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500/1-001 - Main Auditorium (CERN)
Description
This event will be webcast, see link above.
The International Conference on Quantum Technology for High-Energy Physics will be hosted at CERN on 20-24 January 2025.
Following the great success of QT4HEP 2022, surrounded by the magnificent new architecture of the Science Gateway, this second edition of the #QT4HEP conference takes place to further investigate quantum technology and what the overall impact of this new frontier of science on fundamental physics might be.
The conference will serve as a forum to discuss the potential and challenges surrounding quantum technology. The focus will be on what the overall impact of these emerging technologies might be on fundamental physics research, and vice versa, how the expertise and technologies developed across the decades for high energy physics research can contribute to quantum technologies.
Bringing the whole community together, we will discuss the recent developments in the field and keep looking for activities within the HEP community – and beyond – that can most benefit from, or contribute to, the emerging quantum technologies.
The event is a great opportunity to share knowledge and ideas, co-develop quantum expertise and skills, as well as to foster collaborations with academia and industry on national and international levels.
Join us in the effort to explore the full potential of innovative quantum technology and its links to the high-energy-physics and particle physics community.
The programme will cover all areas of quantum technology at CERN, specifically hybrid quantum computing and algorithms, quantum simulations, quantum sensors and quantum networks. The event aims to foster the co-development partnerships with companies, institutes and other entities and training and education while highlighting ongoing partnerships.
A Quantum Hackathon will take place on January 24th: this is a hybrid hackathon with a in-person kickoff at CERN with the introduction to the challenge and to the platform hosting the competition (aqora) and will continue the whole Friday 24th and then remotely until March 28th.
More details here: cern-hep-challenge-2025
We look forward to welcoming you to the event!
For more information about CERN QTI, follow us on LinkedIn.
Poster session and contributed talk:
Selected participants have an opportunity to showcase their research results as an oral or poster presentation during the QT4HEP conference.
Contact
Webcast
There is a live webcast for this event
Zoom Meeting ID
66053527089
Host
Kristina Gunne
Passcode
97023907
Useful links
Join via phone
Zoom URL
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Welcome introduction 500/1-001 - Main AuditoriumSpeaker: Enrica Maria Porcari (CERN)
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Speaker: Dr Sofia Vallecorsa (CERN)
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The European Commission Joint Research Centre's contribution to quantum and precise time research 500/1-001 - Main AuditoriumSpeaker: Petra Scudo (European Commission)
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10:30
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High-speed SNSPDs for clock-rate scaling in quantum networks 500/1-001 - Main AuditoriumSpeakers: Boris Alexander Korzh (California Institute of Technology (US)), Boris Korzh (University of Geneva)
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Photon-number-resolving SNSPDs and their applications for quantum networks and quantum computing [IDQuantique] 500/1-001 - Main AuditoriumSpeaker: Félix Bussières
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Metropolitan-scale entanglement generation between quantum processors: from the lab into the real world 500/1-001 - Main Auditorium
Presenting recent quantum networking demonstration: Metropolitan-scale heralded entanglement of solid-state qubits
https://www.science.org/doi/10.1126/sciadv.adp6442?cookieSet=1Speaker: Arian Stolk (Delft University of Technology) -
12:30
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The Entanglement Fabric: enabling distributed quantum computing with quantum networks [Nu Quantum] 500/1-001 - Main AuditoriumSpeaker: Simone Eizagirre Barker (NU Quantum)
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Entanglement Networking Hardware: Driving Real-World Applications Beyond QKD [Qunnect] 500/1-001 - Main AuditoriumSpeaker: Maël Flament (Qunnect)
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The Road to a Quantum-Connected World: Insights from Deutsche Telekom’s Activities 500/1-001 - Main AuditoriumSpeaker: Matheus Ribeiro Sena (Deutsche Telekom)
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Building physics-centric Quantum Networks [Stony Brook University ] 500/1-001 - Main AuditoriumSpeaker: Eden Figueroa (Stony Brook University)
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16:00
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Open discussion session on synchronisation for quantum communication 500/1-001 - Main Auditorium
The conference at CERN will be a unique opportunity to have a discussion among key actors in synchronisation for quantum communication:
What is missing in the domain of synchronisation for quantum communication?
- Precision/accuracy needs.
- User interface: providing and time-stamping pulses, bursts, etc. Controlling times, repetition rates, duty cycles…
- Distance requirements
- Combining or not quantum and classical networks in the same fibre
- Opportunities for standardisation, formal or de-facto
- Other?
What are your plans for the short and medium term?
Can this event spawn one or more collaborative efforts in this domain?Please write to us at qti@cern.ch with your initial thoughts so we can put in place a plan for this session, including moderation, and make the best use of our valuable time together. We are looking forward to seeing many of you at CERN!
Join the conversation also remotely by: https://cern.zoom.us/j/64805775491?pwd=nEFVqxJyMP8pZumuxpkZ8WvLOgVdSa.1
Speaker: Javier Serrano (CERN)
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Colloquia 81/R-003C - Science Gateway Auditorium CConvener: Benjamin Frisch (CERN)
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IntroductionSpeaker: Benjamin Frisch (CERN)
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Quantum network technology – the second life of rare-earth crystals [University of Geneva]Speaker: Wolfgang Tittel (University of Geneva)
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Quantum computing roadmaps toward fault-tolerance
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Synopsis : Olivier Ezratty will present the state of the art of quantum computing across various qubit modalities and algorithms classes, and how the academic and industry vendor ecosystem is planning to build utility-grade fault-tolerant quantum computers in the next decades. He will frame the wealth of challenges ahead related to qubit quality at scale, manufacturing, quantum error correction, quantum computers interconnect, resource estimations, energetic footprints, as well as on algorithms design and software engineering.
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Speaker : Olivier Ezratty is a freelance quantum engineer, author, trainer, teacher and researcher, mostly known for “Understanding Quantum Technologies”, his comprehensive open-source book on quantum technologies (September 2021, 2022, 2023 and 2024, 1,554 pages). He is a teacher and lecturer on quantum and classical technologies at EPITA, CentraleSupelec, ENS Paris-Saclay, and other Universities. He works for a diverse set of government institutions and industry organizations, as a referent expert for Bpifrance, Agence Nationale de Recherche (France), the European Commission (European Quantum Flagship) and venture capital funds. He is also one of the cofounders of the Quantum Energy Initiative. He has an Msc in Computer Science from CentraleSupelec.
Speaker: Olivier Ezratty -
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10:35
Coffee
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The Quantum Internet: Applications, Challenges and OpportunitiesSpeaker: Arian Stolk (Delft University of Technology)
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The Open Quantum Institute (OQI)Speaker: Marianne Schoerling (Geneva Science and Diplomacy Anticipator (CH))
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12
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12:15
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12:30
Lunch 81/R-003C - Science Gateway Auditorium C
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Computing and Algorithms 81/R-003C - Science Gateway Auditorium CConvener: Dr Michele Grossi (CERN)
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Efficient Use of Quantum Computers for Collider PhysicsSpeaker: Christian Walter Bauer (Lawrence Berkeley National Lab. (US))
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Quantum Machine Learning with Physics-Informed and Symmetry-Aware Models
In the talk I will describe recent advances in building quantum models that can learn on tasks with symmetries and laws of physics in the form of differential equations. First, I will introduce approaches based on differential constraints and discuss their applications in generative modelling. Second, I will discuss the concept of quantum data and present an analysis for embeddings motivated by physical processes. Finally, I will show that embedding symmetries into quantum machine learning models can help discovering protocols able to solve Simon's and forrelation problems with excellent generalization.
Speaker: Prof. Oleksandr Kyriienko -
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Quantum Observables in HEPSpeaker: Prof. Fabio Maltoni (Universite Catholique de Louvain (BE))
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15:30
Coffee break
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Quantum simulating far-from-equilibrium dynamics of gauge theoriesSpeaker: Jad C. Halimeh
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The quantum life of a Feynman propagator as a qubitSpeaker: German Rodrigo (IFIC UV-CSIC)
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Networking cocktail and Poster Session 81/R-003C - Science Gateway Auditorium C
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Quantum Technology Platforms 500/1-001 - Main AuditoriumConvener: Clara Murgui Galvez (CERN)
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Overview Quantum Sensors for particle physicsSpeaker: Steven Worm (Deutsches Elektronen-Synchrotron (DE))
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High Tc superconducting particle detectorsSpeakers: Ilya Charaev (University of Zurich), Ilya Charaev (Massachusetts Institute of Technology)
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10:30
Coffee Break
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Superconducting CalorimetersSpeaker: Prof. Sebastian Kempf (Institute of Micro- and Nanoelectronic Systems, Karlsruhe Institute of Technology)
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Chromatic calorimetry (quantum dots)Speaker: Yacine Haddad (Northeastern University (US))
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Nuclear clocks for fundamental and particle physicsSpeaker: Dr Ekkehard Peik (PTB)
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SRF / heterodyne cavity applications for axion searchesSpeaker: Sebastian Ellis (SLAC National Accelerator Laboratory (US))
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12:25
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Quantum Technologies Platforms 500/1-001 - Main AuditoriumConvener: Sergio Calatroni (CERN)
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QSNET: network of clocks for measuring the stability of fundamental constantsSpeaker: Leonid Prokhorov (University of Birmingham)
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Superconducting Qubits as Particle DetectorsSpeaker: Francesco De Dominicis (Gran Sasso Science Institute, Italy)
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High-Q cavity coupled to a high permittivity dielectric resonator for sensing applicationsSpeakers: Antonio Cassinese (Unknown), antonio cassinese
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Magnetic techniques for quantum sensing using single molecule magnets in the NAMASSTE experimentSpeaker: Giuseppe Latino (Universita e INFN, Firenze (IT))
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Towards realization of long-lived chains of circular Rydberg atoms for quantum simulatioSpeaker: Ankul Prajapati (Laboratoire Kastler Brossel, École Normale Supérieure, CNRS, Université PSL, Sorbonne Université, Paris, France.)
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Characterization of a Rubidium based Four Way Mixing Entangled Photon Pair Source with SNSPDsSpeaker: Federica Facchin (Single Quantum)
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Highly Sensitive Optical Quantum SensorsSpeaker: Dr Young Jin Kim (Los Alamos National Laboratory)
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15:30
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Computing and Algorithms 500/1-001 - Main AuditoriumConvener: Dr Michele Grossi (CERN)
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On multivariate polynomials achievable with quantum signal processing
Quantum signal processing (QSP) is a framework which was proven to unify and simplify a large number of known quantum algorithms, as well as discovering new ones. QSP allows one to transform a signal embedded in a given unitary using polynomials. Characterizing which polynomials can be achieved with QSP protocols is an important part of the power of this technique, and while such a characterization is well-understood in the case of univariate signals, it is unclear which multivariate polynomials can be constructed when the signal is a vector, rather than a scalar. This work uses a slightly different formalism than what is found in the literature, and uses it to find simpler necessary conditions for decomposability, as well as a sufficient condition - the first, to the best of our knowledge, proven for a (generally inhomogeneous) multivariate polynomial in the context of quantum signal processing.
Speaker: Lorenzo Laneve (Università della Svizzera Italiana) -
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Complementary polynomials in quantum signal processing
Please see attached pdf file.
Speaker: Dr Bjorn Berntson (Riverlane) -
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Estimates of loss function concentration in noisy parametrized quantum circuits
See attached pdf abstract.
Speaker: Giulio Crognaletti (Universita e INFN Trieste (IT)) -
16:45
Break
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Guarantees for smart initializations in variational quantum computing
Added as a separate file.
Speaker: Ricard Puig -
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Neural quantum states for lattice field theory
The study and impact of lattice gauge theories on high-energy physics cannot be understated. However, the difficulties involved in simulating strongly-coupled systems has hampered our attempts to fully understand phenomena like quark confinement and hadronisation. We present an application of state-of-the-art machine learning techniques under the umbrella of neural quantum states to high-energy physics. Notably, the ansatze for this work combines physically motivated Jastrow terms with non-trivial equivariance under gauge transformations. Using this, we will present accurate determinations of ground state properties of the non-Abelian SU(2) lattice field theory across a range of inverse couplings.
Speaker: Thomas Spriggs (Delft University of Technology) -
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Prospects for the quantum simulation of quark-gluon plasma
We present a quantum computing algorithm for fluid flows based on the Carleman-linearization of the Lattice Boltzmann (LB) method.
We demonstrate the convergence of the classical Carleman procedure at moderate Reynolds numbers, namely for Kolmogorov-like flows. Since the CLB procedure shows excellent convergence properties up to Reynolds numbers of order of hundreds, it is plausible to expect that once a viable CLB quantum algorithm is available, it can be readily extended to the quantum simulation of quark gluon plasmas.
We proceed to formulate the corresponding quantum algorithm, including the quantum circuit layout and analyze its computational viability.
We exploit the sparse nature of the CLB matrix to build a quantum circuit based on block-encoding techniques which make use of matrix oracles. The gate complexity of the algorithm is quadratic with the number of qubits, but the probability of success of the corresponding circuit is very low, due to the need of employing several ancilla qubits. It thus appears like the oracle formulation of the CLB procedure implies a tension between the depth of the circuit and its probability of success. To date, such tension does not result into any viable tradeoff, pinpointing the need of further developments in the block-encoding procedure. Finally we describe possible directions along this line.Speaker: Claudio Sanavio (Istituto Italiano di Tecnologia) -
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Tree Tensor Network predictors implemented on FPGA for ultra-low latency inference.
Tree Tensor Networks (TTNs), a loopless type of tensor network, are commonly used to represent and simulate many-body quantum systems, but they can also be exploited for several applications in Machine Learning (ML).
They rely on the factorization of high-order tensors into networks of smaller tensors, effectively overcoming the "curse of dimensionality” by moving the computational complexity from an exponential to a polynomial scaling with the number of inputs. Originally designed for studying weakly entangled quantum states, TTNs can also be leveraged in several ML tasks, exploiting their quantum-inspired characteristics to gain useful insights into the distribution of learned information. Entanglement entropy and quantum correlations can help optimize data representation, eliminate redundancies, and reduce the number of active parameters. Moreover, due to the linearity of the operations involved in their algorithms, TTNs happen to be well-suited for implementation on hardware like Field Programmable Gate Arrays (FPGAs), which excel in fast parallel computations.
Starting from the statements above, this study focuses on the development of TTNs on FPGAs, intending to deploy them in ultra-low latency environments, such as High Energy Physics (HEP) experiments, where rapid decision-making is crucial. To do this, various TTN architectures are studied as binary classifiers, starting with simple benchmark datasets (Iris and Titanic) and moving to the more complex case of a classifier for b/b̄ jet flavor tagging, exploiting LHCb open data.
The TTNs are trained using tensor-specific optimization techniques ("sweeping" algorithms) rather than classical ML optimizers, reducing computational costs and avoiding issues like barren plateaus. Additionally, the architectures are initialized with unsupervised learning methods to improve training stability and avoid falling into local minima. For the inference algorithm offloaded in hardware, the full contraction process iteratively combines feature vectors with network weights, producing output values representing the probabilities of a single sample to belong to each final class.
Two different hardware strategies for tensor contraction are produced as VHDL firmware for FPGA, allowing resource usage and latency analyses to support a wide range of TTN topologies. The Full Parallel (FP) implementation performs most operations simultaneously and it achieves the lowest latency, but it uses significant hardware resources, making it suitable for environments with high resource availability. The Partial Parallel (PP) implementation instead reduces Digital Signal Processors (DSPs) usage by sequentially computing tensor-weight multiplications, happening to be more suitable for resource-constrained environments where latency is less critical. Both methods use pipelining for efficient computation of multiple input samples, and the flexibility of DSPs is leveraged to adapt tensor operations to FPGA constraints.
Regarding the firmware precision, the numbers in the TTN implementations are represented using 16-bit fixed-point precision and normalized to the range [−2,2]. However, different TTN architectures might require varying numeric precision, therefore quantization studies are essential for optimizing resource usage since reducing the number of bits used to represent values in firmware can significantly lower the number of needed DSPs.
This project is developed on XCKU115 FPGA, which is pre-programmed with fixed hyperparameters; the trained tensor weights are instead stored in on-chip memory (BRAM). Communication between the host PC and FPGA is handled using AXI Lite and AXI Stream protocols for weight programming and data streaming, respectively. Eventually, inference results are validated by comparing FPGA outputs with software outputs, ensuring consistency. For all the involved networks, hardware outputs closely match software results, ensuring identical classification labels in hardware and software and accurate FPGA deployment of TTNs for real-time classification tasks.
The LHCb TTN classifier for b/b̄ jet flavor tagging is successfully implemented on FPGA, achieving sub-microsecond inference latency, demonstrating its potential for real-time use in HEP trigger systems. With this work, it is confirmed the feasibility of deploying TTNs on FPGA with efficient resource usage and high-speed performance, making them suitable for low-latency applications like HEP experiments.
For the future developments of this project, several possibilities are being considered. Currently, this TTN deployment is being compared to classic NN implementations available in hls4ml, considering LHC jet tagging datasets. The comparisons are in terms of accuracies, readability, and resource consumption, to identify the most suitable tool that can be used for ultra-low latency classification at the lower levels of the trigger pipelines of HEP experiments, where the available signals are not yet constituting complex physical objects but better trigger primitives. If the two approaches would report completely orthogonal characteristics, the joint usage of TTNs and NNs for learning the dataset's relevant information for classification could lead to the production of a new software tool for quantum-inspired ML. In addition to this, the transposition from VHDL to hls4ml of the firmware generative code is being considered, also possibly enlarging the current libraries with additional TN ansatzes: this could allow more users to exploit the perks of tensor network methods.Speaker: Lorenzo Borella (Universita e INFN, Padova (IT))
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Conveners: Enrique Rico Ortega (CERN), Dr Michele Grossi (CERN)
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Some recent progress in the description of atomic nuclei using quantum computers
Atomic nuclei are complex many-body systems with a number of constituents ranging from very few to several hundred [1-2]. Among the difficult aspects, nuclei are self-bound systems that require the treatment of a continuum of wave functions in the Hilbert space. The nuclear strong interaction is scarcely known and highly non-perturbative, with the onset of multi-body interaction beyond the usual interaction of particles two-by-two. Nuclear physics also belongs to problems that face the exponential growth of the Hilbert space when the number of constituents increases. For these reasons, the exact treatment of these systems on classical computers, starting from the interaction, is still restricted to a few percent of the nuclear chart.
Quantum technologies and associated quantum algorithms appear in this context as disruptive technologies that might surpass the current limitations in the coming years [3]. We have recently initiated a long-term project to explore using quantum computers and quantum information for nuclear physics and related many-body problems. Inspired by strategies used in classical computing, several novel approaches have been proposed to obtain the ground or low-lying states in many-body systems. A significant effort has been made to use the symmetry breaking/symmetry restoration method [2,4,5]. Based on the use of projectors through phase estimation, quantum oracles, or classical shadow, the Quantum Variation After Variation was formulated. Several methods were proposed to access excited states, including the Quantum Krylov, Quantum equation of motion, or Quantum Generator Coordinate Method [6,7,8]. After reviewing and illustrating these methods, the current status and future challenges in using quantum computers for atomic nuclei will be discussed.References:
[1] Quantum computing with and for many-body physics, Ayral, P. Besserve,
D. Lacroix and E. A. Ruiz Guzman, Eur. J. Phys. A. 59, 227 (2023), arXiv:2303.04850 [Review][2] Symmetry breaking/symmetry preserving circuits and symmetry restoration on quantum computers: A quantum many-body perspective, D. lacroix, E. A. Ruiz Guzman and P. Siwach, Eur. Phys. J. A 59, 3 (2023). arXiv:2208.11567 [Review]
[3] Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group, A. Di Meglio et al, PRX Quantum 5, 037001 (2024) , arxiv:2307.03236. [White paper]
[4] Restoring symmetries in quantum computing using Classical Shadows, Edgar Andres Ruiz Guzman, Denis Lacroix, Eur. J. Phys. A 60, 112 (2024). arXiv:2311.04571
[5] Restoring broken symmetries using quantum search oracles, E. A. Ruiz Guzman and D. Lacroix, Phys. Rev. C 107, 034310 (2023), arXiv:2210.11181
[6] Entanglement in selected Binary Tree States: Dicke/Total spin states, particle number projected BCS states, D. Lacroix, Phys. Rev. C 110, 034310 (2024), arxiv:2405.04665
[7] Neutron-proton pairing correlations described on quantum computers, Jing Zhang, Denis Lacroix and Yann Beaujeault-Taudière, submitted to Phys. Rev. C, arXiv:2408.17294
[8] Solving the Lipkin model using quantum computers with two qubits only with a hybrid quantum-classical technique based on the Generator Coordinate Method, Yann Beaujeault-Taudière and Denis Lacroix, Phys. Rev. C 109, 024327 (2024), arXiv:2312.04703
Speaker: Dr Denis Lacroix (IJCLab-Paris Saclay University) -
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Engineering periodic boundary conditions with circuit cutting for high-energy physics
Quantum computers process information with the laws of quantum mechanics. Current quantum hardware is noisy, can only store information for a short time and is limited to a few quantum bits, that is, qubits, typically arranged in a planar connectivity. However, many applications of quantum computing require more connectivity than the planar lattice offered by the hardware on more qubits than is available on a single quantum processing unit (QPU). Circuit cutting is a promising tool to engineer long-ranged interactions and break quantum circuits into smaller components. In this talk I will present recent advances in circuit cutting such as coupling quantum processors with a real-time classical link [1]. I will discuss how these advances relate to our vision of quantum centric supercomputing. Finally, I will show how to simulate lattice Gauge theories with periodic boundary conditions on superconducting qubit QPUs. Here, the periodic boundary conditions are created with circuit cutting to avoid costly swap gates.
[1] Vazquez et al., Combining quantum processors with real-time classical communication, Nature (2024).
Speaker: Daniel Egger -
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Efficient Encoding of Quantum States for Hamiltonian Simulation of (2+1)-dimensional U(1) Lattice Gauge Theory with Finite Temperature
In this work, we discuss an efficient way to discretize gauge fields in the (2+1)-dimensional U(1) lattice gauge theory with finite temperature for the Hamiltonian simulation using quantum computation. We extend a previous study based on Canonical Commutation Relation (CCR), which investigated the efficient discretization of low-lying states, to systems with finite temperature, where excited states play a crucial role. We specifically investigate two models: (1) (2+1)-dimensional pure U(1) lattice gauge theory with finite temperature, and (2) (2+1)-dimensional U(1) lattice gauge theory with finite temperature including dynamical fermions. Through this study, we find the effectiveness of CCR method in both systems at small coupling constants and low temperatures.
Speaker: Reita Maeno (University of Tokyo (JP)) -
46
Enhancing quantum field theory simulations on NISQ devices with Hamiltonian truncation
Quantum computers can efficiently simulate highly entangled quantum systems, offering a solution to challenges facing classical simulation of quantum field theories (QFTs). In this talk, we present an alternative to traditional methods for simulating the real-time evolution in QFTs by leveraging Hamiltonian truncation (HT). As a use case, we study the Schwinger model, systematically reducing the complexity of the Hamiltonian via HT while preserving essential physical properties. The HT approach converges quickly with the number of qubits, allowing for the vacuum persistence probability to be computed efficiently. Identifying the truncated free Hamiltonian’s eigenbasis with the quantum device’s computational basis avoids the need for complicated and costly state preparation routines, reducing the algorithm’s overall circuit depth and required coherence time. As a result, the HT approach to simulating QFTs on a quantum device is well suited to noisy-intermediate scale quantum devices, which have a limited number of qubits and short coherence times. We validate our approach by running simulations on a noisy-intermediate scale quantum device, showcasing strong agreement with theoretical predictions. We highlight the potential of HT for simulating QFTs on quantum hardware.
Speaker: JAMES,ALLAN INGOLDBY (IPPP, Durham University) -
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Projected Entangled Pair States for Lattice Gauge Theories with Dynamical Fermions
Lattice gauge theory is an important framework for studying gauge theories that arise in the Standard Model and condensed matter physics.
Yet many systems (or regimes of those systems) are difficult to study using conventional techniques, such as action-based Monte Carlo sampling. In this paper, we demonstrate the use of gauged Gaussian projected entangled pair states as an ansatz for a lattice gauge theory involving dynamical physical matter. We study a $\mathbb{Z}_2$ gauge theory on a two dimensional lattice with a single flavor of fermionic matter on each lattice site. Our results show agreement with results computed by exactly diagonalizing the Hamiltonian, and demonstrate that the approach is computationally feasible for larger system sizes where exact results are unavailable. This is a further step on the road to studying higher dimensions and other gauge groups with manageable computational costs while avoiding the sign problem.For further information, we refer to the extended abstract attached to this submission.
Speaker: Patrick Emonts -
48
Fault-tolerant simulation of Lattice Gauge Theories with gauge covariant codes
Quantum computers are a promising platforms to efficiently simulate systems hard to tackle on classical machines. An important challenge to overcome is the efficient control of errors that, if left undisturbed, make quantum simulations useless. A solution to this challenge is quantum error correction, that exploiting redundancy is able to correct errors. In this talk I will explore the connections between quantum error correction and lattice gauge theories and exploit them to propose a path forward for error corrected simulations of interest for high energy physics.
Speaker: Luca Spagnoli -
10:45
Coffee break
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Building quantum event generators through particle-based formulations
Quantum computers may revolutionize event generation for collider physics by allowing calculation of scattering amplitudes from full quantum simulation of field theories. Although rapid progress is being made in understanding how best to encode quantum fields onto the states of quantum registers, most formulations are lattice-based and would require an unrealistically large number of qubits when applied to scattering events at colliders with a wide momentum dynamic range. In this regard, particle-based formulations of field theory dynamics developed in works such as Barata et al. (Phys. Rev. A 103) and Gálvez-Viruet (arXiv:2406.03147) are highly attractive for their qubit efficiency and strong association with scattering. In fact, we believe that adopting some sort of sparse Fock representation is the only viable approach to realizing quantum event generators.
Since particle-based formulations are uncommon, basic properties such as their relation to analytic perturbation theory calculations are yet to be established. In this presentation, we compare physical observables computed numerically through a particle-based formulation to corresponding results of perturbation theory calculation using the 1+1d scalar field theory. We then discuss a possible roadmap to realizing quantum event generators, describing the known unknowns along the way.
Speaker: Yutaro Iiyama (University of Tokyo (JP)) -
50
Quantum Chebyshev Generative model for Fragmentation Functions
In this work, we study a Quantum Generative Model based on the Quantum Chebyshev Transform that enables to learn and sampling probability distributions. The model is applied to fragmentation functions, which quantify the probability that a given parton decays into a particular hadron after a hard scattering event. The results show that this model enables an efficient sampling, performing a natural quantum interpolation when the sampling is executed on an extended register, a task that might be challenging to perform classically. Furthermore, we investigate the model's performance when correlations between the momentum fraction $z$ and the energy scale $Q$ are introduced via entanglement in quantum circuits. This study provides valuable insights into the correlations of these two variables
Speaker: Jorge Juan Martinez De Lejarza Samper (Univ. of Valencia and CSIC (ES)) -
51
Efficient calculation of Green’s functions on quantum computers via simultaneous circuit perturbation
Understanding the dynamical properties of quantum many-body systems is a pivotal question in virtually all areas of modern physical sciences, including condensed matter, high-energy physics, and quantum chemistry. However, because of the often strongly correlated nature of the relevant models and the associated rapid growth of entanglement during time evolution, their study remains challenging at large scale. In the last decade, quantum computers have emerged as a promising computational paradigm to address this class of problems, and recent demonstrations of quantum simulations at the quantum utility scale have opened the way to the use of digital quantum information processing platforms as concrete, state-of-the-art research tools for fundamental physics.
In this work, we focus on the computation of the retarded Green’s function (RGF). These functions naturally arise in linear response theory (LRT), where they help derive essential properties like conductivity and magnetization, and are closely related to two-time dynamical correlations (DCs). In a $N$-site lattice spin model, ground state DCs are represented as $ \mathcal{C}(r, r^\prime, t, t^\prime)=\langle o_{r}(t) o_{r^\prime}(t^\prime)\rangle=\sum_p\langle 0|o_{r}(t)|p\rangle\langle p|o_{r^\prime}(t^\prime)|0\rangle e^{-i\mathcal{E}_pt}$. Here, $o_{r}$ is a local observable (e.g., a spin component represented by a Pauli matrix) on the lattice site at position $r$ and $|p\rangle $ are excited Hamiltonian eigenstates with energies $\mathcal{E}_p$.
A typical method for extracting DCs on a quantum computer leverages the so-called Hadamard test, an ancilla-based algorithm. However, its inherent non-local nature restricts its applicability on large-scale devices with limited qubit-qubit connectivity. Here, we propose a method to overcome these restraints by leveraging randomized quantum circuit perturbations within a linear response-inspired framework.
Speaker: Francesco Tacchino -
52
Learning to generate high-dimensional distributions with low-dimensional quantum Boltzmann machines
In recent years, researchers have been exploring ways to generalize Boltzmann machines (BMs) to quantum systems, leading to the development of variations such as fully-visible and restricted quantum Boltzmann machines (QBMs). Due to the non-commuting nature of their Hamiltonians, restricted QBMs face trainability issues, whereas fully-visible QBMs have emerged as a more tractable option, as recent results demonstrate their sample-efficient trainability. These results position fully-visible QBMs as a favorable choice, offering potential improvements over fully-visible BMs without suffering from the trainability issues associated with restricted QBMs. In this work, we show that low-dimensional, fully-visible QBMs can learn to generate distributions typically associated with higher-dimensional systems. We validate our findings through numerical experiments on both artificial datasets and real-world examples from the high energy physics problem of jet event generation. We find that non-commuting terms and Hamiltonian connectivity improve the learning capabilities of QBMs, providing flexible resources suitable for various hardware architectures. Furthermore, we provide strategies and future directions to maximize the learning capacity of fully-visible QBMs.
Speaker: Cenk Tüysüz (DESY) -
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Towards quantum advantage with photonic state injection
We propose a new scheme for near-term photonic quantum device that allows to increase the expressive power of the quantum models beyond what linear optics can do. This scheme relies upon state injection, a measurement-based technique that can produce states that are more controllable, and solve learning tasks that are not believed to be tackled classically. We explain how circuits made of linear optical architectures separated by state injections are keen for experimental implementation. In addition, we give theoretical results on the evolution of the purity of the resulting states, and we discuss how it impacts the distinguishability of the circuit outputs. Finally, we study a computational subroutines of learning algorithms named probability estimation, and we show the state injection scheme we propose may offer a potential quantum advantage in a regime that can be more easily achieved that state-of-the-art adaptive techniques. Our analysis offers new possibilities for near-term advantage that require to tackle fewer experimental difficulties.
Speaker: Léo Monbroussou (LIP6 (La Sorbonne Université), Naval Group)
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13:00
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Quantum Hackathon
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Hackathon Opening & Challenge Presentation 31/3-004 - IT AmphitheatreSpeaker: Dr Michele Grossi (CERN)
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Breakout Rooms for Team Discussions 513/R-070 - Openlab Space
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LUNCH CERN
CERN
lunch is not included but participants are invited to join the community at the Restaurant R2, next to the building.
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Discussion
Teams and participants can still stay at CERN to continue discussing and working on the challenge
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