilation, examining how the translation from high-level quantum programs to efficient hardware instructions becomes a bottleneck as applications scale. The seminar identifies parts of the quantum software stack where machine learning and artificial intelligence can improve this process. It presents the group’s work using graph-based reinforcement learning to optimize quantum circuits, then considers outstanding challenges and research questions for scalable quantum software.

Fault-tolerant computation usually builds an error-correctable logical qubit by entangling several physical qubits. Even when it represents one quantum bit, this logical object occupies a higher-dimensional space. Andrea Morello discusses a different approach that stores quantum information directly in a naturally high-dimensional physical system. The system uses an antimony nucleus with spin I = 7/2 and an eight-dimensional Hilbert space. As a group-V donor, the atom can be implanted into a silicon nanoelectronic device. Its nuclear spin supports Schrödinger-cat states that serve as logical codewords, without constructing them from multiple entangled physical qubits. Encoding employs a generalized rotating frame made possible by modern FPGA signal generators, including the Quantum Machines OPX. The talk considers this control infrastructure as a route to quantum-computing hardware that uses fewer resources.

Bell sampling can efficiently recover the stabilizer group and nullity of pure quantum states with little magic, supporting their tomography and certification. Pauli noise generally destroys exact stabilizers, undermining existing mixed-state structure learners; agnostic tomography instead incurs exponential dependence on nullity. The ideal group nevertheless remains encoded as Pauli invariances of the noisy state. The proposed algorithms learn this group to accuracy ε for nullity at most t. A finder–verifier method extracts structure from partly corrupted sample batches and checks candidate updates, overcoming the loss of group structure in noisy samples. Under sufficiently weak global depolarizing noise, sample and runtime costs are polynomial in n, t and 1/ε when total error is of order εn/t or smaller. Up to approximately 0.16 total error, samples remain polynomial with classical processing scaling as 2^t; any error below one permits a 4^t overhead. Arbitrary Pauli channels have corresponding regimes with stricter thresholds. For weak noise, the results narrow the gap between agnostic methods such as stabilizer bootstrapping and efficient pure-state algorithms.

Mid-infrared spectroscopy reveals molecular signatures of proteins and lipids, but conventional detection needs specialised equipment. Quantum correlations between differently coloured photons, including entanglement, offer another approach: a mid-infrared photon interacts with the sample while interference enables its information to be measured with visible or near-infrared light. The speakers explain the underlying principle and the progression from quantum spectroscopy to imaging with undetected photons. They discuss their work at the University of Technology Sydney to turn quantum mid-infrared imaging into compact, practical instrumentation for biological research.

This introductory webinar connects the physical foundations of quantum computing with present research infrastructure. Matthew Viljoen explains qubits, superposition, entanglement and the transition from classical binary logic to quantum algorithms. Attila Csaba Marosi surveys quantum-hardware types, architectures and practical capabilities, mapping systems available in Europe and beyond. An open discussion considers how infrastructure operators, developers and domain scientists can prepare hybrid classical–quantum workflows within the EGI Federation’s 2026–2030 strategy.

Organic semiconductor molecules underpin the light-emitting diodes used in high-performance smartphone and television displays. Richard Friend traces the path from early discoveries to practical OLED technology and examines a wider range of applications, including solar cells, printable electronics and biological interfaces. He then discusses emerging phenomena in these materials and current work on their use as quantum sensors that can be accessed optically.

Oren Renard studies security of quantum position verification based on f-routing. The work derives resource lower bounds for uniformly generated attackers by connecting successful attacks to the computational complexity of the routed function. Separate results cover arbitrary quantum channels, Pauli-sparse unitaries and Clifford+T circuits. Time and space hierarchies yield explicit functions secure against polynomial or quasipolynomial numbers of attacker qubits under these computational restrictions, extending earlier bounds while increasing the complexity of honest evaluation.

Susanne Yelin explains how modest control of analog quantum platforms can reveal observables and dynamics of strongly correlated fermions, including pairing, long-range interactions and cooling. The seminar then examines universal quantum computation under global control, optimal-control methods for engineering effective interactions and topological dynamics, and approximate error correction designed for analog systems. Together, these approaches treat large quantum devices as adaptable computational resources whose capabilities extend beyond simulating a single model. Hybrid attendance: free in-person participation at Michigan Memorial Phoenix Project, Room 2000, 2301 Bonisteel Boulevard, Ann Arbor, MI 48109, USA, or join remotely through the Zoom link in the organizer page’s main event description. Thursday 8 October, 11:00–12:00 EDT (America/Detroit).

Nicole Yunger Halpern (NIST, QuICS and University of Maryland) examines how to describe nonequilibrium matter in the early universe and particle colliders from the Standard Model. Classical lattice-gauge calculations have limited reach, motivating quantum simulation. Local Gauss-law constraints create an intricate Hilbert-space structure that complicates thermodynamic definitions for systems coupled to reservoirs, both at equilibrium and during nonequilibrium processes. The talk develops definitions of work and heat through strong-coupling thermodynamics, applying them to instantaneous quenches that quantum simulators can implement. A Z2 lattice gauge theory coupled to matter in one spatial and one temporal dimension provides the example: computed work and heat vary with the quench parameter and reveal the expected phase transition. The framework connects gauge constraints, reservoir coupling and experimentally relevant thermodynamic quantities.

Yujie Zhang presents a self-consistent route to certifying entanglement without prior characterisation of measurement devices. Experimentally inferred operational identities and violations of noncontextuality inequalities provide an alternative to conventional witnesses and tomography, and can in principle certify states beyond those accessible to Bell tests. The talk examines the complexity of this approach and implications for practical self-consistent certification methods.

Seminar by Morten Kjaergaard (Niels Bohr Institute, University of Copenhagen) on rapid FPGA-based feedback for superconducting qubits, including sparse-sampling techniques and on-FPGA inference enabling millisecond-timescale T1 estimation, ~100 ms readout optimization, and over 74,000 consecutive recalibrations in closed-loop operation.

Fault-tolerant quantum computation must overcome complex control requirements and substantial error-correction overhead. This talk describes reconfigurable neutral-atom arrays in which optical tweezers coherently transport atoms, enabling any-to-any connectivity, high-fidelity programmable operations and mid-circuit processing in a zoned architecture. Parallel control, transversal operations and long-range connections support experiments ranging from precise quantum-scrambling simulations to a universal fault-tolerant processing architecture. Theoretical methods exploiting reconfigurable connectivity reduce error-correction overhead and suggest that useful large-scale computation could require as few as 10,000 atomic qubits. The discussion considers the prospects for realizing this approach in the near term.

Sahand Tabatabaei presents SQUINT, a sensing platform that combines molecular electron spins, sensitive mechanical detection and control of spin interactions. Molecular sensors can be chemically adjusted and positioned near targets, offering flexibility that defects fixed inside a solid host cannot provide. The work uses a modified XYXY decoupling sequence to reduce dipolar interactions despite uneven control fields. In a sample containing roughly 100 trityl radicals, the reported coherence time reaches about 400 microseconds. These longer-lived spins enable frequency-selective measurements of weak alternating magnetic fields and spectroscopy of nearby nuclear-spin ensembles. The seminar explores how molecular control and mechanical readout can bring quantum sensing closer to complex chemical targets.

Sisi Zhou examines how quantum information methods can improve the precision of physical measurements. Qubit examples distinguish noise that quantum error correction can suppress from noise indistinguishable from the signal. The discussion extends to several qubits, phase estimation, many-body measurements and distributed sensors. A second part applies quantum-learning tools, including tomography, certification and purification, to estimation of several parameters. Because incompatible observables prevent all parameters from being measured optimally together, designing measurements is difficult. The talk presents practical protocols for several such sensing tasks that combine efficient implementation with sensitivity close to the optimum.

Quantum information methods can improve the design of precision sensors. This talk first uses error correction to distinguish suppressible noise from disturbances inseparable from the signal, extending from individual qubits to collective and distributed sensing. It then applies ideas from tomography, certification and purification to estimating several parameters at once. The proposed measurement protocols address incompatible observables while seeking practical implementation and near-optimal sensitivity.

Lawrence Cheuk presents experiments using laser-cooled polar molecules held in optical-tweezer arrays. Molecular internal states and electric-dipole interactions provide resources for quantum computation, simulation and precision measurement. The talk covers preparation, control and detection of individual molecules; two-qubit operations and deterministic molecular entanglement; and one-dimensional spin simulations. Those experiments examine how spin excitations move, form bound pairs and undergo coherent pair creation and removal. A second set of experiments generates molecular spin-squeezed states and uses spatially resolved measurements to investigate reduced measurement noise, bipartite entanglement and Einstein-Podolsky-Rosen steering. Preliminary progress toward larger arrays may also be discussed. The Donald R. Hamilton Colloquium takes place in Jadwin Hall A-10 at Princeton University.

James K. Thompson describes how photons repeatedly reflected inside an optical cavity can couple laser-cooled atoms. These collective interactions provide tools for both simulating many-body systems and improving precision measurements. The seminar examines ways to shape the coupling, including exchange and XYZ interactions, interactions involving three or four particles, dissipative processes and protection of coherence through a many-body energy gap. Applications include studying time-dependent phases of superconductors, increasing the sensitivity of matter-wave interferometers and advancing optical clocks. The organizer provides both an Ann Arbor venue and an online attendance option.

Florian Marquardt surveys approaches to reducing the energy and computational cost of neural networks by implementing their functions in physical devices. The central question is how experiments can efficiently determine updates to adjustable parameters, allowing the hardware itself to learn. Examples include learning through time reversal, obtaining nonlinear computation from linear optical scattering, and training analog quantum simulators as neuromorphic systems. The seminar is available in person and through the registration link on the organizer’s event page.

Two-dimensional topological order has caught much attention in connection to topological phases of matter and topological quantum computation, in particular. It is known that tensor networks are useful tools to study mathematical structures appearing in these topics. We present recent understanding of them from a viewpoint of operator algebras. No knowledge on operator algebras are assumed.

Tensor networks help describe the structures underlying two-dimensional topological phases and topological quantum computation. This talk develops an operator-algebra perspective on those connections and presents recent mathematical understanding of topological order. The introduction is designed to be accessible without prior training in operator algebras.