All talks are usually scheduled at 2pm US Pacific and take place in 50A-5132 Sessler Conference Room.


Upcoming Seminars

Past Seminars – 2024

February 29: Zohreh Davoudi (University of Maryland)

Quantum simulating hadronic scattering: From confining spin models to gauge theories

An exciting promise of quantum simulators is to enable a first-principles look into the real-time dynamics of matter after high-energy collisions of hadrons and nuclei, which mimic conditions in the early universe. To realize such a promise, first the gauge theories of the Standard Model should be mapped to quantum simulators. Then complex initial states, in the form of moving wave packets of composite (bound) states of elementary constituents, need to be prepared. While much progress has happened in the former in recent years, developments in the latter are just starting to gain momentum. In this talk, I will provide three examples from our recent work to demonstrate concrete proposals and algorithms for hadronic wave-packet preparations in confining models, from Ising spin systems to the low-dimensional Abelian lattice gauge theories. These examples involve a range of platforms, from (solid-state and atomic) analog quantum simulators to digital quantum computers. I will further present results for numerical studies of expected scattering outcomes, and conditions for observing inelastic channels, along with a demonstration of a high-fidelity meson wave packet generated on a trapped-ion quantum computer.


Past Seminars – 2023

November 6: Torsten Zache (University of Innsbruck)

SNAQs - Spin-Network Algorithms for Q-deformed Gauge Theories

The real-time dynamics of gauge theories is one of the most promising applications for quantum devices where future quantum simulations are expected to provide a practical advantage over classical computers. However, it remains an outstanding challenge to reformulate non-abelian lattice gauge theories in a way that is tailored to quantum information processing. 

In this talk, I will present a new approach to this problem using a generalisation of the Kogut-Susskind Hamiltonian formulation, where the defining non-abelian Lie algebra is q-deformed to a quantum group. For the example of pure SU(2) lattice gauge theory in 2+1D, I will demonstrate that this formulation enables a controlled truncation on a finite dimensional Hilbert space that is naturally represented on a register of gauge-invariant spin-network states. Most importantly, the q-deformed Kogut-Susskind formulation preserves symmetry-related properties that allow us to construct efficient quantum circuits for Trotterized real-time evolution by analytically diagonalizing the plaquette operators using local changes of the spin-network basis. Additionally, our approach aligns well with tensor network methods and we numerically find that a simple variational ansatz already captures salient features of the continuum theory. Our work thus foreshadows a new class of efficient quantum and classical algorithms to simulate non-abelian lattice gauge theories.

Based on:

Zache, González-Cuadra & Zoller, arXiv:2304.02527 (Phys. Rev. Lett. in press)



June 8: William Kirby (IBM)

Quantum Krylov algorithms for ground state energy approximation

Quantum Krylov algorithms are a family of quantum algorithms for approximating ground state energies of quantum systems. In this talk, we will review the main ideas in common to these algorithms, then discuss implementation and error analysis for two specific cases: Krylov spaces constructed from real time-evolutions and from block-encodings.


April 6: Shivesh Pathak (Sandia National Labs)

Quantifying improvements due to state preparation in ground energy estimation on a quantum computer

Ground state energy estimation is ubiquitous in computational physics, and is a limiting factor in the solution of numerous problems in chemical/materials science, quantum field theory, and model Hamiltonians. In this talk, I will present recent work quantifying the benefits of ground state preparation using an algorithm proposed by Lin and Tong involving amplitude amplification and quantum signal processing. I will cover a theoretical analysis of Lin and Tong's near-optimal state preparation algorithm, showing that it can reduce T-gate counts for ground state energy phase estimation near quadratically. I will also present concrete resource estimates – T-gate and qubit counts – for ground state preparation and ground state energy estimation for interesting materials, like solid state batteries. I will conclude with a discussion on how these concrete resource estimates help inform us on the size of a quantum computer necessary for relevant quantum advantage. Time permitting, I will also give a brief overview of other projects I am working on including (far term) fault tolerant quantum computing for quantum field theory applications, and (moderate term) fault tolerant quantum computing for model Hamiltonians and embedded systems.


March 2: Indrakshi Raychowdhury (BITS Pilani)

Towards quantum simulating QCD: the loop-string-hadron framework

Towards the goal of quantum simulating a theory, choosing a convenient framework itself is an important design decision. This becomes even more crucial when the goal is to quantum simulate the strong interaction of nature, which is described by the theory of quantum chromodynamics. We present a loop-string-hadron (LSH) framework for gauge theories with continuous and non-Abelian gauge symmetries such as SU(2) and SU(3). The LSH approach uses gauge invariant degrees of freedom such as loop segments, string ends, and on-site hadrons, it is free of all non-Abelian gauge redundancy, and it is described by a Hamiltonian containing only local interactions. We discuss how this alternate framework can make a paradigm shift and lead to the goal of quantum simulating QCD a reality soon.


Feb 2: Torin Stetina (Simons Institute, UC Berkeley)

Generating Approximate Ground States of Molecules Using Quantum Machine Learning 

The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical nuclear coordinates. Our approach uses a classical neural network to convert the nuclear coordinates of a molecule into quantum parameters of a variational quantum circuit. The model is trained using a fidelity loss function to optimize the neural network parameters. We show that gradient evaluation is efficient and numerically demonstrate our method’s ability to prepare wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In all cases, we find that a small number of training points are needed to achieve very high overlap with the groundstates in practice. From a theoretical perspective, we further prove limitations on these protocols by showing that if we were able to learn across an avoided crossing using a small number of samples, then we would be able to violate Grover’s lower bound. Additionally, we prove lower bounds on the amount of quantum data needed to learn a locally optimal neural network function using arguments from quantum Fisher information. This work further identifies that quantum chemistry can be an important use case for quantum machine learning. 


Past Seminars – 2022

Oct 20: Alessandro Roggero (University of Trento)

Quantum Simulation of Collective Neutrino Oscillations

In extreme astrophysical environments like supernova explosions, the large neutrino density can lead to collective flavor oscillations driven by neutrino-neutrino interactions. These phenomena are important to describe flavor transport and have potentially important consequences for both the explosion mechanism and nucleosynthesis in the ejected material. Even simple models of neutrino-neutrino interactions require the solution of a challenging many-body problem whose exact solution requires exponential resources in general. In this talk I will describe the physics of collective flavor oscillations and present the recent efforts to simulate the real-time flavor dynamics of two-flavor neutrinos using current generation quantum computers based on both superconducting qubits as well as trapped ions.


Sep 29: Niladri Gomes (LBNL)

Adaptive Variational Approach for Quantum Simulations

We propose a general-purpose, self-adaptive approach to construct variational wavefunction ansatze for highly accurate quantum dynamics and imaginary time simulations based on McLachlan's variational principle. The key idea is to dynamically expand the variational ansatz along the time-evolution path such that the “McLachlan distance”, which is a measure of the simulation accuracy, remains below a set threshold. We apply this adaptive variational quantum dynamics simulation (AVQDS) approach to the nonintegrable mixed-field Ising model, where it captures both finite-rate and sudden post-quench dynamics with high fidelity. The AVQDS quantum circuits that prepare the time-evolved state are much shallower than those obtained from first-order Trotterization and contain up to two orders of magnitude fewer CNOT gate operations. Finally, we also deploy the adaptive variational imaginary time evolution (AVQITE) to prepare ground states of , and molecules, where it yields compact variational ansatze and ground state energies within chemical accuracy. We envision that a wide range of dynamical and ground state simulations of quantum many-body systems on near-term quantum computing devices will be made possible through the adaptive framework.

Aug 18: Ed Younis (LBNL)

Generating Resource Efficient Programs Using BQSKit

Current and near-future quantum hardware is likely resource-constrained: programs have to use few qubits

and carefully limit their gate count. In this presentation, we will introduce and demonstrate the

Berkeley Quantum Synthesis Toolkit (BQSKit) framework to assist in the algorithm development workflow.

Synthesis is a powerful circuit compilation technique whose dynamics and trade-offs are different from

traditional quantum optimizing compilers such as Qiskit, Tket, etc. Currently, BQSKit can produce circuits

of better quality than most existing tools. Furthermore, since our tools are open source, we can take a

deep dive into their nuts and bolts, showing how they can be reconfigured, customized, and extended to

arrive at better problem specifications and improved workflow construction. Understanding their design principles,

necessary trade-offs, and effect on overall performance throughout the workflow will equip attendees with the

knowledge and ability to better implement algorithms on NISQ devices using our tools.


July 28: Jesse Stryker (University of Maryland)

Circuitizing product formulas for lattice gauge theories in electric eigenbases

Lattice gauge theory Hamiltonians exhibit a number of general features that must be thoroughly understood before their digital quantum simulation becomes realistic and scalable. Electric eigenbases have received the most attention because they are easier to understand in terms of mapping to qubits, Hilbert space truncation, and Gauss's law. A model that is sufficiently rich to illustrate many of the issues to be confronted is the SU(2) analogue of the Schwinger model, but algorithms for its quantum simulation remain in their infancy. We develop start-to-finish, product-formula-based algorithms for digital time evolution in this model, using the Schwinger boson and loop-string-hadron formulations in their electric eigenbases. We share the insights we have gained from this model into how simulation cost estimates can be reduced (while retaining gauge degrees of freedom) and how cost differences may arise between formulations. Emphasis is placed on the art of distilling individually-circuitizable terms within the Hamiltonian.

May 26: Minh Tran (MIT)

Faster Digital Quantum Simulation by Symmetry Protection

Simulating the dynamics of quantum systems is an important application of quantum computers and has seen a variety of implementations on current hardware. We show that by introducing quantum gates implementing unitary transformations generated by the symmetries of the system, one can induce destructive interference between the errors from different steps of the simulation, effectively giving faster quantum simulation by symmetry protection. We derive rigorous bounds on the error of a symmetry-protected simulation algorithm and identify conditions for optimal symmetry protection. In particular, when the symmetry transformations are chosen as powers of a unitary, the error of the algorithm is approximately projected to the so-called quantum Zeno subspaces. We demonstrate the symmetry-protection in the simulations of several systems, including the lattice Schwinger model, showing the capability to reduce the simulation error by several orders of magnitude over the unprotected simulations.


April 28: Henry Lamm (Fermilab)

Tell Me Why: How Quantum Computing Saves Lattice Field Theory

While lattice field theory has been successful beyond our wildest dreams, there remain problems that are intractable to its application on classical computers due to sign problems. Quatum computers present an elegant solution to this issue, opening new fields to nonpertubative study: neutron star equations of state, viscosity of the quark-gluon plasma, and particle collisions.  In this talk, I will discuss the basics of lattice field theory,  the obstacles for it on classical computers, and how quantum computers can be used to solve interesting physics problems in particle physics.


March 31: Hsin-Yuan Huang (Caltech)

Predicting many properties of a quantum system from very few measurements

Predicting the properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for constructing an approximate classical description of a quantum state using very few measurements of the state. This description, called a classical shadow, can be used to predict many different properties: order log M measurements suffice to accurately predict M different functions of the state with high success probability. The number of measurements is independent of the system size and saturates information-theoretic lower bounds. Moreover, target properties to predict can be selected after the measurements are completed. Recently, many numerical and physical experiments have validated the effectiveness of classical shadows in a wide range of problems, such as predicting expectation values of local observables, classifying quantum phases of matter, verifying entanglement in mixed states, and training machine learning models to predict ground states for unexplored physical systems.


February 24: Natalie Klco (Caltech)

Calculating Nature Naturally:  Quantum Simulation of Quantum Fields

In this journal club we will discuss perspectives and progress in quantum simulation---the precision control of atomic scale quantum systems to efficiently explore the properties and dynamics of subatomic quantum systems---both for applications in fundamental physics and in large-scale quantum computations more broadly.  For a concrete example, we will examine the spacelike distribution of entanglement in simple fields and discuss its implications for quantum simulation design.

January 13: Balint Koczor (University of Oxford)

Exponential Error Suppression for Near-Term Quantum Devices

Suppressing noise in physical systems is of fundamental importance. As quantum computers mature, quantum error correcting codes (QECs) will be adopted in order to suppress errors to any desired level. I will first discuss that in the noisy, intermediate-scale quantum (NISQ) era, the complexity and scale required to adopt even the smallest QEC is prohibitive: a single logical qubit needs to be encoded into many thousands of physical qubits. I will then discuss alternative means for achieving practical value of early quantum computers via quantum error mitigation techniques: these can reduce the effect of noise for the crucial case of estimating expectation values of observables (key to almost all NISQ algorithms). I will finally present the recently introduced Error Suppression by Derangements (Virtual Distillation) technique which can indeed achieve an effective exponential suppression by taking n independently prepared circuit outputs to create a state whose symmetries prevent errors from contributing bias to the expected value. I will also show how the approach is very well suited for current and near-term quantum devices as it is modular in the main computation and requires only a shallow circuit that bridges the n copies immediately prior to measurement. The talk will be based on [B. Koczor, Phys. Rev. X 11, 031057].



Past Seminars – 2021

December 9: Felix Ringer (Stony Brook University)

Quantum simulation of open quantum systems in high energy nuclear physics

Quantum computing has emerged in recent years as a promising approach to solve a variety of classically intractable problems, due to considerable progress in hardware and algorithms. In high-energy nuclear physics, it may eventually allow for simulations of real-time dynamics of field theories and first-principles computations of scattering cross sections. In this talk, I will focus on a lower-dimensional field theory, the Schwinger model. It exhibits several features which are also present in quantum chromodynamics (QCD) such as confinement. In particular, I will discuss the real-time dynamics of the string breaking mechanism, non-equilibrium dynamics, and the preparation of thermal states which are relevant for proton proton and heavy-ion collisions at the LHC. I will present numerical results using both simulators and quantum devices from IBM.


November 4: 

Christian Bauer (LBNL)

Overview of quantum computing for high energy physics

Bert de Jong (LBNL)

Overview of near-term strategies for circuit implementation on quantum computers